EP4649568A1

EP4649568A1 - Gemini grid-connectable renewable powerplant delivering high capacity factor to controllable loads

Info

Publication number: EP4649568A1
Application number: EP25717834.3A
Authority: EP
Inventors: Thomas Buttgenbach
Original assignee: 1st Avenue Nova LLC
Current assignee: 1st Avenue Nova LLC
Priority date: 2024-03-21
Filing date: 2025-03-21
Publication date: 2025-11-19
Also published as: AU2025208504B2; AU2025248678A1; AU2025208504A1; CN121039918A

Abstract

A renewable power system with a twin-configurable architecture is described. The system includes a renewable energy source (RES), an energy storage system (ESS), and at least one controllable load (CL) (e.g., AI training/datacenter). The system can serve as a baseload or semi-baseload plant for CL(s) and/or as a peaker or semi-peaker plant for an electric grid, or vice-versa, and optionally in parallel, can also provide ancillary services to the electric grid and/or to the CL(s). In certain embodiments, e.g. solar photovoltaic (PV) RES(es), the system can have capacity factors of at least about 60% and up to 100%, higher asset utilization, better economics for the RES-ESS, improved system performance, and lower energy costs as compared with known systems without a CL(s). By making load a variable, and integral part of the system, sophisticated resource allocation strategies, including AI algorithms, can be developed not previously possible with known systems lacking a CL(s).

Description

GEMINI GRID-CONNECTABLE RENEWABLE POWERPLANT DELIVERING HIGH CAPACITY FACTOR TO CONTROLLABLE LOADS CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims priority to U.S. Patent Application No.19/069,820, filed March 4, 2025 and titled “Twin-Configurable Architecture Renewable Power Plant for High Capacity Factor Servicing of Controllable Loads,” which is a Continuation of U.S. Patent Application No. 18/792,847, filed August 2, 2024 and titled “Twin-Configurable Architecture Renewable Power Plant for High Capacity Factor Servicing of Controllable Loads,” now U.S. Patent No.12,244,147, which in turn claims priority to and the benefit of U.S. Provisional Patent Application No. 63/645,837, filed May 10, 2024 and titled “Twin Mode Renewable Electric Generation Resources and Energy Storage Systems Serving High Capacity Factor Controllable Loads,” and this application also claims priority to U.S. Provisional Patent Application No.63/568,397, filed March 21, 2024 and titled “Networked Energy Generation, Storage, and Distribution,” U.S. Provisional Patent Application No. 63/574,733, filed April 4, 2024 and titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads,” U.S. Provisional Patent Application No. 63/639,474, filed April 26, 2024 and titled “System and Methods for Smart Renewable Powerplant Serving Multiple Controllable and Uncontrollable Loads,” and U.S. Provisional Patent Application No. 63/642,490, filed May 3, 2024 and titled “Systems and Methods Utilizing AC Overbuilt Renewable Electric Generation Resource with Storage Device and Controllable Loads”; the entireties of each of the foregoing patent applications are hereby incorporated by reference herein in their entireties for all purposes. FIELD [0002] This disclosure relates generally to energy generation, storage, and distribution. This disclosure relates to, for example, a configurable renewable powerplant providing efficient generation and asset utilization for behind-the-meter and in-front-of-the-meter loads benefitting from high-capacity factors or peaking power with constraint grid connections. This disclosure also relates to, for example, a renewable powerplant servicing multiple loads, and to a renewable powerplant being overbuilt for its grid connection. BACKGROUND [0003] The global shift towards renewable energy sources has catalyzed innovative developments in energy generation, storage, and distribution, aimed at reducing greenhouse gas emissions and fostering sustainable energy practices. Known energy grids rely on fossil fuel generation, presenting challenges in terms of environmental impact, slow response times, and resource depletion. In contrast, renewable energy technologies, such as solar, wind, and hydroelectric power, offer abundant and environmentally friendly alternatives. The intermittent nature of many renewable energy resources, however, typically necessitates efficient storage and distribution systems to address fluctuations in supply and demand. Consequently, a critical need exists for advancements in renewable energy storage and electric grid and load management to optimize the integration of renewable energy into existing power infrastructures. Additionally, after decades of near flat electricity demand growth in developed countries, a huge rise in demand is occurring for clean and affordable electricity from 2 sectors: (i) transitioning from fossil fuels to clean energy sources powering much of the electric gid, real estate, transportation, and commercial/industrial processes, and (ii) the digital transformation of our economy, e.g. datacenters and now AI training. This growth puts additional strain on existing electric grid infrastructure, creating a need for advancements and new approaches by integrating these new loads into more flexible power plant system architecture operated by smart/AI controllers taking a holistic view of the entire energy system. [0004] Energy generated by a renewable energy source (RES) may vary seasonally. In some cases, energy generated by a solar RES can be higher during the summer months when periods of daylight are longer and when peak daytime power is higher; and lower during the winter months when periods of daylight are shorter and when peak daytime power is lower. Similarly, wind power is generally stronger in the winter months than in the summer months. [0005] Electrical energy demand or electrical power demand from a grid may also exhibit variability. For example, in hot desert regions like the US Southwest, electrical energy demand from the grid can be highest during the summer months, because of air conditioning loads. Conversely, in other regions electrical energy demand from a grid can be highest in winter due to heating loads. Dependent on the geographical location, the climate, industries, and/or culture where the grid is operable, the energy demand or power demand from the grid may exhibit a variety of patterns. [0006] In some cases, variability in energy generation by a RES and variability in energy demanded from a grid may be mismatched. SUMMARY [0007] The following is a non-exhaustive listing of some aspects of the present techniques. These and other aspects are described in the following disclosure. [0008] In some embodiments, a system includes at least one renewable energy source (RES), at least one energy storage system (ESS) (also referred to herein as an electrical storage system), and a controller. The at least one RES is configured to electrically couple to a grid interconnection point of an electric grid. An aggregated alternating current (AC) power output capacity of the at least one RES exceeds (e.g., by a factor of at least about 1.3 times, or by a factor of between about 3 and about 6) a point of grid interconnect (POGI) limit of the grid interconnection point (also referred to herein as the “POGI limit” or “POGI capacity”). In some instances, an electric grid interconnection at the POGI can specify a different capacity / value for the POGI capacity depending on whether the system is a net load to the electric grid or a net generation resource for the electric grid. Thus, where applicable, the POGI capacity numbers discussed herein and used for calculations herein shall be understood to refer to the capacity of the POGI in the corresponding load or generation scenario. The at least one ESS is electrically coupled to the grid interconnection point and the at least one RES. The at least one ESS has an aggregated power capacity that is less than or equal to the aggregated power output capacity (e.g., AC power output capacity) of the at least one RES. The controller is communicatively coupled with at least one controllable load, the at least one ESS, and the at least one RES. The at least one controllable load(s) can be positioned / located in-front-of-the meter (e.g., energy-related activities occurring on a utility company/entity side of the electric grid) and/or behind-the-meter (e.g., energy-related activities occurring on the customer side of the electric grid, optionally on the customer’s premises / on-site, and/or energy- related activities occurring on the electric grid but involving one or more independent power producers (IPPs), utilities, customer specific tariff(s), and/or energy service providers (ESPs)), as further discussed herein. In-front-of-the-meter operations can include, but are not limited to, direct access, pseudo-ties (e.g., involving one or more balancing authorities), and/or special tariffs. As used herein, “direct access" can refer, by way of non-limiting example, to an electric service option (e.g., a retail electric service option) in which customers can purchase electricity from a competitive non-utility entity such as an ESP (or a utility with a customer or customer group specific tariff), optionally within a service territory of a utility that itself is still responsible for transmission and distribution for the direct access customers. It is noted that different utility service territories can use differing nomenclatures to refer to “direct access,” but nevertheless can have the shared ability to provide to customers the option of purchasing electric service(s) directly from an energy provider(s). [0009] The controller is configured to control a net load profile of the at least one CL such that the net load profile of the at least one CL includes at least one value between a maximum net load value and a minimum net load value of the at least one CL. As used herein, a “net load profile” for a CL(s) can refer to the total / gross load of the CL(s) minus the RES generation allocated to the CL(s), and a “net load profile” for the electric grid can refer to the total / gross load minus renewable energy generation allocated to the electric grid. Controlling a net load profile of the at least one CL can include controlling one or more subsystems of the at least one CL, for example to perform “pre-cooling” of a data center. The controller is also configured to provide a first instruction to at least one of the at least one RES or the at least one ESS to provide a first portion of electric power generated by the at least one RES or stored by the at least one ESS to at least one controllable load up to an aggregated power demand. The controller is also configured to provide a second instruction to at least one of the at least one RES or the at least one ESS to provide power to the electric grid in response to (A) electric power generated by the at least one RES exceeding an aggregated power capacity of the ESS and the aggregated power demand, or (B) the controller, using a predictive algorithm and power data, determining that a grid condition exists in a power system forecast. The controller is also configured, in response to determining that the grid condition exists without the electric power generated by the at least one RES exceeding the aggregated power capacity of the ESS and the aggregated power demand, to provide a third instruction to the at least one controllable load to decrease or increase a power demand at the at least one controllable load. Alternatively or in addition, the controller can be configured to provide a fourth instruction to the at least one controllable load to increase a power demand at the at least one controllable load in response to detecting / determining that the ESS has reached a storage limit, and/or in response to a prediction that the ESS will reach a storage limit at a future time (e.g., at a time when the ESS is predicted to next reach a storage limit without providing energy to the at least one controllable load), and/or in response to determining that it is operationally or economically more desirable to do so, such that excess energy can be used by the at least one controllable load (e.g., to perform pre-cooling for a data center). [0010] In some embodiments, a method of providing power on an RES-ESS-CL system includes providing, at a first time and by at least one of a renewable energy source (RES) or an energy storage system (ESS), power to a point of grid interconnect (POGI) associated with an electric grid, the POGI disposed between at least one controllable load and the electric grid. The method also includes providing, at a second time and by the at least one of the RES or the ESS, power to the at least one controllable load. The method also includes providing, at a third time, power received from the electric grid at the POGI to the ESS. The method also includes providing, at a fourth time, power from received from the electric grid at the POGI to the at least one controllable load. The method also includes providing, at a fifth time, no power via the POGI and providing at least one of power from the ESS to the at least one controllable load, power from the RES to the at least one controllable load, or power from the RES to the ESS. [0011] In some embodiments, a non-transitory, processor-readable medium stores instructions that, when executed by a processor, cause the processor to cause at least one of a renewable energy source (RES) or an energy storage system (ESS) to supply electric power to a controllable load without using an electric grid. The processor-readable medium also stores instructions that, when executed by a processor, cause the processor to cause at least one of the RES or the ESS to supply electric power to the electric grid in response to determining that (A) electric power generated by the at least one RES exceeds a storage capacity associated with the ESS and a power demand associated with the controllable load, or (B) a grid condition associated with the electric grid exists. The processor-readable medium also stores instructions that, when executed by a processor, cause the processor to cause the controllable load to decrease or increase a power demand associated with the controllable load when the grid condition exists without the electric power generated by the at least one RES exceeding the local storage capacity and the local power demand. [0012] Some embodiments of the present disclosure include a system comprising a grid interconnection point that is on an electric grid and that has a point of grid interconnect (POGI) limit; at least one renewable energy source (RES) that is electrically coupled to the grid interconnection point, wherein an aggregated AC power output capacity of the at least one RES significantly (e.g., by a factor of at least about 1.3 times) exceeds the POGI limit; at least one ESS that is electrically coupled to the grid interconnection point and the at least one RES, wherein the at least one ESS has an aggregated power capacity that is less than the aggregated power output capacity; at least one controllable load that is electrically coupled to at least one of the at least one RES or the at least one ESS, wherein the at least one controllable load has an aggregated power demand that is less than the aggregated power output capacity; and a controller that is communicatively coupled with the at least one controllable load, the at least one ESS, and the at least one RES, wherein the controller is configured to: provide first instructions to at least one of the at least one RES or the at least one ESS to provide a first portion of the electric power generated by the at least one RES or stored by the at least one ESS to the at least one controllable load up to the aggregated power demand; provide second instruction to at least one of the at least one RES or the at least one ESS to provide power to the electric grid via the grid interconnection point only if (1) the electric power generated by the at least one RES exceeds the aggregated power capacity and the aggregated power demand or (2) the controller, using a predictive algorithm and power data obtained from the power data sources, determines that a grid condition exists in a power system forecast; and if the grid condition exists without the electric power generated by the at least one RES exceeding the aggregated power capacity and the aggregated power demand, providing third instructions to the at least one controllable load to decrease or increase power demand at the at least one controllable load. Alternatively or in addition, the controller can be configured to provide fourth instructions to the at least one controllable load to increase a power demand at the at least one controllable load in response to detecting / determining that the ESS has reached a storage limit and/or in response to a prediction that the ESS will reach a storage limit at a future time (e.g., at a time when the ESS is predicted to next reach a storage limit without providing energy to the at least one controllable load), or it is operationally or economically more desirable, such that excess energy can be utilized by the at least one controllable load. [0013] Some embodiments of the present disclosure include tangible, non-transitory, machine-readable media storing instructions that when executed by a data processing apparatus cause the data processing apparatus to perform operations including the above-mentioned process(es). [0014] Some embodiments of the present disclosure include a system having one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the one or more processors to effectuate operations of the above-mentioned process(es). BRIEF DESCRIPTION OF THE DRAWINGS [0015] FIG. 1 is a schematic view illustrating an embodiment of a twin-mode power generation, storage, and distribution system, in accordance with some embodiments of the present disclosure. [0016] FIG.2 is a schematic view illustrating an embodiment of a renewable energy source- energy storage system-controllable load RES-ESS-CL controller used in an RES-ESS-CL system of the twin-mode power generation, storage, and distribution system FIG. 1, in accordance with some embodiments of the present disclosure. [0017] FIG. 3 illustrates a flowchart of the twin-mode power generation, storage, and distribution system of FIG. 1 serving as a baseload for one or more controllable loads and as a peaker plant for a grid, in accordance with some embodiments of the present disclosure. [0018] FIG.4 shows an example of a computing device by which the present techniques may be implemented, in accordance with some embodiments of the present disclosure. [0019] FIG. 5 is a flow diagram showing a first method for controlling a power generation, storage, and distribution system, in accordance with some embodiments. [0020] FIG.6 is a flow diagram showing a second method for controlling a power generation, storage, and distribution system, in accordance with some embodiments. [0021] FIG.7 is a flow diagram showing a third method for controlling a power generation, storage, and distribution system, in accordance with some embodiments. [0022] FIG.8 is a flow diagram showing a fourth method for controlling a power generation, storage, and distribution system, in accordance with some embodiments. [0023] FIG. 9A is a first example set of plots comparing net load profiles for a controllable load and an electric grid, in accordance with some embodiments. [0024] FIG.9B is a second example set of plots comparing net load profiles for a controllable load and an electric grid, in accordance with some embodiments. [0025] FIG.9C is a third example set of plots comparing net load profiles for a controllable load and an electric grid, in accordance with some embodiments.. [0026] FIG. 10 is a schematic view illustrating an embodiment of a networked energy generation, storage, and controllable load system, in accordance with some embodiments of the present disclosure; [0027] FIG. 11 is a schematic view illustrating an embodiment of a networked energy generation, storage, and distribution controller used in the networked energy generation and controllable load system of FIG. 10, in accordance with some embodiments of the present disclosure; [0028] FIG. 12 is a flowchart illustrating an embodiment of a method of networked energy generation, energy storage, and energy distribution to controllable loads, in accordance with some embodiments of the present disclosure; and [0029] FIG. 13 is a block diagram of an example renewable energy power plant (REPP), according to one or more embodiments, in accordance with some embodiments of the present disclosure; [0030] FIG.14 is a block diagram of the REPP of FIG.13, with a switch connecting a second energy storage system (ESS) with a controllable load, in accordance with some embodiments of the present disclosure; [0031] FIG.15 is a block diagram of the REPP of FIG.13, with a switch connecting a second ESS with a second meter, in accordance with some embodiments of the present disclosure; [0032] FIG.16 is a block diagram of the REPP of FIG.13, with a switch connecting a second ESS with a first meter, in accordance with some embodiments of the present disclosure; [0033] FIG.17 is a block diagram of the REPP of FIG.13, with a second switch connecting a first ESS with a second meter, in accordance with some embodiments of the present disclosure; [0034] FIG. 18 is a schematic view illustrating an embodiment of an energy management system used in the REPP of FIG. 13, in accordance with some embodiments of the present disclosure; [0035] FIG.19 illustrates a flowchart of renewable powerplant serving multiple controllable and uncontrollable loads, in accordance with some embodiments of the present disclosure; and [0036] FIG. 20 is a schematic view illustrating an embodiment of an RES-ESS system, in accordance with some embodiments of the present disclosure; [0037] FIG.21 is a schematic view illustrating an embodiment of RES-ESS controller used in the RES-ESS system of FIG.20, in accordance with some embodiments of the present disclosure; [0038] FIG.22 illustrates a flowchart of the RES-ESS system serving one or more controllable in accordance with some embodiments of the present disclosure; and [0039] While the present techniques are susceptible to various modifications and alternative forms, specific embodiments thereof are shown by way of example in the drawings and will herein be described in detail. The drawings may not be to scale. It should be understood, however, that the drawings and detailed description thereto are not intended to limit the present techniques to the particular form disclosed, but to the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present techniques as defined by the appended claims. DETAILED DESCRIPTION Twin-Configurable Architecture Renewable Power Plant for High Capacity Factor Servicing of Controllable Loads [0040] To mitigate the problems described herein, the inventors had to both invent solutions and, in some cases just as importantly, recognize problems overlooked (or not yet foreseen) by others in the field of energy generation, storage, and distribution and renewable energy power plants, grid connected loads, both controllable and non-controllable and/or correlated, partially correlated, and uncorrelated, as well as behind-the-meter controllable and/or non-controllable loads and in-front-of-the-meter controllable and/or non-controllable loads. Indeed, the inventors wish to emphasize the difficulty of recognizing those problems that are nascent and will become much more apparent in the future should trends in industry continue as the inventors expect such as, for example, massive load growth from industries such as data centers, artificial intelligence (AI) training, vertical farming, carbon capture, electric vehicle charging, smelters, water treatment plant (including desalination and purification), industrial or real estate process energy transitioning to electricity, hydrogen production, cryptocurrency mining, and the like. Further, because multiple problems are addressed, it should be understood that some embodiments are problem-specific, and not all embodiments address every problem with known systems described herein or provide every benefit described herein. That said, improvements that solve various permutations of these problems are described below. [0041] In recent years, new loads have been introduced to the power grid. These loads often have large power requirements. For example, loads such as a vertical farming, data centers, AI training, cryptocurrency mining, smelters, water treatment plant (including desalination and purification), electric vehicle charging, industrial or real estate process energy transitioning to electricity, hydrogen production, or other loads may include power intensive processes. Also, these loads may be uncorrelated with the known grid where the power profile or net load profile is different than the typical power consumption of the grid that often follows HVAC schedules in hot areas, heating in colder climates, established industrial process in the area, time of day when business and residential zones are typically requiring power, or the like. These new loads often desire clean renewable energy as well as inexpensive energy as energy is often a large portion of their operating expenses. [0042] However, renewable energy generation sources, notably solar photovoltaic (PV) and wind power generators, have variability, influenced by natural and meteorological conditions. The variability poses challenges to grid stability, including frequency and voltage deviations. As renewable electric generation resources begin to supply a larger portion of the electrical grid and replace known base-load units such as coal-fired and nuclear-powered plants, a host of technical challenges arise. These include grid interconnection, power quality, reliability, stability, protection, and generation dispatch and control. The intermittent nature of solar and wind generation, coupled with rapid fluctuations in output, has resulted in the integration of energy storage systems (ESS), with energy storage devices such as battery energy storage systems (BESS). This integration aims to enhance grid compatibility by smoothing fluctuations and improving the predictability of energy supply from renewable energy sources as known renewable energy resources typically exhibit low-capacity factors, typically ranging from 15% to 40% depending on the resource, the location and weather patterns. [0043] Typically, these RES-ESS systems can be linked with transmission resources of an electrical grid at a point of grid interconnection (POGI) that typically operates at a voltage that is optimal for transmitting electric power over long distances with minimal transmission losses. To uphold reliability and safeguard transmission resources, a POGI limit is established for each electrical energy generation resource, delineating the maximum power that can be supplied to a transmission resource. [0044] To enhance the revenue potential from a photovoltaic energy generation resource in tandem with associated transmission resources of predetermined cost, oversizing the aggregate output of a photovoltaic array or other renewable energy source (RES) relative to the POGI limit has recently been introduced. This strategic move is motivated by the sporadic occurrence of peak photovoltaic generation, attributable to various factors such as adverse weather conditions, solar conditions, panel cleanliness, PV panel aging, and elevated ambient air temperatures diminishing PV panel output. The ESS may be used in conjunction with the oversized RES to help absorb excess power production during peak energy generation times that exceeds the POGI limit and provide power to the grid during times when the RES is not generating power or generating less power than the POGI limit. It should be noted that while the ESS may be charged during peak energy generation times, the ESS may be charged when the power distribution to the grid is below the POGI limit to provide a fuller ESS capacity in some circumstances, or the ESS may be charged by the grid via the POGI. While oversizing the photovoltaic array increases power sales over the year and the ESSs absorb excess power generation during peak RES generation times, these systems still typically require the need to curtail excess power during peak irradiance/wind periods and when the ESS is full, often accomplished through inverter clipping required or specified by regulations to shield the grid from potential failures induced by circuit overloads, transmission line overloads, transformer strains, or instances necessitating circuit breakers to disconnect an over- generating facility. This curtailment of power is often undesirable. Furthermore, while the new grid loads, discussed above, are seeing more renewable power on the grid and may contract with renewable energy sources to deliver power on the grid, cost and reliable renewable power is still an issue as the systems still require or involve grid transmission fees and some reliance on non- renewable energy sources. [0045] Another issue with current power plants is that the grid often relies on peaking power plants – also known as peaker plants - when power demand is high. Peaking power plants may often include low use, high-emitting power plants that grid operators call on at times of high demand. Gas turbines or diesel generators are common peaker plants because of their ability to start and ramp up at times of high demand. Because of their high cost to operate and maintain and their use of fossil fuels, these power plants are often undesirable but a necessity for the grid to provide additional generation to meet any potential power shortfalls. It is estimated that peaker plants make up 10% of the grid infrastructure to supply energy during dangerous peak demand, which occurs only 1% of the time. [0046] In light of these challenges, there is a pressing need for advancements in renewable electrical energy generation resources, energy storage, and distribution facilities. Additionally, there is a demand for sophisticated control methods to manage these facilities effectively. Furthermore, there is a necessity for streamlined processes to facilitate power delivery transactions for the outputs generated by such facilities. Further still, there is a need to provide reliable, inexpensive, renewable energy to certain industries as well as quickly provide power to the grid at peak times without the use of fossil fuels and without the peaker plant infrastructure required (or provided) for grid stability. [0047] Systems and methods of the present disclosure provide a twin-mode power generation, storage, and distribution system for providing a high-capacity factor baseload for a controllable load and peaking power for a gird connection. The twin-mode power generation, storage, and distribution system may include a networked renewable energy source (“RES”) (e.g., solar, wind, etc.), energy storage system (“ESS’), and controllable load (“CL”) facility or plant, where the combination may be referred to here as RES-ESS-CL or a RES-ESS-CL facility (of which a photovoltaic plus storage or “PV+S” facility is a subset). In various embodiments, the RES-ESS may be coupled directly with one or more controllable loads. As such, the one or more controllable loads may be defined as being behind-the-meter. The controllable load may be correlated or uncorrelated with a load on the grid. In some embodiments, the controllable load(s) may be on the grid or may be both on the grid and off the grid (e.g., one or more controllable loads may be behind-the-meter and one or more controllable loads may be in-front-of-the-meter). In various embodiments, the networked RES-ESS-CL system defaults as a baseload for supplying power to the controllable load for which the RES and ESS is built. As used herein, the phrase “capacity factor” refers to a ratio of electrical energy produced by an electricity generating system (e.g., a system including one or more RESes and/or one or more ESSes) to a load (e.g., of one or more controllable loads as to their maximum rated load, or of the electric grid via the POGI capacity thereof) that the electricity generating system is servicing. [0048] In one or more embodiments, a RES-ESS-CL system or facility can be configured to reduce a correlation of one or more controllable loads with an electric grid. For example, a load profile of the one or more controllable loads (CL(s)) (e.g., a “net load” profile of the CL(s), which may refer to the total / gross load of a CL(s) minus the RES generation allocated to the CL(s)) may be shifted (e.g., in time or in load), adjusted, or modified in a de-correlating manner relative to a load profile of the electric grid (e.g., a “net load” profile of the electric grid, representing the total / gross load of the electric grid minus renewable energy generation allocated to the electric grid) or a power profile (e.g., a “net power” profile) of the electric grid, such that a performance associated with the RES-ESS-CL system (e.g., a financial performance of the RES-ESS-CL system, an asset utilization associated with the RES and/or the ESS, etc.) is improved. For example, the de-correlation can include causing one or more peaks of the load profile of the one or more controllable loads to no longer overlap with, or to overlap less with, one or more peaks of the load profile (e.g., net load profile), net power profile, or price of energy services profile of the electric grid. Alternatively or in addition, the de-correlation can include causing a shape of the load profile of the one or more controllable loads to be substantially inverse relative to, or otherwise differ from (e.g., be flatter than or less flat than, or time shifted), the load profile, power profile, or price of energy services profile of the electric grid, for example as shown graphically in FIGS.9A-9C, discussed below. [0049] The RES of the present disclosure may be oversized more so than other oversized RESes in comparison to the POGI limit because of the RES serves as a baseload for the controllable load rather than the grid. For example, the RES of the RES-ESS-CL system of the present disclosure may be overbuilt by many factors over the power limit of the POGI than what can be reasonably overbuilt in other oversized systems without suffering from inefficiencies or potentially lost energy. As discussed above, known oversized systems are built as a baseload for the grid. As such, RESes for oversized systems are typically capped at the sum of the power limit of the POGI and the ESS, where the ESS is sized to be up to or equal to the POGI, thus yielding a cap for the RES’es of 2x the POGI, which avoids curtailment of energy. For example, if the POGI limit is 100 MW, the ESS is then also sized to deliver 100 MW to the POGI, to be equal to (within allowable limits of the grid operator) or less than the POGI for maximum efficiency while using the maximum available transmission capability to the grid via the POGI. The RES is then limited to 200MW (i.e., 2x the POGI), as any additional power from the system could not go anywhere and thus energy would be lost. [0050] In contrast, the RES of the RES-ESS-CL system of the present disclosure is not limited by the POGI. Rather, the RES may have a capacity that is based on the capacity of the controllable load that is behind-the-meter. As such, the RES may scale three times, four times, five times, or higher than the POGI limit. For example, the POGI limit may be 100 MW, but the controllable load, which may include a plurality of controllable loads, may have a total capacity of 100 MW and the ESS may have a capacity of 300 MW, and the RES may have a capacity of 500MW or other power output capacity. As such, with the behind-the-meter controllable load included in the RES-ESS-CL system, the RES may have a power capacity that is five times (or more) the POGI limit. As a result, the RES-ESS-CL system may be massively overbuilt when providing a baseload for a controllable load rather than providing a baseload to the grid. Thus, economies of scale can be realized for the RES and ESS, making the system more efficient with higher asset utilization. Furthermore, the RES-ESS-CL system of the foregoing example can provide very high capacity factors (which are the same in this example) for the CL and the electric grid, with values that are well in excess of what a known system without a CL would be able to accomplish. This again presents a very high asset utilization for the PGI electric grid connection and/or the CL, where the capacity factors can be more than 80%, and can even approach 100%. While the overbuild of the RES versus the POGI limit is one benefit of the RES-ESS-CL system, another benefit is that the RES-ESS-CL system’s twin configurable architecture (also referred to herein as “twin mode,” “gemini,” and/or “gemini system”) can operate as a baseload or peaker plant for a controllable load and also as a peaker plant or baseload for the grid or a micro-grid or micro-utility grid (e.g., an islanded grid) or a geographically limited utility grid. [0051] For example, while the RES-ESS of the RES-ESS-CL system acts as a baseload for the controllable load, the RES-ESS-CL may be in a twin mode, for example in that the RES-ESS-CL may also operate as a peaker plant and supply power to the grid when a condition to do so is satisfied. In some embodiments, a twin-configurable architecture system as described herein can be implemented in / as a single, standalone power plant, and can be configured to operate in a first mode, in which the system operates as a baseload or semi-baseload plant (i.e., operating between pure baseload and pure peaker) and/or provides ancillary services, and (optionally concurrently with, in parallel with, or overlapping in time with) a second mode, in which the system operates as a peaker plant or semi-peaker (between pure peaker and pure baseload) and/or provides ancillary services to a customer or group of customers. The ancillary services provided in the first mode made be the same as, overlap with, or be different from, the ancillary services provided in the second mode. In various embodiments, the condition may be based on power demand on the grid such as when the power demand on the grid satisfies a predetermined threshold or the delta of available power supply and power demand satisfies a predetermined threshold. During times of high grid demand and low power generation where the RES is not generating enough power to satisfy both the grid connection power capacity and the controllable load power capacity, the RES- ESS-CL may control the load at the controllable load by communicating with the controllable load to reduce power consumption such that the power can be redirected from the controllable load to the grid interconnection point. This may include redirecting power provided by the ESS or the RES from the controllable load to the grid interconnection. [0052] In other embodiments, the overbuilt, high-capacity factor RES-ESS-CL system may experience times when the RES is generating too much power for the ESS and the CL to consume. During these peak power generation times, the RES-ESS-CL system may provide excess power generation to the grid. As such, the grid acts as a source to remove excess generated power or to subsidize the capital costs of building the oversized RES-ESS system by diverting power to the grid when conditions on the grid are favorable such that power can be provided more efficiently and cost effective to the controllable load. In contrast, recent RES-ESS systems are designed to be built to only service the grid. [0053] In one or more embodiments of the present disclosure, the RES-ESS-CL system can be configured to control (e.g., using one or more controllers of the RES-ESS-CL system and/or using communications via one or more communications networks described herein) one or more “legacy” (e.g., non-renewable) power generators, such as a gas turbine(s) or diesel generator(s), in addition to the RES, ESS, and CL. These non-renewable energy sources (referred to herein and in FIG.1 as “NRES”) can be coupled to one or more CLs of the RES-ESS-CL system, one or more ESSes of the RES-ESS-CL system, and/or to the electric grid. [0054] In one or more embodiments of the present disclosure, the RES-ESS-CL system can be configured to function/operate in multiple modes (e.g., more than two modes), each mode including two or more of: operation as a baseload (e.g., at one or multiple different output levels), operation as a peaker plant for an electric grid (e.g., at one or multiple different output levels), operation as a peaker plant for a micro grid or micro-utility (e.g., at one or multiple different output levels), or operation as a provider of one or more ancillary services to the electric grid. As used herein, “ancillary services” can refer to services that help to maintain or supplement the integrity, stability and/or power quality associated with an electric power transmission and/or distribution system. By way of non-limiting example, ancillary services may refer to one or more of: reactive power compensation, regulation including voltage regulation, flicker control, active power filtering, harmonic cancellation, frequency control (including inertia support, frequency containment reserves/primary control, frequency restoration reserves/secondary control, and/or replacement reserves/tertiary control), performing synchronized regulation (e.g., to correct/compensate for changes in electrical imbalances that can affect the stability of a power system), ramp up service, ramp down service, providing contingency reserves (e.g., supplying power to respond to an unexpected electrical outage or failure of an electrical element or system component such as a generator, a transmission line, a circuit breaker, a switch, etc.), black-start regulation (e.g., supplying electrical power for system restoration when the entire electrical grid or a subset thereof loses power), or flexibility reserves (e.g., supplying power to compensate for variability and/or uncertainty over longer timescales than are typically involved with contingency reserves, synchronized regulation and/or black-start regulation), day-ahead scheduling reserve, loss compensation, congestion management, or oscillation damping. [0055] Thus, aspects of the present disclosure provide a smart network of controllable loads, ESSs, and RESes (e.g., solar and wind sharing a grid connection) that are behind-the-meter and in-front-of-the-meter. The RES-ESS-CL system may be “networked” for being centered around a single node (if a node is defined as one connection to the grid) that optimizes costs and capacity factor for the controllable loads (and maximize revenue/profitability/emergency needs) by increasing utilization of assets (such as ESS and RES). As such, aspects of the present disclosure provide more efficiency and better economics for a RES-ESS-CL system over overbuilt RES-ESS systems because the RES-ESS is built as a baseload for the controllable a load, which may benefit from lower cost of electricity and better capacity factors, when taking a system approach. Having a “controllable” load, means that the system architecture is not just limited to the generation, storage, and distribution, but incorporates the load and uses some uncorrelated “grid load” to subsidize economics through better asset utilization by making the RES-ESS a peaker plant. The grid may also provide flexibility and be a source of power when prices on the grid are inexpensive such that the life span of the ESS can be extended by reducing charge/discharge cycles. A controller powered by AI algorithms works to optimally get the most out of the synergies. [0056] As discussed above, the controllable loads may be the new loads (e.g., AI training, data centers, vertical farming, smelters, EV charging, hydrogen production, water treatment plant (including desalination and purification), cryptocurrency mining, or the like) and can have different characteristics than the known loads on the grid (HVAC in hot areas, heating in colder, industrial, etc.). In order to utilize their high capital expenditures these new loads need to run with high utilization, which is a conflict with low capacity factor known renewables. However, at the same time, these new loads are very dependent on finding cheap power as their economics are dominated by electricity costs. While renewables are now often the cheapest form of power, their capacity factors are often low – to increase the capacity factor, one needs storage, which costs additional money. With embodiments of the present disclosure, the controller can cross-subsidize the storage cost and other capital costs with selling power to the grid when those prices are high (for which one usually needs ESS as well as prices are not high when renewables produce), effectively turning the power plant into a peaker plant for the grid, which can also provide valuable ancillary services to the grid or customers. So, by designing an RES and an ESS with one or more controllable loads, one can explore synergies and have a large ESS that is used to both drive capacity factor up and make lots of revenue when grid prices are high. Being behind-the-meter helps with avoiding grid charges that can dominate the economics. [0057] Depending on the grid load profile (and price profile) and controllable load profile, the RES can be optimized in its operation (and design). Running simulation of the RES-ESS-CL system shows that the combination RES-ESS with controllable (and uncorrelated) loads gives better results at lower costs. That is because the ESS is better utilized, and the solar field or wind turbines are larger (EOS). The controllable loads can be in-front-of-the-meter and/or behind-the- meter – behind-the-meter has the additional advantage of maximizing interconnection / grid access that is a constraint, reducing losses, avoiding transmission charges, avoiding grid curtailments, avoiding grid congestion and related charges, and grid operator’s overhead and administrative costs. The grid connection is valuable and expensive and fixed costs, so having more energy flowing through the entire system also drives cost per MWh down. [0058] In some embodiments, the system may include multiple controllable loads (one or more behind-the-meter (e.g. AI training and vertical farming or cooling for data centers) that is the focus for cost optimization and one or more in-front-of-the-meter that may be used to optimize economics and utilization). Being grid connected also allows to run the controllable load(s) on cheap power when that is available from the grid (e.g. wind at night) further reducing costs, or providing or receiving power from ESSs on the grid or an RES. Again, the controller with machine learning/AI can predict and manage the system accordingly. The ESS on the grid may be controlled to store energy when the RES-ESS-CL system has too much production at RES and not enough behind-the-meter load and ESS capacity left or the controller determines that it is better to keep some ESS capacity unused) and the controller can push the power out to the grid. Similarly, when grid net loads are low (e.g., the grid is getting close to overgeneration from renewables) or energy prices are low, and to conserve battery life or stored power on the ESS of the RES-ESS-CL system, the controller may obtain power from an RES on the grid and/or from the grid marketplace. [0059] In various embodiments, loads that are controllable loads may include loads where a controller, described herein, can change the demand by either increasing or decreasing power demand at that load. As such, the present disclosure considers both adjusting energy allocation from the RES and adjusting energy demand from one or more of the controllable loads that can either be on the grid or behind-the-meter. In contrast with known / non-controllable loads, controllable loads of the present disclosure can be controlled such that their energy demand / load has a value that is between 0 and a maximum value thereof, and can be dynamically adjusted or tuned over time, for example by a controller and/or in response to user inputs, AI model outputs, etc. In various embodiments, the behind-the-meter controllable loads allow an energy producer to increase size and performance of the RES. For example, the RES may be built to generate a larger capacity than what the RES can provide to the grid. This provides economy of scale cost and performance advantages over an oversized system without the behind-the-meter controllable loads. When generation is not at peak (e.g. clouds or early morning or late afternoon or low wind etc.) or when the energy storage system included with the RES is full, the excess energy is absorbed by the grid in addition to the ESS and controllable loads. Also, when RES generation is low and the delta between energy supply and demand on the grid is low, the oversized system can deliver more power to more critical or valuable loads on the grid using the stored charge on the ESS and fulfill the bandwidth of what the RES can provide to the grid. As such, the RES-ESS-CL system may be designed for better performance and lower cost, i.e., overall system performance is better such that a more consistent energy supply, capacity, or other ancillary services are provided to the new behind-the-meter loads and the grid by acting as a peaker plant. [0060] In various embodiments of the present disclosure, the controller may include a predictive algorithm such as, for example, model predictive control (MPC), model-based reinforcement learning (MBRL), adaptive model predictive control (AMPC) or other predictive algorithm/machine learning algorithm. MPC may be implemented with a long short-term memory (LSTM), state space model, or transformer architecture. Some embodiments may use a multi- modal time-series forecasting model (e.g., accounting for weather, wind production, solar production, grid demand, and value of behind-the-meter load outputs), examples including: autoregressive–moving-average (ARMA) models (e.g., Seasonal ARIMA); autoregressive integrated moving average (ARIMA) model; generalized autoregressive conditional heteroskedasticity (GARCH) models; vector autoregression models, Holt-Winters exponential smoothing; state space models; and Kalman filters. The predictive algorithm may predict a priority in a future time interval, and based on the prioritization and total predicted energy storage and generation, the predictive algorithm may determine any demand adjustments on the controllable loads and allocate energy and power to the various loads (on or off the grid) or the ESS (on or off the grid) based on a prioritization and other constraints. [0061] The controller may include predictive and machine learning algorithms for balancing energy distribution to the controllable loads. For example, the energy generation and distribution controller may ingest data from various data sources (e.g., a weather forecast, an event schedule, a calendar, historical energy use data, sensor data or other data sources that would be apparent to one of skill in the art in possession of the present disclosure). In other embodiments, the data sources may include state of charge data or analytics of other RES and their ESS or standalone ESSes. These other RESes may include energy storage systems that are not on the network and may be those of competitors or grid resources. As such, a prediction of how much energy storage another RES provides may be beneficial as to anticipate how much energy will be available for the grid at a certain time so that control of the ESSs, controllable loads, or even controllable RESes (e.g., a hydro plant) can be managed. [0062] The controller, using the predictive/ML algorithms, trained on historical or simulator data, may then anticipate energy demand, grid operating parameters, for uncontrollable loads on the grid as well as an energy supply on the power plants. Based on the anticipated energy demand and the energy supply and operating parameters of the grid, the controller may determine whether one or more energy balancing conditions associated with a respective controllable load are satisfied to either increase power distribution to that controllable load or reduce power distribution to that controllable load. For example, in exchange for a better rate on its energy price or some other energy distribution factor that the controllable load desires, the controllable load may allow controller to reduce energy consumption at that controllable load to reallocate the RESes or ESSes energy supply to loads that are not controllable and that may pay a higher premium or are higher prioritized based on various factors (e.g., more necessary / high priority infrastructure such as a hospital, a water distribution plant, critical communication infrastructure, or the like). Furthermore, the controllable loads themselves may be adjusted to reduce or increase energy consumption. [0063] In some embodiments, where the controllable load includes a plurality of controllable loads, the controller and its machine learning/predictive algorithms may efficiently balance the controlling of the loads when power capacity is needed from the controllable load to charge the ESS or to provide power to the grid. For example, the controllable loads may include an AI training data center, a cryptocurrency mining center, and/or a vertical farm. These controllable loads may have load profiles (e.g., net load profiles) that can be operated / controlled in a manner that further optimizes the operation and efficiency of each individual CL while the combined load of the CLs presents a load profile to the grid that is more favorable (e.g., in terms of electric grid stability, POGI utilization efficiency, the behind-the-meter combined RES-ESS-CL system asset utilization, , electric grid ancillary services efficiency and effectiveness provided by the RES-ESS-CL, energy price minimization, and/or etc.) than would exist if each CL were individually and independently controlled. For example, the cryptocurrency mining may fluctuate with the weather as the computers performing the mining may be operating continuously while the cooling of the computers may fluctuate with the outside temperature. The data center may experience a similar profile to that of the cryptocurrency miner while the vertical farm may have a profile of several hours of lower energy needs when dark cycles for the plants are needed. By the controller anticipating the amount of power that the aggregated controllable load requires (or is) to be reduced and when, the controller can intelligently select which controllable load or loads to send instructions for reduction of power demand. In some embodiments, the controller may be aware of various processes that are occurring at the controllable load. For example, a data center may be conducting a time consuming process that takes hours or days to complete as well as processes that are less than a second, seconds, minutes or other short time interval with respect to the grid demand where the machine completing those process may be instructed to idle or consume less power while the machines that are performing the “long” processes remain running. However, in other embodiments, the controller may determine, at a high level, which controllable loads should have their power consumption reduced or increased, provide instructions to those controllable loads, and the controllable loads themselves may have intelligent algorithms to determine which process running on those controllable loads may be reduced or increased based on the parameters provided by the controller of the RES-ESS-CL system. [0064] Similarly, the controllable load may include an energy storage system where the controller may increase or decrease power distribution to the energy storage device. Furthermore, more optimal decisions can be made of which energy storage device in the ESS to store energy. For example, a zinc air battery, heat battery, pumped hydro, gravity energy storage, or hydrogen production facilities may be charged/powered when cheap power is available while a lithium-ion battery may be charged when more expensive power is available, faster response times are anticipated, higher round trip efficiency are beneficial, or when other beneficial conditions are present that would be apparent to one of skill in the art in possession of the present disclosure. As such, a type of energy storage device or other factors associated with the energy storage device may be used to determine when a particular energy storage device is to be charged or how much charge a particular energy storage device is to receive. [0065] As mentioned above, the controllable loads may include their own ESSes. In some embodiments, those ESSes may include a BESS system. However, in other embodiments the controllable load may include other ESSes such as, for example, a heat or thermal storage battery, pumped hydro, gravity energy storage, hydrogen production facilities, or the like. In one example, the controllable load may be a data center, an AI training center, a cryptocurrency miner, or the like that generates a tremendous amount of heat during the operation of the servers performing the operation. To cool servers, these controllable loads also use power from the RES/ESS to cool the servers. In some instances, the controllable load may include a system that can convert the waste heat to cold air or ice that can be stored and then later used to cool the servers when power reduced at the controllable load. The controllable load may reduce the air conditioning used to cool the servers and allow the stored cooling medium to transfer heat from the servers to that cooling medium. [0066] In other embodiments, the controller may also use the anticipated energy demand and the energy supply to balance the storage of energy generated by the RESs on associated batteries. For example, the controller may determine the amount of energy stored on each battery and how those batteries in the power plants are going to distribute the energy in an optimized manner. For example, to preserve the life expectancy of a battery, under normal conditions, that battery may not be completely filled or completely drained (e.g., the battery may be placed in a battery preservation mode) since completely charging and/or completely draining the battery can decrease the useful life expectancy of the battery. However, if the anticipated energy supply and demand indicate a condition where it is more beneficial to fully charge a battery or fully discharge a battery than to consider the life expectancy of the battery, the controller may cause the battery to be fully charged in anticipation of the future event. For example, if there is an anticipated event that requires (or involves) a high demand of energy, the energy generation and distribution controller may fully charge the battery. In other embodiments, the controller may tier the batteries such that a first battery distributes energy based on a first condition, a second battery distributes energy based on a second condition, and a third battery distributes energy based on a third condition. These conditions may be prioritized based on different levels. For example, the third battery may only distribute energy if the price of energy is above a certain threshold or the discharge cycle times are long. [0067] In yet other embodiments of the present disclosure, the energy generation and distribution controller may determine when to provide energy storage to power plants that are not included in the RESs such as power plants that are on the grid. The energy generation and distribution controller may determine conditions where the out-of-network power plant may store energy on the RES’s batteries or other ESS. Using the anticipated energy demand and energy storage determinations made by the machine learning algorithms of the RES-ESS-CL controller, the RES-ESS-CL controller may determine when to purchase power from power plants on the grid, from the grid itself (e.g., via the energy marketplace), or provide storage for contracted out- of-network power plants. The RES-ESS-CL controller may communicate with an application located at the out-of-network power plant similarly to an application provided at the controllable loads and storage of the networked power plants. As such, the systems and methods of the present disclosure provide more optimal and consistent energy generation, storage, and distribution of energy generated by RESs by providing a baseload to one or more controllable load and acting as a peaker plant for the grid, which increases grid reliability while at the same time providing more consistent/higher capacity factor for the “new” loads seen entering the grid. [0068] FIG. 1 illustrates an example twin-mode power generation, storage, and distribution system 100 in accordance with one or more embodiments. While described herein as a “twin”- mode, the inventors of the present disclosure recognize that additional modes may be included, optionally operating simultaneously or overlapping in time, or fewer modes may be operating simultaneously than two. The energy generation, storage, and twin-mode power generation, storage, and distribution system 100 may include a controller 102; a network 104; an RES-ESS- CL system 106 that includes one or more RESes 109, one or more ESSes 107, one or more non- renewable energy sources (NRESes) 113, one or more controllable loads 108, one or more inverters 116, one or more inverters 118, and optionally one or more inverters 119 (e.g., when the NRESes 113 do not have built-in inverter(s) or output AC power directly); an electric grid 110; one or more power data sources 111; one or more known loads (e.g., a load 114a and/or a load 114b); one or more controllable load(s) 114c that are in-front-of-the-meter, one or more RESes 120, and one or more ESSes 122. The one or more NRESes 113 can include, for example, one or more diesel, gasoline, hydrogen, heavy fuel oil, jet fuel, or other types of fuel generators (which may or may not include their own associated, built-in inverters) and/or one or more gas turbine generators (which may or may not include their own associated, built-in inverters). While some components are listed and illustrated as one or more in number, other components that are illustrated as individual components may include more than one of those components. Also, herein, while a component may include one or more, for ease of discussion, the component may be described as one component (e.g., one or more controllable loads 108 may simply be described as a controllable load for discussion purposes). [0069] The load 114a, the load 114b, the controllable load(s) 114c, one or more NRESes 124, RES 120 and ESS 122 may be electrically coupled to the electric grid 110. The one or more NRESes 124 can include, for example, one or more gas turbines and/or one or more diesel generators. The controllable load(s) 114c may be paired with / electrically coupled to the one or more NRESes 124 (e.g., such that the one or more NRESes can serve, for example, as backup energy sources for the controllable load(s) 114c). The load 114a, the load 114b, the one or more NRESes 124 and/or the controllable load(s) 114c may be remote from each other and have separate power requirements. The load 114a may have a first power delivery profile which details power requirements for the load 114a at different times. The load 114b may have a second power delivery profile which details power requirements for the load 114b at different times. The controllable load(s) 114c may have a third power delivery profile which details power requirements for the controllable load(s) 114c at different times. In some embodiments, the electric grid 110 may be a utility grid owned and operated by a single utility or system operator. In other embodiments, the electric grid 110 may be a plurality of electrical connections allowing for the transmission of power from the RES-ESS-CL system 106 to the load 114a, the load 114b, and the controllable load(s) 114c. In some embodiments, the electric grid 110 may include a micro-grid or micro-utility (e.g., a self-sustained grid) that creates its own grid with customers. For example, a village in Africa or an island has its own utility with paying customers. [0070] The RES 109 may include a first renewable energy power plant (REPP). Examples of REPPs include, but are not limited to, solar plants, wind plants, geothermal plants, and biomass plants. However, the RES 109 may include multiple REPPs. A portion of the multiple REPPs may be of a first type of REPP (e.g., multiple solar plants), another portion of the multiple REPPs may be a of a second type (e.g., multiple wind turbines), yet another portion of the multiple REPPs may be of a third type and up to a nth type. The RES-ESS may include an energy storage system (ESS) 107. An example of an ESS is a battery. A battery-based ESS may be called a battery ESS or BESS. As discussed above, the ESS may include a heat or thermal storage battery, pumped hydro, gravity energy storage, hydrogen production facilities, or other energy storage systems that would be apparent to one of skill in the art in possession of the present disclosure. The RES 109 may have a first power output that varies over time. The multiple REPPs of different types may share the ESS or have separate ESSes or a combination of shared and dedicated ESSes. In various embodiments, a ratio of the power generated by the RES 109 to the power limit of the POGI may be any ratio greater than 2 (e.g., can be a ratio of 3, 4, 5, or 6). For example, the power generated by the RES 109 can be between about 3 and about 6 times the power limit of the POGI, since the RES 109 size is not limited to the POGI because the controllable loads 108 behind-the-meter may allow the RES 109 to upsize in scale. The ratio may be optimized based on the controllable loads, the type or types of RESes and the ESS as well as grid energy consumption and generation such that a high capacity factor is achieved for the RES-ESS-CL system 106 with minimal energy curtailment. As an example, for a solar PV RES in areas with sunshine where the natural capacity factor of the sun is between about 15% (e.g., northern Europe or Canada) and about 30% (e.g., northern Africa or south-western US deserts), the higher ratios described herein allow the RES- ESS-CL system to have a higher asset utilization and increase the capacity factors (e.g., as measured relative to the POGI capacity, i.e., capacity factors of POGI utilization) substantially (e.g., capacity factors of about 60% to about 90%) when compared to a RES-ESS system with a POGI ratio of about 2 that has capacity factors of POGI utilization of about 35% to about 55%. Thus, while the RES 109 is built as a baseload for the controllable load 108, the entire RES-ESS- CL system 106 may operate as a twin-mode system where it has a dual purpose to (1) serve as a baseload for the controllable loads 108 or in some circumstances controllable load(s) 114c and (2)serve as a peaker plant for the electric grid 110 to provide power to the grid during times of high demand and low supply, and/or ancillary services, as well as an outlet to provide excess power generation when the ESS 107 and the controllable load 108 cannot consume any additional power. These modes may operate concurrently or separately. [0071] In some embodiments, the RES 109 may be coupled to an inverter 116. The inverter 116 may convert DC power generated by the RES 109 to AC power provided to the electric grid 110 at a grid interconnection point. The grid interconnection point has a point of grid interconnect (POGI) limit. The inverter 116 may have an AC power output limit that is greater than the POGI limit. The RES-ESS-CL system 106 may include an inverter 118 that may be coupled between the ESS 107 and the electric grid 110 and coupled between the inverter 116 and the electric grid 110. The inverter 118 may be bidirectional such that it converts RES AC power outputted from the inverter 116 to DC power that can charge the ESS 107. Similarly, the inverter 118 may convert ESS DC power to AC power that can be outputted to the electric grid 110. The RES-ESS-CL system 106 may also include an inverter 119 that may be coupled between the NRES 113 and the electric grid 110, and the NRES 113 may be directly electrically coupled to the ESS 107 (e.g., such that the NRES 113 can be used to charge the ESS 107) and the controllable load(s) 108 (e.g., such that the NRES 113 can serve as a backup energy source for the controllable load 108). In various embodiments, the inverter 118 may be optionally built to have an AC power output that is greater than the POGI. In some embodiments, the inverter 118 may be a bi-directional inverter and receive grid AC power from the electric grid 110 and convert the grid AC power to DC power that is used to charge the ESS 107 or power the controllable loads (or both). The controllable load 108 may be coupled between the inverter 116 and the electric grid 110 and the inverter 118 and the electric grid 110. In some embodiments, the controllable load 108 may be electrically coupled with the RES 109 or the ESS 107 directly such that it receives DC power from the RES 109 or the ESS without converting from DC to AC power and back again via inverters. In some embodiments, the electric grid 110 may provide power to the controllable load 108 so other inverters are bi- directional inverters (not illustrated) may be used to convert the AC power from the electric grid 110 to DC power supplied directly to the controllable load 108. However, it is envisioned that the controllable load may operate off of AC or DC power and require (or use) bi-directional inverters. Grid power 108 may be used by the controllable load 108 in times when the net load or price of power on the grid 108 is below a threshold. As such, using the inexpensive power on the electric grid 110 may conserve the power on the ESS 107 or the life span of the ESS 107 by only using the ESS 107 when conditions require it, and the excess renewable energy available on the electric grid 110, or inexpensive energy, or possibly negatively priced energy (i.e., when a customer is paid to consume electricity) on the electric grid 110 may also be used to charge the ESS. [0072] In various embodiments, the controllable load 108 and the ESS 107 may have similar ratios of demand. For example, the ESS 107 may be sized to at least service the power interconnect of the controllable load 108. The RES 109 may be sized such that at peak production, the RES 109 may provide its power to the controllable load 108, the ESS 107, and the electric grid 110. For example, the POGI limit for the electric grid 110 may be 100 MW, the power connection limit for the controllable load 108 may be 200 MW, and the power connection for the ESS 107 may be 300 MW such that the ESS 107 may provide power to the electric grid 110 and the controllable load 108. Thus, the RES 109 may be oversized up to 600 MW, which is a 6X oversize to the POGI. [0073] The RES-ESS-CL system 106 may communicate with the networked energy RES-ESS- CL controller 102 via a network 104. Similarly, the controllable load(s) 108 and 114c, the RESes 109 and 120, and the ESSes 107 and 122 may communicate with the RES-ESS-CL controller 102 via a network 104. Additionally, the ESS 122, ESS 107, RES 120 and/or RES 109 can communicate with the electric grid 110 via the network 104, for example using one or more supervisory control and data acquisition (SCADA) systems optionally residing on, accessible by, and/or operatively coupled to one or more of the ESS 122, ESS 107, RES 120 and/or RES 109. Additionally, the NRES 113 and/or NRES 124 can communicate with the electric grid 110 via the network 104. Additionally, the inverter 116, the inverter 118, and/or the inverter 119 can communicate with the RES 120, the RES 109, the ESS 107 and/or the ESS 122 via the network 104. Furthermore, the controller 102 may communicate with power data sources 111 via the network 104. The data sources may include sensors, weather data, local schedules, or any other system data or third-party information that would be apparent to one of skill in the art in possession of the present disclosure. The network 104 may be any local area network (LAN), wide area network (WAN) and/or satellite-based network. In some embodiments, the network 104 is the internet. In other embodiments, the network 104 is a private communications network. The RES- ESS-CL controller 102 may include a processor and a memory. [0074] The RES-ESS-CL controller 102 may control the RES 109 and cause the RES 109 to direct power to the ESS 107, the controllable load 108, and the electric grid 110. The RES-ESS- CL controller 102 may also control the ESS 107 on when to charge or discharge power received from the inverter 118 from the RES 109 or in some embodiments from the electric grid 110. The RES-ESS-CL controller 102 may also control the power demand at the controllable load(s) 108 and 114c. While a specific system is described, one of skill in the art in possession of the present disclosure will recognize that other variations, components, multiple RESes, ESSs, and controllable loads may be contemplated without deviating from the scope of the present disclosure. [0075] Although not explicitly shown in FIG.1, multiple switches (e.g., electronic switches, low, medium or high voltage switches or smart controllable breakers) may be positioned throughout the system 100 at appropriate locations to facilitate selection and control (e.g., via controller 102) of various operational modes that can include, but are not limited to, one or more of: supplying electricity to the electric grid 110 from inverter 116 (or direct if the NRES has AC output), supplying electricity to the electric grid 110 from inverter 118, supplying electricity to the electric grid 110 from inverter 119, supplying electricity to the electric grid 110 from RES 120, supplying electricity to the electric grid 110 from ESS 122, supplying electricity to the electric grid 110 from NRES 113, supplying electricity to the electric grid 110 from NRES 124, powering controllable load(s) 108 using the electric grid 110, powering controllable load(s) 114c using the electric grid 110, powering load(s) 114a using the electric grid 110, and/or powering load(s) 114b using the electric grid 110. [0076] In one or more implementations of the system 100 of FIG.1, the controller 102 can be configured to dynamically control one or more other components of the system 100 of FIG.1, e.g., in a manner that varies over time and/or automatically in response to / based on one or more user- provided instructions and/or AI model outputs). For example, the controller 102 can be programmed / configured to variously perform one or more of the following: control (e.g., increase, decrease, piecewise modify, etc.) a net load profile of the controllable load(s) 108 (including its subsystems, e.g. cooling systems for a data center); modify an operational mode of the controllable load(s) 108; modify a number of controllable loads 108 that are in operation for a given predefined interval of time; modify a distribution of load across multiple controllable loads 108 (e.g., in a uniform or non-uniform manner) for a given predefined interval of time; cause / control operation of the controllable load(s) 108 (and optionally of the NRES 113) while charging ESS 107 (e.g., with a predefined, modifiable charge rate / profile) and/or operating RES 109 (and/or RES 120) (with or without supplying power to the electric grid 110); cause / control operation of the controllable load(s) 108 (and optionally of the NRES 113) while discharging ESS 107 (e.g., with a predefined, modifiable discharge rate / profile) and/or operating RES 109 (and/or RES 120) and receiving power from the electric grid 110; cause / control operation of the controllable load(s) 108 while charging ESS 107 (e.g., with a static or dynamically adjusted charge rate), operating NRES 113 (if present) and/or operating RES 109 (and/or RES 120) (with or without supplying power to the electric grid 110); cause / control operation of the controllable load(s) 108 while discharging ESS 107 (e.g., with a predefined, modifiable discharge rate / profile), operating NRES 113 (if present) and/or operating RES 109 (and/or RES 120) and receiving power from the electric grid 110); cause / control operation of the controllable load(s) 108 and ESS 107 while operating NRES 113 (if present) and curtailing operations of RES 109 (and/or RES 120) (with or without supplying power to the electric grid 110); cause / control operation of the controllable load(s) 108 while placing portions or all of ESS 107 into an energy conservation mode (e.g., reducing parasitic loads and/or HVAC systems associated with ESS 107) and while operating NRES 113 (if present) and/or RES 109 (and/or RES 120) and receiving power from the electric grid 110; cause / control operation of the controllable load(s) 108 while placing ESS 107 into a frequency regulation services mode (with or without supplying power from the NRES 113 and/or the RES 109 to the electric grid 110); cause / control operation of the controllable load(s) 108 and RES 109 (and/or RES 120) while operating NRES 113 (if present) and charging ESS 107 (e.g., with a predefined, modifiable charge rate / profile) (with or without supplying power to the electric grid 110); cause / control operation of the controllable load(s) 108 while preventing operation of the RES 109 and optionally operating NRES 113 and optionally charging ESS 107 or discharging ESS 107 (with or without supplying power to the electric grid 110); or cause / control operation of the controllable load(s) 108 and RES 109 while operating NRES 113 (if present), discharging ESS 107 (e.g., with a predefined, modifiable discharge rate / profile), and receiving power from the electric grid 110. [0077] In some embodiments, any two or more of the foregoing system operational regimes may be combined or concatenated, for example such that they are executed sequentially in time by the controller 102, e.g., as part of an electrical resource deployment schedule. Moreover, an ordering of such combination(s) of system operational regimes may vary over time (e.g., automatically via the controller 102, optionally dynamically and/or in response to an AI model output(s)). Resource deployment schedules can be specific to / unique to individual power plants within a system (e.g., a networked system) of power plants, each power plant including a system (e.g., system 100 of FIG. 1) of the present disclosure, and the resource allocation strategy implemented by each resource deployment schedule (for each power plant) can differ from the others, such that the overall resource allocation strategy reflected by the system of power plants is diversified. [0078] In some embodiments, one or more of the foregoing system operational regimes may be selected by the controller 102 based on predictive analytics / analyses performed by one or more AI models. For example, predictions relating to one or more of weather, grid conditions, electricity demand information, load responses, and/or marketplace pricing for electricity services (e.g., including ancillary services) on / via the electric grid can be taken into account when generating and/or modifying the foregoing system operational regimes and/or the associated resource allocation strategies. [0079] According to one or more embodiments of the present disclosure, by making “load” a variable (via the use and control of one or more CLs, as described herein), resource (e.g., energy resources and/or financial resources) allocation strategies that are more sophisticated and/or complex than those associated with known systems lacking a CL(s), e.g., facilitating more granular adjustments and optimizations. Moreover, resource allocation strategies not previously possible (e.g., in terms of flexibility, energy efficiency, cost-efficiency, size, scale, etc.) with known systems lacking a CL(s) can be developed / realized. By adding CL(s) to a RES-ESS system, the ability to optimize the overall performance of the system goes up / improves, from just one dimension of the RES-ESS system (e.g., where the ESS may be controllable) to a multi- dimensional control regime allowing for multi-dimensional optimization strategies, which may more than linearly improve system performance and facilitate the economically viable construction and operation of larger, more efficient, power systems / plants. For embodiments that include NRES(es), the controllability of the overall system can be further increased, such that system performance may be further enhanced and the size of the power system / plant that can be constructed and operated may be even larger. [0080] In some embodiments, one or more functionalities of the system 100 of FIG.1 can be combined with or replaced by one or more functionalities of any of system 400 of FIG.4, system 1000 of FIG.10, system 1300 of FIGs.13-17, and/or system 2000 of FIG.20. Alternatively or in addition, one or more functionalities of the controller 102 of FIG.1 can be implemented using one or more functionalities / features of any of controller 200 of FIG.2, controller 1102 of FIG. 11, controller 1802 of FIG. 18, and/or controller 2102 of FIG. 21. Alternatively or in addition, the system 100 of FIG.1 can be configured to perform one or more of method 300 of FIG.3, method 500 of FIG.5, method 600 of FIG.6, method 700 of FIG.7, method 800 of FIG.8, method 1200 of FIG.12, method 1900 of FIG.19, or method 2200 of FIG.22. [0081] FIG. 2 of the present disclosure illustrates an embodiment of an RES-ESS-CL controller 200 that may be the RES-ESS-CL controller 102 discussed above with reference to FIG. 1. While described as a standalone system, those skilled in the art will appreciate that the RES-ESS-CL controller 200 may be distributed across many computing devices such as in a cloud environment. In the illustrated embodiment, the RES-ESS-CL controller 200 includes a chassis 202 that houses the components of the RES-ESS-CL controller 200, only some of which are illustrated in FIG.2. For example, the chassis 202 may house a processing system (not illustrated) and a non-transitory memory system (not illustrated) that includes instructions that, when executed by the processing system, cause the processing system to provide an RES-ESS-CL engine 204 that is configured to perform the functions of the RES-ESS-CL engines or the RES-ESS-CL controller discussed below. In the specific example illustrated in FIG.2, the RES-ESS-CL engine 204 may include an RES-ESS-CL predictive algorithm 205 that is configured to perform the functions of the RES-ESS-CL predictive algorithms discussed herein. In various embodiments, the RES-ESS- CL predictive algorithm 205 may ingest data provided by data sources and anticipates energy demand and energy supply, grid conditions, or any other functionality discussed herein. In various embodiments, the RES-ESS-CL predictive algorithm 205 may include a network simulator to model behavior, which may predict the components being incorporated into the grid by running simulations due to lack of historical data. In other examples, the RES-ESS-CL predictive algorithm 205 may include model predictive control or other predictive algorithms/machine learning algorithms that would be apparent to one of skill in the art in possession of the present disclosure. [0082] The chassis 202 may further house a communication system 206 that is coupled to the energy generation, storage, and distribution engine 204 (e.g., via a coupling between the communication system 206 and the processing system) and that is configured to provide for communication through the communication network 104 as detailed below. The chassis 202 may also house a storage system 208 that is coupled to the RES-ESS-CL engine 204 through the processing system and that is configured to store the rules or other data utilized by the RES-ESS- CL engine 204 to provide the functionality discussed below. While an RES-ESS-CL controller 200 has been illustrated, one of skill in the art in possession of the present disclosure will recognize that other RES-ESS-CL controller (or other devices operating according to the teachings of the present disclosure in a manner similar to that described below for the RES-ESS-CL controller 200) may include a variety of components and/or component configurations for providing known computing device functionality, as well as the functionality discussed below, while remaining within the scope of the present disclosure as well. [0083] FIG. 3 depicts an embodiment of a method 300 of renewable energy generation, storage, and distribution, which in some embodiments may be implemented with at least some of the components of FIGs.1 and 2 discussed above. As discussed below, some embodiments make technological improvements to RES-ESS systems that are overbuilt (e.g., whereby the ESS component(s) can store excess capacity generated by the RES(es) and not otherwise consumed, supplied to the grid, etc.) and/or that otherwise provide a “twin-mode” system that serves a high capacity baseload for a controllable load (optionally in combination with providing auxiliary services) and as more of a peaker plant to the grid (optionally in combination with providing auxiliary services). Some or all of the steps of the method 300 may be performed by other actors in the energy generation, storage, and twin-mode power generation, storage, and distribution system 100 and still fall under the scope of the present disclosure. Furthermore, and as mentioned above, the RES-ESS-CL controller 102/200 may include one or more processors or one or more servers, and thus the method 300 may be distributed across the those one or more processors or the one or more servers. [0084] The method 300 may begin at block 302 where first instructions are provided to at least one of at least one RES or at least one ESS to provide a first portion of the electric power generated by the at least one RES or stored by the at least one ESS to at least one controllable load up to an aggregated power demand. [0085] The method 300 may proceed to block 304 where second instructions are provided to at least one of the at least one RES or the at least one ESS to provide power to the electric grid via the grid interconnection point only if the electric power generated by the at least one RES exceeds the aggregated power capacity of the ESS and the aggregated power demand or the controller using a predictive algorithm and power data obtained from the power data sources, determines that a grid condition exists in a power system forecast (e.g., a forecast or prediction specifying one or more of: electricity demand, electricity availability, grid capacity, grid availability and grid congestion, or pricing of electricity or pricing of ancillary services over a given time / during a predefined period of time). [0086] The method 300 may proceed to block 306 where if the grid condition exists, provide third instructions to the at least one controllable load to decrease power demand (or, optionally, to increase power demand) at the at least one controllable load. Thus, the twin-mode power generation, storage, and distribution system may provide, at a first time and by at least one of the RES 109 or the ESS 107, power to at least one of a POGI to the grid 110 or at least one controllable load 108 that is behind-the-meter. At a second time, power may be received from the POGI to at least one of the ESS 107 or the at least one controllable load. At the second time, power may also be provided from at least one of the RES 109 or the ESS 107 to the controllable load 108 or power may be provided from the RES 109 to the ESS 107. At a third time, no power may be provided or received via the POGI and power may be provided from at least one of the ESS 107 to the controllable load 108, the RES 109 to the controllable load 108, or from the RES 109 to the ESS 107. [0087] As such, the grid 110 is typically provided with power when there is high demand for power on the grid (which usually coincides with peak prices). This may occur only 1%-10% of the time but other percentages are contemplated. As such, the RES-ESS-CL system 106 may act as a peaker plant in some cases and as a baseload for the controllable load 108. Thus, the grid 110 may be used for only making money when prices are peak or to avoid curtailing energy within the RES- ESS-CL system 106. When prices are low on the grid 110, the RES-ESS-CL controller 102 may instruct for the controllable load 108 to consume power from the grid 110 that may be cheaper such as nighttime generated wind power. Thus, systems and methods of the present disclosure provide a primary customer with behind-the-meter load to get high capacity factor. Now with networking added an approximately 3x or even higher over capacity may be built. This overcomes the typical 2x overbuilds in overbuilt RES-ESS systems, which allows for more efficient systems with a twin-mode base load and peaker plant configuration. [0088] FIG.4 is a diagram that illustrates an exemplary computing system 400 in accordance with embodiments of the present technique. Various portions of systems and methods described herein may include or be executed on one or more computer systems similar to computing system 400. For example, the networked energy generation, storage, and distribution RES-ESS-CL controller 102/200, the power plant 106a, the power plant 106b, the controllable load 108a, the controllable load 108b, the power plant 112 and the controllable load(s) 114c may include the computing system 400. In another example, the EMS controller 1305/1800, the RES 1335, inverters 1315, 1340, and 1360, ESSs 1310 and 1365, the loads 1370 and 1375, or the meters 1320 and 1350 of FIG. 13 may include the computing system 400. Further, processes, operations, services, and modules described herein may be executed by one or more processing systems similar to that of computing system 400. [0089] Computing system 400 may include one or more processors (e.g., processors 410a- 410n) coupled to system memory 420, an input/output I/O device interface 430, and a network interface 440 via an input/output (I/O) interface 450. A processor may include a single processor or a plurality of processors (e.g., distributed processors). A processor may be any suitable processor capable of executing or otherwise performing instructions. A processor may include a central processing unit (CPU) that carries out program instructions to perform the arithmetical, logical, and input/output operations of computing system 400. A processor may execute code (e.g., processor firmware, a protocol stack, a database management system, an operating system, or a combination thereof) that creates an execution environment for program instructions. A processor may include a programmable processor. A processor may include general or special purpose microprocessors. A processor may receive instructions and data from a memory (e.g., system memory 420). Computing system 400 may be a uni-processor system including one processor (e.g., processor 410a), or a multi-processor system including any number of suitable processors (e.g., 410a-410n). Multiple processors may be employed to provide for parallel or sequential execution of one or more portions of the techniques described herein. Processes, such as logic flows, described herein may be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating corresponding output. Processes described herein may be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). Computing system 400 may include a plurality of computing devices (e.g., distributed computer systems) to implement various processing functions. [0090] I/O device interface 430 may provide an interface for connection of one or more I/O devices 460 to computer system 400. I/O devices may include devices that receive input (e.g., from a user) or output information (e.g., to a user). I/O devices 460 may include, for example, graphical user interface presented on displays (e.g., a cathode ray tube (CRT) or liquid crystal display (LCD) monitor), pointing devices (e.g., a computer mouse or trackball), keyboards, keypads, touchpads, scanning devices, voice recognition devices, gesture recognition devices, printers, audio speakers, microphones, cameras, or the like. I/O devices 460 may be connected to computer system 400 through a wired or wireless connection. I/O devices 460 may be connected to computer system 400 from a remote location. I/O devices 460 located on remote computer system, for example, may be connected to computer system 400 via a network and network interface 440. [0091] Network interface 440 may include a network adapter that provides for connection of computer system 400 to a network. Network interface 440 may facilitate data exchange between computer system 400 and other devices connected to the network. Network interface 440 may support wired or wireless communication. The network may include an electronic communication network, such as the Internet, a local area network (LAN), a wide area network (WAN), a cellular communications network, or the like. [0092] System memory 420 may be configured to store program instructions 401 or data 402. Program instructions 401 may be executable by a processor (e.g., one or more of processors 410a- 410n) to implement one or more embodiments of the present techniques. Instructions 401 may include modules of computer program instructions for implementing one or more techniques described herein with regard to various processing modules. Program instructions may include a computer program (which in certain forms is known as a program, software, software application, script, or code). A computer program may be written in a programming language, including compiled or interpreted languages, or declarative or procedural languages. A computer program may include a unit suitable for use in a computing environment, including as a stand-alone program, a module, a component, or a subroutine. A computer program may or may not correspond to a file in a file system. A program may be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program may be deployed to be executed on one or more computer processors located locally at one site or distributed across multiple remote sites and interconnected by a communication network. [0093] System memory 420 may include a tangible program carrier having program instructions stored thereon. A tangible program carrier may include a non-transitory computer readable storage medium. A non-transitory computer readable storage medium may include a machine readable storage device, a machine readable storage substrate, a memory device, or any combination thereof. Non-transitory computer readable storage medium may include non-volatile memory (e.g., flash memory, ROM, PROM, EPROM, EEPROM memory), volatile memory (e.g., random access memory (RAM), static random access memory (SRAM), synchronous dynamic RAM (SDRAM)), bulk storage memory (e.g., CD-ROM and/or DVD-ROM, hard-drives), or the like. System memory 420 may include a non-transitory computer readable storage medium that may have program instructions stored thereon that are executable by a computer processor (e.g., one or more of processors 410a-410n) to cause the subject matter and the functional operations described herein. A memory (e.g., system memory 420) may include a single memory device and/or a plurality of memory devices (e.g., distributed memory devices). Instructions or other program code to provide the functionality described herein may be stored on a tangible, non- transitory computer readable media. In some cases, the entire set of instructions may be stored concurrently on the media, or in some cases, different parts of the instructions may be stored on the same media at different times. [0094] I/O interface 450 may be configured to coordinate I/O traffic between processors 410a- 410n, system memory 420, network interface 440, I/O devices 460, and/or other peripheral devices. I/O interface 450 may perform protocol, timing, or other data transformations to convert data signals from one component (e.g., system memory 420) into a format suitable for use by another component (e.g., processors 410a-410n). I/O interface 450 may include support for devices attached through various types of peripheral buses, such as a variant of the Peripheral Component Interconnect (PCI) bus standard or the Universal Serial Bus (USB) standard. [0095] Embodiments of the techniques described herein may be implemented using a single instance of computer system 400 or multiple computer systems 400 configured to host different portions or instances of embodiments. Multiple computer systems 400 may provide for parallel or sequential processing/execution of one or more portions of the techniques described herein. [0096] Those skilled in the art will appreciate that computer system 400 is merely illustrative and is not intended to limit the scope of the techniques described herein. Computer system 400 may include any combination of devices or software that may perform or otherwise provide for the performance of the techniques described herein. For example, computer system 400 may include or be a combination of a cloud-computing system, a data center, a server rack, a server, a virtual server, a desktop computer, a laptop computer, a tablet computer, a server device, a client device, a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a vehicle-mounted computer, or a Global Positioning System (GPS), or the like. Computer system 400 may also be connected to other devices that are not illustrated, or may operate as a stand-alone system. In addition, the functionality provided by the illustrated components may in some embodiments be combined in fewer components or distributed in additional components. Similarly, in some embodiments, the functionality of some of the illustrated components may not be provided or other additional functionality may be available. [0097] In some embodiments, one or more functionalities of the system 400 of FIG.4 can be combined with or replaced by one or more functionalities of any of system 100 of FIG.1, system 1000 of FIG.10, system 1300 of FIGs.13-17, and/or system 2000 of FIG.20. Alternatively or in addition, the system 400 of FIG. 4 can be configured to perform one or more of method 300 of FIG.3, method 500 of FIG.5, method 600 of FIG.6, method 700 of FIG.7, method 800 of FIG. 8, method 1200 of FIG.12, method 1900 of FIG.19, or method 2200 of FIG.22. [0098] FIG. 5 is a flow diagram showing a first method for controlling a power generation, storage, and distribution system, in accordance with some embodiments. The method 500 of FIG. 5 may be performed using the system 100 of FIG.1, the system 200 of FIG.2, and/or the computer system 400 of FIG.4. For example, method 500 of FIG.5 can be performed using a system that includes at least one renewable energy source (RES), at least one energy storage system (ESS), and a controller. The at least one RES may be configured to electrically couple to a grid interconnection point of an electric grid, and an aggregated power output capacity of the at least one RES may exceed a point of grid interconnect (POGI) limit of the grid interconnection point. The at least one ESS can be electrically coupled to the grid interconnection point and the at least one RES. The at least one ESS can have an aggregated power capacity that is less than the aggregated power output capacity of the at least one RES. The controller can be communicatively coupled with the at least one controllable load, the at least one ESS, and the at least one RES. The controller can be configured to perform the method 500 of FIG. 5 by providing, at 502, a first instruction to at least one of the at least one RES or the at least one ESS to provide a first portion of electric power generated by the at least one RES or stored by the at least one ESS to at least one controllable load up to an aggregated power demand. The controller can also be configured to provide, at 504, a second instruction to at least one of the at least one RES or the at least one ESS to provide a second portion of electric power to the electric grid in response to (A) electric power generated by the at least one RES exceeding an aggregated power capacity and the aggregated power demand, or (B) the controller, using a predictive algorithm and power data, determining that a grid condition exists in a power system forecast. The controller can also be configured to provide, at 506 and in response to determining that the grid condition exists without the electric power generated by the at least one RES exceeding the aggregated power capacity and the aggregated power demand, a third instruction to the at least one controllable load to decrease a power demand (or, alternatively, to increase a power demand) at the at least one controllable load. [0099] In some implementations of method 500, the aggregated AC power output capacity of the at least one RES exceeds the POGI limit by a factor of at least about 1.3. [00100] In some implementations of method 500, the grid condition is associated with at least one of a price of power associated with the electric grid, a demand for power from the electric grid, a temperature associated with the grid, an operating conditions associated with the grid, a price of ancillary services associated with the electric grid, a curtailment order associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. Alternatively or in addition, the grid condition can include or be associated with a relative value of energy for a given location, day and/or time (e.g., a value of energy to power an air conditioner on a hot day may be higher than a value of that energy on a cool day). A grid condition may be determined to exist when one or more of the following exceeds a predefined maximum threshold value, is below a predefined minimum threshold value, falls within a predefined threshold range, or falls outside a predefined threshold range: a price of power associated with the electric grid, a demand for power from the electric grid, a temperature associated with the grid, an operating conditions associated with the grid, a price of ancillary services associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. As used herein, a curtailment associated with the electric grid can refer to a deliberate reduction in power output below what could have been produced, and can occur, by way of non-limiting example, in response to an emergency condition, or according to a predefined schedule, or as a measure to balance energy supply and demand resulting from transmission or generation constraints. [00101] In some implementations of method 500, the controller is further configured to operate the at least one RES or the at least one ESS as at least one of a peaker plant for the electric grid or a provider of ancillary services to the electric grid, and the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00102] In some implementations of method 500, the at least one controllable load includes a plurality of controllable loads, and the controller is further configured to provide instructions to the plurality of controllable loads to balance an energy distribution associated with the plurality of controllable loads. [00103] In some implementations of method 500, the at least one controllable load (CL) includes a data center. Alternatively or in addition, the at least one controllable load can include one or more of: an artificial intelligence (AI) training center, a cryptocurrency miner, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility (e.g., an electrolyzer), a smelter, water treatment plant (including desalination and purification), an industrial process heater, or a thermal battery. [00104] In some implementations of method 500, the controller is configured to select the first instruction such that a correlation of the at least one controllable load with the electric grid is one of reduced or increased in response to the first instruction or in response to the at least one controllable load executing the first instruction. For example, when the net load on the electric grid is below a certain threshold, e.g., the grid is getting close to an overgeneration condition bringing the electric grid close to an unstable condition, or when energy prices are negative, the first instruction may result in more energy being consumed by the controllable load. Alternatively or in addition, in some implementations of method 500, the controller is configured to select the first instruction such that a correlation of (1) at least one peak of a net load profile associated with the at least one controllable load, with (2) at least one peak of a net load profile associated with the electric grid is reduced in response to the first instruction or in response to the at least one controllable load executing the first instruction. [00105] In some implementations of method 500, the first instruction is configured to cause a reduction in a correlation of the at least one controllable load with the electric grid in response to the first instruction or in response to the at least one controllable load executing the first instruction. Optionally, one or more correlations described herein is associated with a predefined time period. [00106] In some implementations of method 500, the controller is further configured to cause delivery of power from the electric grid to the at least one controllable load. [00107] In some implementations of method 500, a load profile of the at least one controllable load can be controlled to be less correlated with a load profile of the electric grid. In some implementations of method 500, a load profile of the at least one controllable load differs from a load profile of at least one additional load electrically coupled to the electric grid. [00108] In some implementations of method 500, a correlation of a net load profile of the at least one controllable load to a net load profile of the electric grid can be reduced or otherwise changed during peak price times and increased during times when the grid energy prices are low. [00109] In some implementations of method 500, the system is configured to: (1) operate in a first mode as one of a baseload, a semi-baseload, or a peaker plant for the at least one controllable load, and (2) concurrently with operating in the first mode, operate in a second mode as a peaker plant for the electric grid. [00110] In some implementations of method 500, the system (e.g., the system 100 of FIG. 1, the system 200 of FIG.2, and/or the computer system 400 of FIG.4) has an associated capacity factor of at least about 60%, or at least about 65%, or at least about 70%, or at least about 75%, or at least about 80%, or at least about 80%, or at least about 85%, or at least about 90%, or at least about 95%, or approaching about 100%, or between about 60% and about 90%, or between about 50% and about 80%, or between about 70% and about 90%, or between about 75% and about 95% or between 80% and about 100%. [00111] In some implementations of method 500, a ratio of the power generated by the at least one RES to an aggregate load of the at least one controllable load is between about 3 and about 6, or is between about 4 and about 7, or is between about 3 and about 9, or is between about 6 and about 9, or has a value of about 3, or has a value of about 4, or has a value of about 5, or has a value of about 6, or has a value of about 7, or has a value of about 8, or has a value of about 9, or has a value of about 10. [00112] In some implementations of method 500, the controller is further configured to provide a fourth instruction to at least one non-renewable energy source (NRES) to cause the at least one NRES to provide a third portion of electric power generated by the at least one NRES to the at least one controllable load, in a behind-the-meter manner and/or via direct access. [00113] FIG.6 is a flow diagram showing a second method for controlling a power generation, storage, and distribution system, in accordance with some embodiments. The method 600 of FIG. 6 may be performed using the system 100 of FIG.1, the system 200 of FIG.2, and/or the computer system 400 of FIG. 4. As shown in FIG. 6, the method 600 includes providing, at 602, at a first time and by at least one of a renewable energy source (RES) or an energy storage system (ESS), power to a point of grid interconnect (POGI) associated with an electric grid, the POGI disposed between at least one controllable load and the electric grid. The method 600 also includes providing, at 604 and at a second time and by the at least one of the RES or the ESS, power to the at least one controllable load. The method 600 also includes providing, at 606 and at a third time, power received from the electric grid at the POGI to the ESS. The method 600 also includes providing, at 608 and at a fourth time, power from received from the electric grid at the POGI to the at least one controllable load. The method 600 also includes providing, at 610 and at a fifth time, no power via the POGI and providing at least one of power from the ESS to the at least one controllable load, power from the RES to the at least one controllable load, or power from the RES to the ESS. [00114] In some implementations, the method 600 also includes providing, at the fourth time, power from at least one of the RES or the ESS to the at least one controllable load. [00115] In some implementations, the method 600 also includes providing, at the fourth time, power from the RES to at least one of the ESS or the at least one controllable load. [00116] In some implementations of the method 600, the providing at the fifth time includes providing (1) power from the ESS to the at least one controllable load, and (2) one of: power from the RES to the at least one controllable load or power from the RES to the ESS. [00117] In some implementations of the method 600, the providing at the fifth time includes providing (1) power from the RES to the at least one controllable load, and (2) one of: power from the ESS to the at least one controllable load or power from the RES to the ESS. [00118] In some implementations of the method 600, the method also includes providing, at a sixth time, power from at least one non-renewable energy source (NRES) to the at least one controllable load. [00119] In some implementations of the method 600, the providing at the fifth time includes providing (1) power from the RES to the ESS, (2) power from the RES to the at least one controllable load, and (3) power from the ESS to the at least one controllable load. [00120] FIG.7 is a flow diagram showing a third method for controlling a power generation, storage, and distribution system, in accordance with some embodiments. The method 700 of FIG. 7 may be performed using the system 100 of FIG.1, the system 200 of FIG.2, and/or the computer system 400 of FIG. 4. As shown in FIG. 7, the method 700, which may be implemented via processor-executable instructions that are stored in/on a non-transitory, processor-readable medium, includes causing, at 702, at least one of a renewable energy source (RES) or an energy storage system (ESS) to supply electric power to a controllable load without using an electric grid. The method 700 also includes, at 704, causing at least one of the RES or the ESS to supply electric power to the electric grid in response to determining that (A) electric power generated by the at least one RES exceeds a storage capacity associated with the ESS and a power demand associated with the controllable load, or (B) a grid condition associated with the electric grid exists. The method 700 also includes, at 706, causing the controllable load to one of decrease or increase a power demand associated with the controllable load when the grid condition exists without the electric power generated by the at least one RES exceeding the local storage capacity and the local power demand. Alternatively or in addition, in some implementations (not shown), the method 700 can include increasing a power demand at the at least one controllable load in response to detecting / determining that the ESS has reached a storage limit and/or in response to a prediction that the ESS will reach a storage limit at a future time (e.g., at a time when the ESS is predicted to next reach a storage limit without providing energy to the at least one controllable load), such that excess energy can be utilized by the at least one controllable load (e.g., to perform pre-cooling for a data center). [00121] In some implementations of the method 700, the controllable load includes a data center. Alternatively or in addition, the controllable load can include one or more of: an artificial intelligence (AI) training center, a cryptocurrency miner, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a smelter, water treatment plant (including desalination and purification), an industrial process heater, or a thermal battery. [00122] In some implementations of the method 700, a net load profile of the at least one controllable load is not substantially correlated with a net load profile of the electric grid during times when the electric grid is above a high threshold of net load or below a low threshold of net load. [00123] In some implementations of the method 700, a net load profile of the at least one controllable load is substantially correlated inversely with a net load profile of the electric grid during times when the electric grid is above a high threshold of net load or below a low threshold of net load. [00124] In some implementations of the method 700, a peak(s) of a load profile of the at least one controllable load does not correlate with, does not coincide with, or does not overlap with a peak(s) of a load profile of at least one additional load electrically coupled to the electric grid. [00125] In some implementations of the method 700, the instructions to cause the at least one of the RES or the ESS to supply electric power to the controllable load include instructions to supply electric power to the controllable load concurrently with the causing of the at least one of the RES or the ESS to supply power to the electric grid. [00126] In some implementations, the non-transitory, processor-readable medium also stores instructions that, when executed by the processor, cause the processor to cause delivery of power from the electric grid to the controllable load. [00127] In some implementations, the non-transitory, processor-readable medium also stores instructions that, when executed by the processor, cause the processor to balance an energy distribution associated with a plurality of controllable loads that includes the controllable load. [00128] In some implementations, the non-transitory, processor-readable medium also stores instructions that, when executed by the processor, cause the processor to switch between or concurrently operate (1) a first mode in which the at least one of the RES or the ESS operates as a peaker plant or baseload for the electric grid, and (2) at least one further mode in which the at least one of the RES or the ESS operates as a baseload or peaker plant for the controllable load. [00129] FIG.8 is a flow diagram showing a fourth method for controlling a power generation, storage, and distribution system, in accordance with some embodiments. In some such implementations, a system includes at least one renewable energy source (RES) configured to electrically couple to a grid interconnection point of an electric grid, at least one energy storage system (ESS) that is electrically coupled to the grid interconnection point and the at least one RES, at least one non-renewable energy source (NRES), and a controller that is communicatively coupled with at least one controllable load, the at least one ESS, the at least one NRES, and the at least one RES. The controller is configured to perform the method 800 of FIG.8, which includes providing, at 802, a first instruction to at least one of the at least one RES, the at least one NRES, or the at least one ESS to provide a first portion of electric power to the at least one controllable load up to an aggregated power demand. The method 800 of FIG. 8 also includes providing, at 804, a second instruction to at least one of the at least one RES, the at least one NRES, or the at least one ESS to provide a second portion of electric power to the electric grid. The method 800 of FIG.8 also includes, at 806 and in response to determining that a grid condition exists, providing a third instruction to the at least one controllable load to change (e.g., increase or decrease) a power demand at the at least one controllable load. [00130] In some embodiments of the present disclosure, a correlation of a net load profile of at least one controllable load with a net load profile of the electric grid is less than about 0.1, or between about 0.1 and 0.2, or between about 0.05 and about 0.5, or between about 0.2 and about 0.5, or between about 0.2 and about 0.3, or between about 0.3 and about 0.5. The foregoing correlation values can be associated, for example, with a predefined time period or interval. The predefined time period or interval can be on the order of (e.g., having a timescale of) minutes (e.g., one minute, 5 minutes, 20 minutes, etc.), hours (e.g., 1 hour, 2 hours, between about 2 hours and about 10 hours, between about 4 hours and about 6 hours, etc.), days (e.g., 1 day, 2 days, between about 3 days and about 5 days, etc.), weeks, months, seasons (e.g., summer, winter, fall, spring), or years. [00131] FIG.9A is a first example set of plots comparing net load profiles (net load versus time) for a controllable load (CL) and for an electric grid, e.g., for a common time period, in accordance with some embodiments. The plots of FIG.9A are not drawn “to scale” (e.g., the magnitude of the CL’s net load, in practice, would typically be far lower than the magnitude of the electric grid’s net load), but instead are scaled for readability. The plots of FIG.9A can represent, for example, net load profiles for a CL that includes a vertical farm, desalination plant, or AI training facility. As can be observed in the lefthand plot (i) of FIG.9A, a peak net load value of the electric grid (e.g., when demand for electricity from the grid is high and thus the associated cost of receiving power from the electric grid has reached a local or global maximum, for example at 5pm PT) is aligned, timewise, with a relatively high level of demand for power for the vertical farm, desalination plant, or AI training facility. The righthand plot (ii) of FIG.9A shows a remediated / optimized net load profile for the CL and the electric grid, e.g., as implemented by a system such as system 100 of FIG.1 and/or using one or more methods described herein, where the peak net load value of the electric grid now substantially overlaps with or coincides with a minimum net load value for the CL during the observed time period. Additionally, the peak net load value of the CL has shifted towards a lower-valued portion of the electric grid’s net load profile (e.g., where demand for electricity from the grid is low and thus the associated cost of receiving power from the electric grid trends toward a local or global minimum, for example at midnight). In other words, the correlation between the net load profile of the electric grid and the net load profile of the CL has been modified such that there is a substantially inverse correlation between the net load profile of the electric grid and the net load profile of the CL. Where the net load of the electric grid is relatively high, the net load of the CL is relatively low, and where the net load of the electric grid is relatively low, the net load of the CL is relatively high. Similarly, where the net load of the electric grid is increasing, the net load of the CL is decreasing, and where the net load of the electric grid is decreasing, the net load of the CL is increasing. [00132] FIG.9B is a second example set of plots comparing net load profiles (net load versus time) for a CL and for an electric grid, e.g., for a common time period, in accordance with some embodiments. The plots of FIG.9B are not drawn “to scale” (e.g., the magnitude of the CL’s net load, in practice, would typically be far lower than the magnitude of the electric grid’s net load), but instead are scaled for readability. The plots of FIG. 9B can represent, for example, net load profiles for a CL that includes a data center (e.g., an AI training facility and/or cryptocurrency miner). As can be observed in the lefthand plot (i) of FIG.9B, a peak net load value of the electric grid (e.g., when demand for electricity from the grid is high and thus the associated cost of receiving power from the electric grid has reached a local or global maximum, for example at 5pm PT) is substantially aligned, timewise, with a relatively high level of operation of the data center, and an area under the curve (AUC) of the CL net load profile that overlaps with an AUC of the electric grid’s net load profile has a first value. The righthand plot (ii) of FIG. 9B shows a remediated / improved net load profile for the CL and the electric grid, e.g., as implemented by a system such as system 100 of FIG.1 and/or using one or more methods described herein, where the peak net load value of the electric grid's net load profile is now substantially aligned, timewise, with a trough / low value of the CL net load profile, and the AUC of the CL net load profile that overlaps with the AUC of the electric grid’s net load profile has a second value that is less than the first value. Moreover, the righthand plot (ii) of FIG.9B can represent a pre-charging of one or more batteries of the CL and/or a pre-cooling of the CL during a period of time when it is more favorable to do so (e.g., from the standpoint of price and/or demand for energy from the electric grid), followed by a ramp down and/or idling of the operation of the CL during the subsequent period of time when it would be less favorable to operate or to power the CL using the electric grid. [00133] FIG.9C is a third example set of plots comparing net load profiles for a controllable load and an electric grid, in accordance with some embodiments. The plots of FIG. 9C are not drawn “to scale” (e.g., the magnitude of the CL’s net load, in practice, would typically be far lower than the magnitude of the electric grid’s net load), but instead are scaled for readability. As can be observed in the lefthand plot (i) of FIG.9C, a peak net load value of the electric grid (e.g., when demand for electricity from the grid is high and thus the associated cost of receiving power from the electric grid has reached a local or global maximum, for example at 5pm PT) is substantially aligned, timewise, with a relatively high level of operation of the CL. The righthand plot (ii) of FIG.9C shows a remediated / improved net load profile for the CL and the electric grid, e.g., as implemented by a system such as system 100 of FIG. 1 and/or using one or more methods described herein, where the peak net load value of the electric grid's net load profile is still substantially aligned with a locally high level of operation of the CL, however the upper range of the magnitude of the net load of the CL has been significantly reduced to reduce the impact of consuming power from the electric grid during peak net load / peak pricing periods. [00134] In some implementations, the electric grid can have a very low net load over a given period / interval of time. In some electric grid markets, this can lead to very low or negative energy prices as the grid is approaching a dangerous regime of overgeneration leading to over voltage conditions, and is potentially risking failure of the electric grid. Under such circumstances, the controllable load’s net load profile may be controlled to be negatively (e.g., inversely) correlated with the net load profile of the electric grid, such that the CL is directed / instructed to increase its load as much as possible to help stabilize the electric grid (and, optionally, to take advantage of the low or negative prices). Networked Energy Generation, Storage, and Distribution [00135] Some aspects of the present disclosure include a process including: receiving power data from a power data source; generating, using an energy generation, storage, and distribution predictive algorithm and based on the power data, an anticipated power supply, energy storage state of charge, and demand profile; determining, based on the anticipated power supply, energy storage state of charge and demand profile, whether a condition exists to issue a control instruction to one or more controllable power components; and providing, in response to determining that the condition exists, the control instruction associated with the condition to the one or more controllable power components. [00136] Some aspects of the present disclosure include a tangible, non-transitory, machine- readable medium storing instructions that when executed by a data processing apparatus cause the data processing apparatus to perform operations including the above-mentioned process. [00137] Some aspects of the present disclosure include a transportation information exchange service platform, including: one or more processors; and memory storing instructions that when executed by the processors cause the processors to effectuate operations of the above-mentioned process. [00138] As discussed above, renewable energy power plants (REPPs) often have inconsistent or intermittent power outputs due to the nature of renewable energy generation. Solar power plants receive variable amounts of sunlight based on the time of day, seasonal cycles and weather patterns. Wind power plants receive variable amounts of wind based on weather patterns and a variety of other factors. Intermittent power delivery, however, is incompatible with loads or grid systems that balance load and production on a real time basis. [00139] To help alleviate some of the inefficiencies and load balancing in grid systems, some embodiments of the present techniques may be used in conjunction with the techniques described in in U.S. Patent No.11,611,217, filed May 12, 2022, titled “Networked Power Plants”, the entire contents of which is hereby incorporated by reference in its entirety. Embodiments of the present disclosure allow an operator of networked REPPs to deliver power with greater reliability. Combining the outputs of REPPs whose outputs are not entirely correlated results in a combined output with a variability and intermittency lower than the variabilities and intermittencies of the outputs of the individual REPPs. This means that power can be delivered more consistently by networked REPPs than by individual REPPs. Additionally, some load operators may want to use only renewable energy but may require or desire consistent power delivery. These load operators may want to receive power over a grid and only use renewable energy. These load operators may correlate their power usage with renewable power production in order to only use renewable energy. These load operators may send a power delivery profile to a renewable energy source representing a request for amounts of renewable power production at different times. If the power delivery profile of a load is satisfied, the load operator can claim to only use renewable energy for the load. The REPP output allocated to a load may be thought of as an overlay on top of the rest of the power delivered on the grid because it is considered to be produced at the REPP and delivered to the load, ignoring the inevitable commingling of power on the grid from different sources. The output is effectively produced at the REPP and delivered to the load, despite the inevitable commingling of power on the grid from different sources. [00140] To consistently satisfy the power delivery profile, consistent power delivery is required or desired. Individual REPPs may struggle to provide consistent power delivery. This means that some loads may have to use some power from non-renewable power sources or the REPP may have to have a power capacity greatly exceeding the power delivery profile of a load in order to consistently satisfy the load's power delivery profile despite fluctuations in power generation. Networked REPPs may be able to provide more-consistent power that comes entirely from renewable power sources. Additionally, and/or alternatively, each REPP may have a power capacity lower than what would be needed or used for a single REPP to provide consistent power. Each REPP having a lower power capacity than what a single, un-networked REPP would need to provide consistent power results in increases in efficiency and lower costs for constructing REPPs due to each REPP needing less excess capacity which would usually not be fully utilized. Networked REPPs may also produce power in excess of what is required or used by various loads. This excess power may be treated as a virtual REPP, or virtual power plant that can deliver power to additional loads. [00141] The outputs of networked REPPs and virtual power plants may be delivered over the grid and allocated to various loads. This allocated combined output of networked REPPs may be thought of as an overlay on top of the rest of the power delivered on the grid because it is effectively produced at the networked REPPs and delivered to the various loads to which it is allocated, ignoring the inevitable commingling of power on the grid from different sources. This overlay may be treated as a green grid, utilizing the existing infrastructure of the grid, but delivering renewable power from REPPs to the various loads. The green grid may function similar to the grid on which it operates, with a market for renewable power distinct from a market for non-green power. The green grid may be owned and operated by one entity, or it may include REPPs owned and operated by a variety of entities. [00142] Some embodiments of the present techniques may be used in conjunction with the techniques described in in U.S. Patent No.11,611,217 to take those techniques a step further and introduced controllable power components including controllable loads (e.g., uncorrelated loads or correlated loads). Loads may be introduced to the system that are behind-the-meter (e.g., are directly connected to the REPPs and not connected to the grid system) or loads that are on the grid system but are controllable by the networked power plants. In some embodiments, these loads on the grid system may be uncorrelated with a typical energy consumption profile experienced by the grid for a given day or other time period (e.g., a vertical farming operation, training AI models (and other latency insensitive compute workloads), aluminum smelting, direct carbon capture from air, hydrogen production with electrolyzers by electrolyzing water, or other loads that would be apparent to one of skill in the art in possession of the present disclosure). Controllable loads may include correlated or known loads as well where certain contractual arrangements can be met. For example, an office building that may see peak power demand during a hot summer day when air conditioners are operating to cool the office space may be an example of a correlated but controllable load. These loads may be controllable even though they generally correlate with the rest of the grid’s energy usage. As such, these loads may be controllable to operate at different times of the day than the peak time. For example, the office building may use the HVAC system to precool or preheat a building in anticipation of having a reduction in power consumption during peak energy demand times. [00143] In various embodiments, the behind-the-meter loads and other controllable loads on the grid system may be in communication with a networked energy generation, storage and distribution controller. Behind the meter controllable loads allow an energy producer to increase size and performance of the REPP. For example, the REPP may be built to generate a larger capacity than what the REPP can provide to the grid. This provides economy of scale cost and performance advantages than without the controllable loads. When generation is not at peak (e.g., clouds, early morning, late afternoon, or low wind, etc.) or when the energy storage system included with the REPP is full, the excess energy is absorbed by the behind-the-meter load. Also, when REPP generation is low the oversized system can deliver more power to more critical or valuable loads on the grid and fulfill the bandwidth of what the REPP can provide to the grid or provide more load to the energy storage system. As such, the REPP may be designed for better performance and lower cost, i.e., overall system performance is better such that a more consistent energy supply is provided to the grid. [00144] The networked energy generation, storage and distribution controller that is used for the networked power plants may include predictive algorithms for balancing energy distribution to the controllable loads. For example, the networked energy generation and distribution controller may ingest data from various data sources (e.g., a weather forecast, an event schedule, a calendar, historical energy use data, sensor data or other data sources that would be apparent to one of skill in the art in possession of the present disclosure). In other embodiments, the data sources may include state of charge data or analytics of other REPPs that are not on the network such that a prediction of how much energy storage another REPP not on the network may have to anticipate how much energy will be available for the grid. The networked energy generation and distribution controller, using the machine learning algorithms trained on similar data, may then anticipate energy demand for uncontrollable loads on the grid as well as an energy supply on the networked power plants. Based on the anticipated energy demand and the energy supply, the networked energy generation and distribution controller may determine whether one or more energy balancing conditions associated with a respective controllable load are satisfied to either increase power distribution to that controllable load or reduce power distribution to that controllable load. For example, in exchange for a better rate on its energy price or some other energy distribution factor that the controllable load desires, the controllable load may allow the networked energy generation and distribution controller to reduce energy consumption at that controllable load to reallocate the networked power plant’s energy supply to loads that are not controllable and that may pay a higher premium, are higher prioritized based on various factors (e.g., more necessary / high priority infrastructure such as a hospital, a water distribution plant, or the like). Similarly, the controllable load may include an energy storage system where the networked energy generation, storage, and distribution controller may increase or decrease power distribution to the energy storage device. Furthermore, more optimal decisions can be made of which energy storage device in an energy storage system to store energy. For example, a zinc air battery may be charged when cheap power is available while a lithium-ion battery may be charged when more expensive power is available. As such a type of storage among other factors associated with the energy storage device may be used to determine when a particular energy storage device is to be charged and how much charge a particular energy storage device is to receive. [00145] In other embodiments, the networked energy generation and distribution controller may also use the anticipated energy demand and the energy supply to balance the storage of energy generated by the REPPs on associated batteries. For example, the networked energy generation and distribution controller may determine the amount of energy stored on each battery and how those batteries in the networked power plants are going to distribute the energy in an optimized manner. For example, to preserve the life expectancy of a battery, under normal conditions, that battery may not be completely filled or completely drained as doing so decreases the life expectancy of the battery. However, if the anticipated energy supply and demand indicate a condition where it is more beneficial to fully charge a battery or fully discharge a battery than to consider the life expectancy of the battery, the networked energy generation and distribution controller may cause the battery to be fully charged in anticipation of the future event. For example, if there is an anticipated event that requires or involves a high demand of energy, the networked energy generation and distribution controller may fully charge the battery. In other embodiments, the networked energy generation and distribution controller may tier the batteries such that a first battery distributes energy based on a first condition, a second battery distributes energy based on a second condition, and a third battery distributes energy based on a third condition. These the conditions may be prioritized based on different levels. For example, the third battery may only distribute energy if the price of energy is above a certain threshold. [00146] In yet other embodiments of the present disclosure, the networked energy generation and distribution controller may determine when to provide energy storage to power plants that are not included in the networked power plants such as power plants that are on the grid. The networked energy generation and distribution controller may determine conditions where the out- of-network power plant may store energy on the networked power plant’s batteries or other energy storage systems. Using the anticipated energy demand and energy storage determinations made by the machine learning algorithms of the networked energy generation and distribution controller, the networked energy generation and distribution controller may determine when to purchase power from power plants on the grid or provide storage for contracted out-of-network power plants. The networked energy generation and distribution controller may communicate with an application located at the out-of-network power plant similarly to an application provided at the controllable loads and storage of the networked power plants. As such, the systems and methods of the present disclosure provide more optimal and consistent energy generation, storage, and distribution of energy generated by REPPs. [00147] FIG.10 illustrates an example networked energy generation, storage, and distribution system 1000 in accordance with one or more embodiments. The networked energy generation, storage, and distribution system 1000 may include a networked energy generation, storage, and distribution controller 1002; a network 1004; a networked power plant system 1006 that includes a power plant 1006a, a power plant 1006b, energy storage 1007a, energy storage 1007b, a load 1008a, and a load 1008b; a grid 1010; one or more data sources 1011, a power plant 1012; a load 1014a; a load 1014b; and a controllable load 1014c. While described as networked, in some embodiments, the power plant system 1006 may only include one power plant. In some such implementations, the networked energy generation, storage, and distribution system 1000 may not be networked and may instead be an energy generation, storage, and distribution system. As well, in some such implementations, the networked power plan system 1006 may not be networked and may instead be a power plan system. The load 1014a, the load 1014b, and the controllable load 1014c may be electrically coupled to the grid 1010. The load 1014a, the load 1014b, and the controllable load 1014c may be remote from each other and have separate power requirements. The load 1014a may have a first power delivery profile which details power requirements for the load 1014a at different times. The load 1014b may have a second power delivery profile which details power requirements for the load 1014b at different times. The controllable load 1014c may have a third power delivery profile which details power requirements for the controllable load 1014c at different times. In some embodiments, the grid 1010 may be a utility grid owned and operated by a single utility or system operator. In other embodiments, the grid 1010 may be a plurality of electrical connections allowing for the transmission of power from the power plant 1006a, the power plant 1006b, and the power plant 1012 to the load 1014a, the load 1014b, and the controllable load 1014c. [00148] The power plant 1006a may be a first renewable energy power plant (REPP). The power plant 1006b may be a second REPP and the power plant 1012 may be a third REPP or other power plant. Examples of REPPs include, but are not limited to, solar plants, wind plants, geothermal plants, and biomass plants. REPPs may include energy storage systems (ESSs) 1007a or 1007b. An example of an ESS is a battery. A battery-based ESS may be called a battery ESS or BESS. The power plant 1006a may have a first power output that varies over time. The power plant 1006b may have a second power output that varies over time. The power plant 1012 may have a second power output that varies over time. The first power output and the second power output may vary differently such that they are not tightly correlated. For example, the power plant 1006a may be geographically remote from the power plant 1006b such that weather patterns at the power plant 1006a differ from weather patterns at the power plant 1006b. Thus, variation in the first power output will not be tightly correlated with variation in the second power output. The less correlated the output of the power plant 1006a with the output of the power plant 1006b, the greater the effects of networking. The less correlated the outputs of the power plant 1006a and the power plant 1006b, the less variation will be present in the combined output of the power plant 1006a and the power plant 1006b. Less variation in the combined output may result in more reliability in satisfying the power delivery profiles of the loads 1014a and 1014b. Less variation in the combined output may result in lower capacity requirements for the power plant 1006a and the power plant 1006b. [00149] In some embodiments, the power plant 1006a may be directly connected to the load 1008a or other directly connected loads such that the load 1008a is behind-the-meter or otherwise not connected to the grid 1010. Similarly, the power plant 1006b may be directly connected to the load 1008b. Load 1008a or load 1008b may be controllable loads and in some cases may be uncorrelated loads. In some embodiments, the power plant 1012 may be connected to the networked power plant system 1006 via the grid 1008 and may provide energy to the ESS of the power plants 1006a or 1006b. [00150] The power plant 1006a and the power plant 1006b may communicate with the networked energy generation, storage, and distribution controller 1002 via a network 1004. Similarly, the controllable loads 1008a, 1008b, and 1014 and the power plant 1012 may communicate with the networked energy generation, storage, and distribution controller 1002 via a network 1004. Further still, the networked energy generation, storage, and distribution controller 1002 may communicate with data sources 1011 via the network 1004. The data sources may include sensors, weather data, local schedules, or any other system data or third-party information that would be apparent to one of skill in the art in possession of the present disclosure. The network 1004 may be any local area network (LAN) or wide area network (WAN). In some embodiments, the network is the internet. In other embodiments, the network is a private communications network. The networked energy generation, storage, and distribution controller 1002 may include a processor and a memory. [00151] The networked energy generation, storage, and distribution controller 1002 may control the power plant 1006a and the power plant 1006b. The networked energy generation, storage, and distribution controller 1002 may coordinate the first power output of the power plant 1006a and the second power output of the power plant 1006b in order to deliver power to the load 1014a, the load 1014b, and the controllable loads 1008a, 1008b, and 1014c. The networked energy generation, storage, and distribution controller 1002 may receive the first power delivery profile of the load 1014a and the second power delivery profile of the load 1014b, and the respective power delivery profile of the controllable loads 1008a, 1008b, and 1014c. In some embodiments, the networked energy generation, storage, and distribution controller 1002 receives the first power delivery profile of the load 1014a, the second power delivery profile of the load 1014b, and the respective power delivery profile of the controllable loads 1008a, 1008b, and 1014c via the network 1004. In other embodiments, the networked energy generation, storage, and distribution controller 1002 receives the first power delivery profile of the load 1014a, the second power delivery profile of the load 1014b, and the respective power delivery profile of the controllable loads 1008a, 1008b, and 1014c from another source. The networked energy generation, storage, and distribution controller 1002 may direct the power plant 1006a to direct power to the load 1014a, the load 1014b, or any of the controllable load 1008a, 1008b, or 1014c. The networked energy generation, storage, and distribution controller 1002 may direct the power plant 1006b to direct power to the load 1014a, the load 1014b, or any of the controllable load 1008a, 1008b, or 1014c. The networked energy generation, storage, and distribution controller 1002 may direct the power plant 1006a to direct a first portion of its power output to the load 1014a, a second portion of its power output to the load 1014b, or other portions of its power output to any of the controllable load 1008a, 1008b, or 1014c. The networked energy generation, storage, and distribution controller 1002 may direct the power plant 1006b to direct a first portion of its power output to the load 1014a, a second portion of its power output to the load 1014b, or other portions of its power output to any of the controllable load 1008a, 1008b, or 1014c. In some embodiments, directing power from a power plant to a load is accomplished by sending power from the power plant to the grid and communicating to the load how much power was sent to the grid. The load draws power from the grid equal to how much power the power plant sent to the grid. The load may match its energy consumption in a time window to the energy sent from the power plant to the grid in the time window. The time window may be a year, a month, a day, an hour, a minute, or any other unit of time. Where power is directed to the load from multiple power plants, the load may match its power consumption in a time window to the total power sent by the multiple power plants in the time window. Where the load needs to consume more energy than the total energy sent by the multiple power plants in the time window, the load operator may draw energy from other sources (which may not be renewable) and keep a record of the portion of energy consumed from the multiple power plants and from the other sources respectively, as input to an algorithm that will adjust its future requests for energy from the multiple power plants. [00152] In some embodiments, one or more functionalities of the system 1000 of FIG.10 can be combined with or replaced by one or more functionalities of any of system 100 of FIG. 1, system 400 of FIG.4, system 1300 of FIGs.13-17, and/or system 2000 of FIG.20. Alternatively or in addition, one or more functionalities of the controller 1002 of FIG.10 can be implemented using one or more functionalities / features of any of controller 200 of FIG.2, controller 1102 of FIG.11, controller 1802 of FIG.18, and/or controller 2102 of FIG.21. Alternatively or in addition, the system 1000 of FIG.10 can be configured to perform one or more of method 300 of FIG.3, method 500 of FIG.5, method 600 of FIG.6, method 700 of FIG.7, method 800 of FIG.8, method 1200 of FIG.12, method 1900 of FIG.19, or method 2200 of FIG.22. [00153] In other embodiments, the present disclosure provides systems and methods for serving multiple electric loads with renewable electrical power. The multiple electric loads may not be correlated. The multiple electric loads may be uncorrelated or partially correlated. For instance, the value of directing power to at least one of the electrical loads may vary over time and such variance may be at least partially independent of the value of directing power to another electrical load. The uncorrelated or partially correlated loads may also be either controllable or noncontrollable loads where the networked energy generation, storage, and distribution controller (e.g., controller 1002 in FIG.10) may control the energy consumption at that load. [00154] Methods and algorithms herein may be used for determining or optimizing the allocation of energy generated by the RES. In such cases, the energy storage system (ESS) may be taken into account as an electric load along with other electric loads and energy generated by the RES may be allocated among the electric loads and the ESS. Alternatively, the methods herein may be used to allocate power generated by the RES-ESS powerplant. In such case, the ESS is part of the RES-ESS and energy is allocated among the electric loads not including the ESS. The EMS may implement methods or algorithms to determine the delivery of power among multiple uncorrelated or partially correlated electric loads. In some embodiments, the methods and algorithms may flexibly adjust the amount of power: a) sent to/drawn from the ESS and b) sent to each of the electrical loads over time, allowing for economically valuable opportunities. This may beneficially allow for an improved power allocation among multiple not (completely) correlated loads and optimizing the total value for delivering the power to the multiple loads (e.g., electric grid, BESS, green hydrogen, crypto mining, etc.). [00155] The present disclosure provides systems, system architectures and methods that allow a REPP-ESS powerplant to serve one or more uncorrelated or partially correlated loads. The methods and systems herein can be easily scaled up and can be applied to any number of uncorrelated or partially correlated loads or can be applied to any powerplant configurations. In some cases, the uncorrelated or partially correlated loads may comprise one or more electrical grid loads, and/or one or more energy-consuming processes directly connected to the RES-ESS without passing through an electrical grid (i.e., off-grid loads). The one or more electric grid loads may include, for example, an electric grid (e.g., a network serving many individual loads) effectively serving as a single load, one or more loads connected to an electric grid and the like. In some cases, the one or more electric grid loads may comprise one or more additional, remotely-located RESs that is connected to the RES-ESS-load system via the electrical grid. [00156] FIG. 1 of U.S. Patent No. 12,119,646 (“Systems and Methods for Renewable Powerplant Serving Multiple Loads”) schematically illustrates an example system of the system 1000 of FIG.10 of the present disclosure, and is hereby incorporated by reference in its entirety for all purposes. [00157] FIG. 11 illustrates an embodiment of a networked energy generation, storage, and distribution controller 1100 that may be the networked energy generation, storage, and distribution controller 1002 discussed above with reference to FIG.10. While described as a standalone system, those skilled in the art will appreciate that the networked energy generation, storage, and distribution controller 1100 may distributed across many computing devices such as in a cloud environment. In the illustrated embodiment, the networked energy generation, storage, and distribution controller 1100 includes a chassis 1102 that houses the components of the networked energy generation, storage, and distribution controller 1100, only some of which are illustrated in FIG. 11. For example, the chassis 1102 may house a processing system (not illustrated) and a non-transitory memory system (not illustrated) that includes instructions that, when executed by the processing system, cause the processing system to provide a networked energy generation, storage, and distribution engine 1104 that is configured to perform the functions of the networked energy generation and distribution engines or the networked energy generation, storage, and distribution controller discussed below. In the specific example illustrated in FIG. 11, the networked energy generation, storage, and distribution engine 1104 may include an energy generation, storage, and distribution predictive algorithm 1105 that is configured to perform the functions of the energy generation, storage, and distribution predictive algorithms discussed herein. In various embodiments, the energy generation, storage, and distribution predictive algorithm 1105 may ingest data provided by data sources and anticipates energy demand and energy supply, or any other functionality discussed herein. In various embodiments, the energy generation, storage, and distribution predictive algorithm 1105 may include a network simulator to model behavior, which may predict the components being incorporated into the grid by running simulations due to lack of historical data. In other examples, the energy generation, storage, and distribution predictive algorithm 1105 may include model predictive control or other predictive algorithms/machine learning algorithms that would be apparent to one of skill in the art in possession of the present disclosure. [00158] The chassis 1102 may further house a communication system 1106 that is coupled to the networked energy generation, storage, and distribution engine 1104 (e.g., via a coupling between the communication system 1106 and the processing system) and that is configured to provide for communication through the communication network 1004 as detailed below. The chassis 1102 may also house a storage system 1108 that is coupled to the networked energy generation, storage, and distribution engine 1104 through the processing system and that is configured to store the rules or other data utilized by the networked energy generation, storage, and distribution engine 1104 to provide the functionality discussed below. While a networked energy generation, storage, and distribution controller 1100 has been illustrated, one of skill in the art in possession of the present disclosure will recognize that other networked energy generation and distribution controller (or other devices operating according to the teachings of the present disclosure in a manner similar to that described below for the networked energy generation, storage, and distribution controller 1100) may include a variety of components and/or component configurations for providing known computing device functionality, as well as the functionality discussed below, while remaining within the scope of the present disclosure as well. [00159] FIG.12 depicts an embodiment of a method 1200 of networked energy generation and distribution with controllable loads, which in some embodiments may be implemented with at least some of the components of FIGs. 10 and 11 discussed above. As discussed below, some embodiments make technological improvements to REPPs. The method 1200 is described as being performed by the networked energy generation, storage, and distribution engine 1104 included on the networked energy generation, storage, and distribution controller 1002/1100. Furthermore, it is contemplated that other computer systems in the networked energy generation, storage, and distribution system 1000 may include some or all the functionality of the networked energy generation, storage, and distribution engine 1104. As such, some or all of the steps of the method 1200 may be performed by other actors in the networked energy generation, storage, and distribution system 1000 and still fall under the scope of the present disclosure. Furthermore, and as mentioned above, the networked energy generation, storage, and distribution controller 1002/1100 may include one or more processors or one or more servers, and thus the method 1200 may be distributed across the those one or more processors or the one or more servers. [00160] The method 1200 begins at operation 1202 where power data is received from a power data source. In an embodiment, at operation 1202, the networked energy generation, storage, and distribution engine 1104 may receive, via the communication system 1106, a message that includes various power data from a power data source. For example, the power data may include sensor data, weather data, event or calendar data for a region, historical power data, power profiles from each load or power plant in the system, ESS health or age, or other data that would be apparent to one of skill in the art in possession of the present disclosure. [00161] The method 1200 may proceed to operation 1204 where an anticipated power supply and demand profile is determined. In an embodiment, at operation 1204, the energy generation, storage, and distribution predictive algorithm 1105 may determine an anticipated power supply and demand profile for the loads 1008a, 1008b, 1014a, 1014b, or 1014c and the power plants 1006a or 1006b. The anticipated power supply and demand profile may include predictions about the power supply of the power plants 1006a or 1006b which may further include predictions about the ESS included in each of the power plants 1006a or 1006b. The anticipated power supply and demand profile may include predictions about the power demand of the controllable loads 1008a, 1008b, and 1014c and the loads 1014a and 1014b. [00162] The method 1200 may then proceed to decision operation 1206 where it is determined whether a condition exists to issue a control instruction to controllable components of the networked energy generation, storage, and distribution system 1000. In an embodiment, at operation 1206, the networked energy generation, storage, and distribution engine 1104 may determine, based on the anticipated energy supply and demand profile whether a condition exists to issue a control instruction to a controllable component. For example, the networked energy generation, storage, and distribution engine 1104 may determine that a condition exists such that the power plant 1012 that is not included in the networked power plant system 1006 may store energy via the grid 1010 to the ESS included in the power plant 1006a or 1006b. In other examples, the networked energy generation, storage, and distribution engine 1104 may determine that a condition exists such that a control instruction should be sent to one or more of the controllable loads 1008a, 1008b, or 1014c such that either power consumption is decreased or is allowed to increase and the time or times those increases and decreases are to occur. In other embodiments, the networked energy generation, storage, and distribution engine 1104 may determine that a condition exists such that a control instruction should be sent to one or more of the power plants 1006a or 1006b to control the storage and distribution of power on the included ESS. In yet other embodiments the condition may include a contractual or regulatory constraint that the networked energy generation, storage, and distribution engine 1104 checks as well. If no condition exists or no new condition exists, the networked energy generation, storage, and distribution engine 1104 may continue to monitor the data and generate anticipated energy supply and demand profiles. [00163] If the condition does exist, the method 1200 may proceed to operation 1208 where a control instruction is sent to the controllable power component. In an embodiment, at operation 1208, the networked energy generation, storage, and distribution engine 1104 may send via the network 1004 the control instruction to the controlled power component. For example, the control instruction may be sent to an application at the controllable load 1008a, 1008b, or 1014c via the network to decrease power consumption so that supply may be redirected to the loads 1014a or 1014b. In other embodiments, the control instructions may be sent to the power plant 1006a or 1006b such that the ESS stores power to particular batteries or distributes power from particular batteries to particular loads 1014a or 1014b or controllable loads 1008a, 1008b, or 1014c. In yet other embodiments, the control instruction may be sent to the power plant 1012 such that power generated by the load is “routed” to the ESS of the power plant 1006a or 1006b via the grid. While particular examples of control instructions are discussed, one of skill in the art in possession of the present disclosure would contemplate that other control instructions to the controllable power components may be contemplated for various purposes and condition in the system. [00164] In some embodiments, a method includes a controller receiving power data from a power data source. The controller generates, using an energy generation, storage, and distribution predictive algorithm and based on the power data, an anticipated power supply and demand profile and determining whether a condition exists to issue a control instruction to one or more controllable power components. In response to the condition existing, the controller provides a control instruction to the one or more controllable power components. [00165] In some embodiments, a non-transitory, processor-readable medium stores instructions that, when executed by a processor, cause the processor to set a power output for a first renewable energy power plant (REPP) and a power output for a second REPP based on a power delivery profile for a first load, a power delivery profile for a second load, a power output capability of the first REPP, and a power output capability of the second REPP. The non-transitory, processor- readable medium also stores instructions to cause the processor to allocate a combined power output of the first REPP and the second REPP to the first load and the second load for a predefined time window, the allocation based at least in part on an anticipated power supply-and-demand profile generated using an energy generation, storage, and distribution predictive algorithm. The non-transitory, processor-readable medium also stores instructions to cause the processor to cause transmission of a first signal representing a first portion of the combined output for the predefined time window and the first load, and a second signal representing a second portion of the combined output for the predefined time window and the second load. The non-transitory, processor-readable medium also stores instructions to cause the processor to cause delivery of the allocated combined power output to an electric grid, the first load receiving a different amount of power from the electric grid during the predefined time window than indicated in the first signal. The non- transitory, processor-readable medium also stores instructions to cause the processor to cause storage of a difference between the first portion of the combined output and a total amount of power received from the electric grid during the predefined time window. [00166] In some implementations, the non-transitory, processor-readable medium also stores instructions to cause the processor to determine, based on the anticipated power supply-and- demand profile, whether a condition exists to issue a control instruction to one or more controllable power components, and to provide, in response to determining that the condition exists, the control instruction associated with the condition to the one or more controllable power components. In some such implementations, the condition can be associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. Alternatively or in addition, the system can have an associated capacity factor of at least about 60%, and the condition can be associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00167] In some implementations, the anticipated power supply-and-demand profile includes data associated with at least one uncontrollable load. [00168] In some implementations, at least one of the first load or the second load includes a controllable load. The at least one controllable load can include at least one of a data center, an artificial intelligence (AI) training center, a cryptocurrency miner, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a water treatment plant, an industrial process heater, or a thermal battery. [00169] In some embodiments, a method includes causing, via a processor and at a first time, transmission of a signal representing an anticipated power profile, the anticipated power profile generated using an energy generation, storage, and distribution predictive algorithm. The method also includes receiving, at a system including a load and the processor, and at a second time subsequent to the first time, power from an electric grid. The method also includes comparing, via the processor and at a third time subsequent to the second time, (1) the amount of power received from the electric grid to (2) the anticipated power profile, and in response to determining that the amount of power drawn from the electric grid matches or exceeds the anticipated power profile, causing transmission of an indication that a load was satisfied using renewable power. [00170] In some embodiments, a method includes receiving, at a system including a load and a processor, power from an electric grid. The method also includes comparing, via the processor, (1) an amount of the power received from the electric grid to (2) an anticipated power profile, the anticipated power profile generated using an energy generation, storage, and distribution predictive algorithm, and in response to determining that the amount of power drawn from the electric grid matches or exceeds the anticipated power profile, causing transmission of an indication that a load was satisfied using renewable power. Smart Seasonal Electrical Resource Allocation with Controllable Loads [00171] Some aspects of the present disclosure include a process including: setting, by a controller of an renewable power plant (REPP), a first charge/discharge for a first REPP electrical storage system (ESS) and a second charge/discharge for a second REPP ESS such that the REPP delivers power to a first load for a first time longer than a first production time period when an REPP renewable energy source (RES) of the REPP produces power, wherein the first ESS is electrically coupled to the RES and to a first meter, and wherein the second ESS is electrically coupled to the RES and to the first meter through a switch; in response to a first trigger condition being satisfied, actuating the switch such that the second ESS is electrically coupled to the controllable load; setting a third charge/discharge for the first ESS such that the REPP delivers power to the first load for a third time longer than a second production time period when the RES produces power; setting a fourth charge/discharge for the second ESS such that the second ESS maintains a portion of its charge in reserve for the third load; in response to a second trigger condition being satisfied, actuating the switch such that the second ESS is electrically coupled to the second meter; and setting a fifth charge/discharge for the second ESS such that the second ESS maintains a portion of its charge in reserve for the second meter, wherein the RES is tuned to satisfy the power delivery requirements of the first load and maintain the portion of charge of the second ESS. [00172] Some aspects of the present disclosure include a tangible, non-transitory, machine- readable medium storing instructions that when executed by a data processing apparatus cause the data processing apparatus to perform operations including the above-mentioned process. [00173] Some aspects of the present disclosure include a system, including: one or more processors; and memory storing instructions that when executed by the processors cause the processors to effectuate operations of the above-mentioned process. [00174] Embodiments of the present disclosure solve the technical problem of allocating specific energy storage resources (ESSs) to specific loads or specific uses. To help alleviate some of the inefficiencies allocating specific ESSs to specific loads or specific uses, some embodiments of the present techniques may be used in conjunction with the techniques described in U.S. Patent No.11,621,566, filed October 5, 2022, titled “Seasonal Electrical Resource Allocation”, the entire contents of which is hereby incorporated by reference in its entirety. Embodiments discussed herein include using a switch to alter a connection between an ESS and a first meter such that the ESS is connected to a second meter instead of the first meter. This allows use of the ESS to be directly tied to the meter, enabling segmentation and cycling of ESS resources. Cycling ESS resources such that different ESSs are used for different purposes at different times allows for ESS use and degradation to be managed or levelized across multiple ESSs. Managing and/or levelizing ESS degradation allows for accurate predictions of ESS lifetime and performance. [00175] Furthermore, more optimal decisions may be made of which energy storage device in an ESS to store energy. For example, a lower cost with lower round trip efficiency ESS is selected (e.g., a zinc air battery), may be charged when cheap power (or even negatively priced power) is available, longer storage cycles are anticipated with lower parasitic loads making them more efficient in totality, while a high-efficiency but more costly ESS (e.g., a lithium-ion battery) may be charged when more expensive power is available, faster response times are anticipated, higher energy storage efficiencies are beneficial, or when other beneficial conditions are present that would be apparent to one of skill in the art in possession of the present disclosure. As such, a type of energy storage device or other factors associated with the energy storage device may be used to determine when a particular energy storage device is to be charged or how much charge a particular energy storage device is to receive. [00176] Additionally, the embodiments discussed herein solve the technical problem of ESS underutilization. By altering connections and uses of ESSs, ESS storage capacity may be directed away from uses where it is underutilized and towards uses where it is more fully utilized. For example, an ESS that is connected to a load for time-shifting renewable energy source (RES) output may be underutilized if the RES output falls below a threshold. Actuating a switch to alter a use of the ESS offers the technical advantage of putting unused ESS capacity to use in a distinct, measurable use case. Time-shifting RES output using an ESS is discussed in U.S. Patent No. 11,451,060 (“Consistent Power Delivery via Power Delivery Limits”), which is incorporated by reference in its entirety herein, for all purposes. [00177] FIG.13 is a block diagram of an example renewable energy power plant (REPP) 1300, according to one or more embodiments. The REPP 1300 may include a renewable energy source (RES) 1335, an RES inverter 1340, a first energy storage system ESS 1310, a first ESS inverter 1315, a first meter 1320, a second ESS 1365, a second ESS inverter 1360, a switch 1345, a second meter 1350, and an energy management system (EMS) 1305. The RES 1335 may be a solar power source, a wind power source, a geothermal power source, or any other source of renewable or non- renewable energy generation with non-renewable resources that would be apparent to one of skill in the art in possession of the present disclosure. The RES may be electrically connected to the RES inverter 1340. The RES inverter 1340 may convert DC power from the RES 1335 to AC power. The RES inverter 1340 may be connected to the first meter 1320. The first energy storage system (ESS) inverter 1315 may be connected to the RES inverter 1340 and the first meter 1320. The first ESS 1310 may be connected to the first ESS inverter 1315. The first ESS 1310 may be configured to receive power from the RES 1335 and provide power to the first meter 1320. The first ESS 1310 may be charged from the RES 1335 and may discharge to provide power to the first meter 1320. The first ESS inverter 1315 may be a bidirectional inverter. The first ESS inverter 1315 may convert AC power from the RES inverter 1340 to DC power to charge the first ESS 1310 and convert DC power from the first ESS 1310 to AC power to provide power to the first meter 1320. The REPP 1300 may be connected to a first load 1330, a second load 1355, or a controllable load 1375 through a grid 1325. While three loads are illustrated by example, one of skill in the art will recognize that hundreds, thousands, tens of thousands, or any number of loads (controllable or uncontrollable) may be coupled to the grid 1325. In some embodiments, the grid 1325 may be a utility grid. The first meter 1320 may be associated with the first load 1330. The first meter 1320 may measure an amount of power delivered by the REPP 1300 to the first load 1330 or other loads through the grid 1325. [00178] The RES inverter 1340 may be connected to the switch 1345. The second ESS inverter 1360 may be connected to the RES inverter 1340 and the switch 1345. The second ESS 1365 may be connected to the second ESS inverter 1360. The second ESS 1365 may be configured to receive power from the RES 1335 and provide power to the switch 1345. The second ESS 1365 may be charged from the RES 1335 and may discharge to provide power to the switch 1345. The second ESS inverter 1315 may be a bidirectional inverter. The second ESS inverter 1315 may convert AC power from the RES inverter 1340 to DC power to charge the second ESS 1365 and convert DC power from the second ESS 1365 to AC power to provide power to the switch 1345. The second meter 1350 may be associated with the second load 1355. The second meter 1350 may measure an amount of power delivered by the REPP 1300 to the second load 1355 or other loads. The switch 1345 may be configured to connect the second ESS inverter 1360 to the second meter 1350. The switch 1345 may be configured to connect the second ESS inverter 1360 to a fourth load 1370. The fourth load 1370 may be a behind-the-meter load that does not receive power via the grid but directly from the REPP 1300 and may be a controllable load. The switch 1345 may be configured to connect the second ESS inverter 1360 to the first meter 1320. [00179] The REPP 1300 may include controllable power components including controllable loads (e.g., loads 1370 and 1375) that may be uncorrelated loads or correlated loads such that those loads are correlated or uncorrelated with other loads generally defining an energy consumption profile of the grid 1325. Loads may be introduced to the system that are behind-the-meter (e.g., are directly connected to the REPPs 1300 and not connected to the grid 1325) or loads that are on the grid 1325 but are controllable by the EMS controller 1305. In some embodiments, these loads on the grid 1325 or behind-the-meter may be uncorrelated with a typical energy consumption profile experienced by the grid for a given day or other time period (e.g., a vertical farming operation, training AI models (and other latency insensitive compute workloads), data centers, aluminum smelting, direct carbon capture from air, hydrogen production with electrolyzers by electrolyzing water, or other loads that would be apparent to one of skill in the art in possession of the present disclosure). A correlated load may be a load that provides a typical energy consumption profile. Controllable loads may include correlated or known loads as well where certain contractual arrangements can be met by virtually integrating them into the network. For example, an office building that may see peak power demand during a hot summer day when air conditioners are operating to cool the office space may be an example of a correlated but controllable load. These loads may be controllable even though they generally correlate with the rest of the grid’s energy usage. As such, these loads may be controllable to operate at different times of the day than the peak time. For example, the office building may use the HVAC system to precool or preheat a building in anticipation of having a reduction in power consumption during peak energy demand times. [00180] In various embodiments, the behind-the-meter loads (e.g., the load 1370) and other controllable loads on the grid 1325 may be in communication with the EMS controller 1305. Behind-the-meter controllable loads allow an energy producer to increase size and performance of the REPP 1300. For example, the REPP 1300 may be built to generate a larger capacity than what the REPP 1300 can provide to the grid. This provides economy of scale cost and performance advantages over a system without the behind-the-meter controllable loads. When generation is not at peak (e.g. clouds or early morning or late afternoon or low wind etc.) or when the ESS 1310 or 1365 included with the REPP 1300 is full, the excess energy is absorbed by the behind-the-meter load 1370. Also, when REPP generation is low, the oversized system can deliver more power to more critical or valuable loads on the grid and fulfill the bandwidth of what the REPP 1300 can provide to the grid 1325 or provide more power to the ESSs 1310 and 1365. As such, the REPP 1300 may be designed for better performance and lower cost, i.e., overall system performance is better such that a more consistent energy supply, capacity, or other ancillary services are provided to the grid. [00181] The EMS 1305 may be configured to gather data via a network 104 from the first meter 1320, the first ESS 1310, the first ESS inverter 1315, the RES inverter 1340, the second ESS inverter 1360, the second ESS 1365, the switch 1345, the second meter 1350, the controllable load 1375, and the load 1370. The EMS 1305 may be configured to control the first ESS inverter 1315, the RES inverter 1340, and the second ESS inverter 1360 by adjusting inverter setpoints. The EMS 1305 may control various components through the network 1304. The EMS 1305 may control the RES inverter 1340 to adjust an RES output. The EMS 1305 may control the first ESS inverter 1315 to control a charge/discharge of the first ESS 1310 and to permit energy to flow directly from the RES inverter 1340 to the first meter 1320. The EMS 1305 may control the second ESS inverter 1360 to control a charge/discharge of the second ESS 1365 and to permit energy to flow directly from the RES inverter 1340 to any load connected through the switch 1345. The EMS 1305 may be configured to control the switch 1345 to selectively connect the second ESS inverter 1360 to the first meter 1320, the second meter 1350, or the third load 1370. The EMS 1305 may control the power usage of the third load 1370 or the controllable load 1375 by either increasing or decreasing the loads. Further still, the EMS controller 1305 may communicate with data sources 1311 via the network 1304. The data sources may include sensors, weather data, local schedules, or any other system data or third-party information that would be apparent to one of skill in the art in possession of the present disclosure. The network 1304 may be any local area network (LAN) or wide area network (WAN). In some embodiments, the network is the internet. In other embodiments, the network is a private communications network. [00182] The EMS controller 1305 may include predictive algorithms for balancing energy distribution to the controllable loads 1370 or 1375. For example, the EMS controller 1305 may ingest data from various data sources 1311 (e.g., a weather forecast, an event schedule, a calendar, historical energy use data, sensor data or other data sources that would be apparent to one of skill in the art in possession of the present disclosure) or the data gathered from the networked components. In other embodiments, the data sources may include state of charge data or analytics of other REPPs and their energy storage systems. These other REPPs may include energy storage systems that are not on the network and may be those of competitors. As such, a prediction of how much energy storage another REPP may be beneficial as to anticipate how much energy will be available for the grid at a certain time so that control of the networked energy stored systems can be managed. [00183] The EMS controller 1305, using the predictive algorithms trained on historical or simulator data, may then anticipate energy demand for uncontrollable loads (e.g., load 1330 or 1355) on the grid as well as an energy supply on the REPP 1300. Based on the anticipated energy demand and the energy supply, the EMS controller 1305 may determine whether one or more energy balancing conditions associated with a respective controllable load are satisfied to either increase power distribution to that controllable load 1370 or 1375 or reduce power distribution to that controllable load 1370 or 1375. For example, in exchange for a better rate on its energy price or some other energy distribution factor that the controllable load desires, the controllable load 1370 or 1375 may allow the EMS controller 1305 to reduce energy consumption at that controllable load to reallocate the networked power plant’s energy supply to loads that are not controllable and that may pay a higher premium, are higher prioritized based on various factors (e.g., more necessary / high priority infrastructure such as a hospital, a water distribution plant, critical communication infrastructure, or the like). [00184] Similarly, the controllable load 1370 or 1375 may include an ESS where the EMS controller 1305 may increase or decrease power distribution to the ESS. Furthermore, more optimal decisions can be made of which energy storage device in an ESS 1310 or 1365 to store energy. For example, a lower cost with lower round trip efficiency ESS is selected (e.g., a zinc air battery), may be charged when cheap power (or even negatively priced power) is available, longer storage cycles are anticipated with lower parasitic loads making them more efficient in totality, while a high-efficiency but more costly ESS (e.g., a lithium-ion battery) may be charged when more expensive power is available, faster response times are anticipated, higher energy storage efficiencies are beneficial, or when other beneficial conditions are present that would be apparent to one of skill in the art in possession of the present disclosure. As such, a type of energy storage device or other factors associated with the energy storage device may be used to determine when a particular energy storage device is to be charged or how much charge a particular energy storage device is to receive. [00185] In other embodiments, the EMS controller 1305 may also use the anticipated energy demand and the energy supply to balance the storage of energy generated by the REPPs on associated batteries. For example, the EMS controller 1305 may determine the amount of energy stored on each battery and how those batteries in the networked power plants are going to distribute the energy in an optimized manner. For example, to preserve the life expectancy of a battery, under normal conditions, that battery may not be completely filled or completely drained as doing so decreases the life expectancy of the battery. However, if the anticipated energy supply and demand indicate a condition where it is more beneficial to fully charge a battery or fully discharge a battery than to consider the life expectancy of the battery, the EMS controller 1305 may cause the battery to be fully charged in anticipation of the future event. For example, if there is an anticipated event that requires or involves a high demand of energy, the EMS controller 1305 may fully charge the battery. In other embodiments, the EMS controller 1305 may tier the batteries of ESSs 1310 or 1365 such that a first battery distributes energy based on a first condition, a second battery distributes energy based on a second condition, and a third battery distributes energy based on a third condition. These the conditions may be prioritized based on different levels. For example, the third battery may only distribute energy if the price of energy is above a certain threshold. [00186] In yet other embodiments of the present disclosure, the EMS controller 1305 may determine when to provide energy storage to power plants that are not included in the REPP 1300 such as power plants that are on the grid 1325. The EMS controller 1305 may determine conditions where the power plant may store energy on the networked power plant’s batteries or other ESSs. Using the anticipated energy demand and energy storage determinations made by the machine learning algorithms of the EMS controller 1305, the EMS controller 1305 may determine when to purchase power from power plants on the grid or provide storage for contracted power plants or shift energy between storage devices. The EMS controller 1305 may communicate with an application located at the power plant similarly to an application provided at the controllable loads and storage of the networked power plants. As such, the systems and methods of the present disclosure provide more optimal and consistent energy generation, storage, and distribution of energy generated by REPPs, which may be seasonal in nature. [00187] In various embodiments, the EMS 1305, based on decisions made using the predictive algorithms and collected data satisfying a trigger condition, may actuate the switch 1345 to selectively connect the second ESS inverter 1360 to the first meter 1320, the second meter 1350, or the third load 1370 based on a trigger condition. In some embodiments, the trigger condition may be a termination of a time period. The time period may be a season. For example, the EMS 1305 may actuate the switch 1345 based on summer ending and fall beginning. In other embodiments, the trigger condition may be the RES output, such as a daily average RES output, falling below a predefined threshold. In some embodiments, the predefined threshold may be based on power demands of the first load 1330. For example, the RES output may fall below a threshold such that the RES 1335 does not produce enough daily energy to satisfy the power demands of the first load 1330 and satisfy power demands of the second load 1355 and the third load 1370. While the actuation of the switch 1345 may be based on trigger conditions that are reactive to condition being satisfied at present time, the trigger conditions may be based on predictive conditions that are likely to occur on the REPP 1300, the grid 1325, the loads 1330, 1355, 1370, or 1375, or other components of the REPP 1300. [00188] The RES 1335 may deliver power directly to the first load via the grid 1325. The RES 1335 may deliver power to the first ESS 1310 to charge the first ESS 1310. The EMS 1305 may determine how much power the RES 1335 delivers to the first load 1330, how much power the RES 1335 delivers to the first ESS 1310, and how much power the first ESS 1310 delivers to the first load 1330. The EMS 1305 may determine how much power the RES 1335 produces. The first ESS 1310 may be charged by the RES 1335 and later discharged to provide power to the first load 1330. In some embodiments, the first ESS 1310 may be simultaneously charged by the RES 1335 and discharged to provide power to the first load 1330. The first meter 1320 measures an amount of energy delivered to the first load 1330. The amount of energy delivered to the first load 1330 may be a sum of the energy delivered to the first load 1330 by the RES 1335 and the energy delivered to the first load 1330 by the first ESS 1310. The first ESS 1310 may deliver power to the first load 1330 when the RES 1335 is not delivering power to the first load 1335, or when the RES 1335 is delivering power to the first load 1335. The EMS 1305 may determine a charge/discharge of the first ESS 1310 and a state of charge (SOC) of the first ESS 1310. [00189] In some embodiments, the RES 1335 and the first ESS 1310 are sized such that the REPP 1300 can deliver power to the first load 1330 with a capacity factor greater than or equal to about 60-80%. In some embodiments, the capacity factor is 80-100%. In some embodiments, the REPP is oversized to have a capacity factor of over 100%, where capacity factor is defined by dividing the REPP 1300 total output by the connection output to the grid 1325. This may be accomplished by increasing the maximum power output of the RES 1335 and its ESSs 1310 and 1365 to generate more power or provide more power than can be inject onto the grid 1325. Excess power may be consumed by the ESSs 1310, 1360, or the behind-the-meter loads 1370. Controllable loads on the grid 1325 may also be utilized to decrease or increase power consumption based on the total output required by the other loads 1330 or 1355 on the grid such that the capacity factor remains stable and near 100%. By utilizing controllable loads, the EMS controller may now shut down a controllable load splitting a REPP 1300 into two REPPs in the winter or other seasons with a controllable load and a partitioned battery. One REPP 1300 may be partitioned into two power plants where one is behind-the-meter and one on the grid 1325. This may guarantee the emergency backup power plant such as when the grid 1325 needs power in the winter due to outages or cold spells, the power can be quickly allocated from the load 1370 and provided on the grid. In various embodiments, the base load portion of the REPP 1300 may be bigger in summer and smaller in the winter. However, it may be desirable in certain scenarios for the base load portion of the REPP 1300 to be bigger in the winter, other seasons or temporarily bigger in the winter. [00190] In some embodiments, the capacity factor varies (e.g., seasonally). The RES 1335 may be sized to produce enough energy to satisfy power demands of the first load 1330 despite variations in RES output inherent in many RESs. The RES 1335 may have a peak output higher than the power demands of the first load 1330. The first ESS 1310 may be sized to store an amount of energy from the RES 1335 sufficient to time-shift the RES output to satisfy the power demands of the first load 1330. The EMS 1305 may control the RES 1335 or the first ESS 1310 to deliver power to the first load 1330. The RES 1335 may produce a first amount of energy each day, where the first amount of energy is sufficient to satisfy the power demands of the first load 1330. The RES 1335 may produce the first amount of energy at a level of reliability (i.e., the RES 1335 produces the first amount of energy a particular percentage of days in a year). The level of reliability may be specified for the first load 1330. The first ESS 1310 may store a portion of the first amount of energy such that the power delivered to the first load 1330 from the REPP 1300 is spread out throughout each day. The REPP 1300 may provide power to the first load 1330 for a period of time longer than the RES 135 produces power. In an example, the RES 1335 is a solar power source which produces power until 7:00 pm at certain times of the year, and the first ESS 1310 stores the portion of the first amount of energy and discharges it such that the REPP 1300 delivers power to the first load 1330 until midnight. In another example, the REPP 1300 is a solar power source which produces power until 7:00 pm at certain times of the year, and the first ESS 1310 stores the portion of the first amount of energy such that the REPP 1300 provides power to the first load 1330 continuously. [00191] FIG.14 is a block diagram of the REPP of FIG.13, with the switch 1345 connecting the second energy storage system (ESS) 1365 with the third load 1370 and not connecting the second ESS 1365 with the first meter 1320 or the second meter 1350. In this configuration, the RES 1335 and the second ESS 1365 can supply power to the third load 1370. In this configuration, the REPP 1300 supplies power to the first load 1330 via the grid 1325 and to the third load 1370 directly (e.g., behind-the-meter). For purposes of discussion, this configuration will be referred to herein as “summer mode.” However, this configuration is in no way restricted to use in summer. In fact, as discussed above, this configuration may be used in winter where the REPP 1300 is partitioned into two or more power plants where the load 1370 is controllable such that a portion of the RES 1335 and the ESSs 1310 and 1365 may be partitioned for use with load 1370 and another portion of the RES 1335 and the ESSs 1310 and 1365 may be dedicated to the grid 1325. Because the grid 1325 may put restrictions on the amount of power needed in the winter because of lower load requirements, some of the power generated and stored by the REPP may be dedicated to the controllable load 1370. However, balancing authorities may require stability with load and generation and as such may require emergency power in the winter due to unexpected power demands. To provide this emergency power, the power to the load 1370 may be reduced so that emergency power can be reallocated to the grid 1325 quickly without having the excessive load constantly present on the grid 1325. Typically, “dirty” backup generators or idling fossil fuel plants provide the emergency power to the grid and this configuration allows the RES 1335 to continue to run providing back-up power when there is a system emergency requiring instantaneous emergency power. As such, the systems and methods of the present disclosure reduce or eliminate the need of fossil fuel power plants idling, burning costly fuel and causing damage to the environment. [00192] With respect to summer mode, summer mode may be used in times when the RES 1335 produces an excess amount of energy over the power demands of the first load 1330. For example, a solar array may produce more power in the summer than in the winter, resulting in surplus power production in the summer. In some embodiments, the REPP 1300 may deliver some or all the excess energy to the grid 1325. However, in places where solar energy is prevalent, power delivered at times of day when solar energy sources produce power, such as as-delivered solar power, generally has low value relative to power delivered at times of day when solar energy sources do not produce power. In other embodiments, the REPP 1300 may deliver power to the third load 1370. The REPP 1300 may deliver power to the third load 1370 because the ability to use an ESS to time-shift the energy to a higher-value time of day may be operationally and economically preferable to delivering power. The REPP 1300 may time-shift the RES output using the second ESS 1360 to deliver power to the third load 1370 longer than the RES 1335 produces power or at times when power is in lesser supply than during peak solar production times. The second ESS 1360 may have a storage capacity large enough to store the excess energy produced by the RES 1335. The second ESS 1360 may have a charge/discharge capacity large enough to be charged by excess power produced by the RES 1335 and deliver power to the third load 1370 when needed. [00193] The EMS 1305 may control the charge/discharge of the first ESS 1310 and the charge/discharge of the second EMS 1360 to time-shift the RES output to satisfy the power demands of the first load 1330 and deliver power to the third load 1370. In some embodiments, the EMS 1305 may control the RES output of the RES 1335 to satisfy the power demands of the first load 1330 and deliver power to the third load. The EMS 1305 may adjust inverter setpoints of the first ESS inverter 1315 and the second ESS inverter 1360 to control the charge/discharge of the first ESS 1310 and the charge/discharge of the second EMS 1360. [00194] When the RES output exceeds the power demands of the first load 1330, the EMS 1305 may direct a first load portion of the RES output to the first load 1330, up to a power limit of the first load 1330. If a current time is a time when power is to be delivered to the third load 1370, dependent upon power demands of the third load 1370 and current energy prices, the EMS 1305 may direct a third portion load of the RES output to the third load 1370 up to a power limit of the third load 1370. The EMS 1305 may deliver RES output in excess of what is directed to the first load 1330 and the third load 1370 to the first ESS 1310, up to the charging power limit of the first ESS 1310 and up to a full charge of the first ESS 1310. The EMS 1305 may deliver RES output in excess of what is directed to the first load 1330, the third load 1370, and the first ESS 1310 to the second ESS 1365, up to the charging power limit of the second ESS 1365 and up to a full charge of the second ESS 1365. The EMS 1305 may curtail, using inverter setpoints of the RES inverter 1340, RES output in excess of the combined power limits of the first load 1330 and second load if the first ESS 1310 and the second ESS 1365 are fully charged. [00195] If the RES output is less than the power limit of the first load 1330 and there is energy stored in the first ESS 1310, the EMS 1305 may set a discharge of the first ESS 1310 such that the REPP 1300 delivers power to the first load 1330 equal to the power limit of the first load 1330, limited by a rated discharge rate of the first ESS 1310 and the energy stored in the first ESS 1310. [00196] If the RES output is greater than the power limit of the first load 1330 but less than the combined power limit of the first load and the third load 1370, and a current time is a time when power is to be delivered to the third load 1370, dependent upon power demands of the third load 1370 and current energy prices, the EMS 1305 may set a discharge of the second ESS 1365 such that the REPP 1300 delivers power to the third load 1370 equal to the power limit of the third load 1370, limited by a rated discharge rate of the second ESS 1365 and the energy stored in the second ESS 1365. [00197] FIG.15 is a block diagram of the REPP of FIG.13, with the switch 1345 connecting the second ESS 1365 with the second meter 1350 and not connecting the second ESS 1365 with the first meter 1320 or the third load 1370. In this configuration, the RES 1335 and the second ESS 1365 can supply power to the second load 1355. In this configuration, the REPP 1300 supplies power to the first load 1330 and the second load 1355 via the grid 1325. For purposes of discussion, this configuration will be referred to herein as “winter mode.” This configuration, however, is in no way restricted to use in winter. As discussed above, this mode may be implemented in summer as well. Energy delivered through the grid is inevitably commingled on the grid. Energy that flows through the first meter 1320, however, may be deemed (operationally, economically, and contractually) to have been delivered to the first load 1330. Similarly, energy that flows through the second meter 1350 may be deemed (operationally, economically, and contractually) to have been delivered to the second load 1355. [00198] Winter mode may be used in times when the RES 1335 does not produce enough daily energy in excess of the power demands of the first load 1330 to fully cycle the second ESS 1365. For example, a solar array may produce more power in the summer than in the winter, resulting in less power production in the winter than in the summer. When the second ESS 1365 is sized to time-shift the RES output for delivery to the third load 1370, a lower winter RES output is insufficient to satisfy the power demands of the third load 1370. However, when the RES 1335 and the ESS 1310 and 1365 are overbuilt, then the REPP 1300 may produce too much power for the winter and there is need to unload some of the excess power while still maintaining emergency power to the load 1370 or the controllable load 1375 by creating more load on the grid as discussed above with respect to FIG.14. The EMS controller 1305 with the predictive algorithm can better manage when during each season the switch 1345 should be actuated between the load 1370 and the meter 1350. [00199] In winter mode, the second ESS 1365 may provide power capacity to the second load 1355 or the controllable load 1375. The second ESS 1365 may store energy for use by the second load 1355 when demanded by the second load 1355. In some embodiments, the use by the second load 1355 is occasional use. The EMS 1305 may direct power to the second ESS 1365 to fully charge the second ESS 1365. In some embodiments, the EMS 1305 may charge the second ESS 1365 with RES output exceeding the power demands of the first load 1330. The EMS 1305 may offset a self-discharge of the second ESS 1365 (i.e., the tendency of the second ESS 1365 to lose stored energy over time even when not discharged) by directing power from the RES 1335 to the second ESS 1365. Charging the second ESS 1365 with RES output exceeding the power demands of the first load 1330 typically requires the RES 1335 to be sized large enough to have excess output even in times of reduced output, such as winter in the case of a solar resource. The RES 1335 may be sized large enough produce RES output sufficient to satisfy the power demands of the first load 1330, account for round-trip energy losses in the first ESS 1310, charge the second ESS 1365 over an acceptable period of time as discussed below, and maintain a charge on the second ESS 1365. In other embodiments, the second ESS 1365 may charge the second ESS 1365 by temporarily reducing the power delivered to the first load 1330. The second ESS 1365, when fully charged to a readiness state of charge, may act as a short-term power source for emergency or contingency use for the second load 1355. The emergency capacity offered by the second ESS 1365 may allow an operator of the grid 1325 to avoid keeping a fossil fuel plant online as a spinning reserve for rapid response. Instead, the grid operator could use the second ESS 1365 as a spinning reserve and use the energy stored in the second ESS 1365 for rapid response. Depending on a length of the emergency or contingency, the grid operator may have time to bring the fossil fuel plant online or may avoid needing to use the fossil fuel plant altogether. [00200] Once the second ESS 1365 has been discharged during an emergency or contingency, the EMS 1305 may direct power from the RES 1335 to the second ESS 1365 to fully charge the second ESS 1365. Depending on how much the RES output exceeds the power demands of the first load 1330 and whether the EMS 1305 decreases the power delivered to the first load 1330, a time required or used to fully charge the second ESS 1365 may be an hour, a day, a week, or any amount of time. A target amount of time for the second ESS 1365 to be fully charged may be used to determine a size of the RES 1335. The RES 1335 may be sized to produce enough winter RES output to fully charge the second ESS 1365 within the target amount of time. In some embodiments, as discussed above, the EMS 1305 may reduce the power delivered to the first load 1330 to more quickly charge the second ESS 1365. [00201] In some embodiments, the second ESS 1365 may provide grid services such as voltage and frequency support to the grid 1325 by charging and discharging the second ESS in small increments as needed. In some embodiments, the second ESS 1365 may provide grid services capacity to the second load 1355 to offset an impact of the second load 1355 on the grid. In some embodiments, the second load 1355 may communicate with the EMS 1305 to coordinate the charge/discharge of the second ESS 1365 with power consumption fluctuations of the second load 1355. [00202] In another example, the grid 1325 may include two or more grids that are connected but regulated by different balancing authorities. The first grid may provide power to load 1330 and the second grid may provide power to load 1355. The load 1330 may be geographically distinct from the load 1355. For example, the load 1330 may be an area in southern California where the load 1330 is correlated with a particular load profile that has less load in the winter and more load in the summer due to air conditioning usage. In contrast, the load 1355 may be located in northern California where the loads are correlated with energy consumptions by large data centers and contracts for renewable energy to service these data centers. As such, the fluctuation in the load profile between the summer and winter in the load 1355 is not as great. That region’s energy production of clean solar energy in the winter months, however, may not satisfy the demand of the load 1355 when power production is lower. As such, the excess power generated by the RES 1335 that is not needed by the load 1330 in the winter months may be rerouted to the load 1355 to satisfy that load’s demand for power and renewable power. Thus, the loads 1330 and 1355 may be described as correlated within each load, uncorrelated with each other, and complimentary because their load profiles and power production profiles at different times of the year provide room for efficiencies and optimization to share power across the grids. As a result, the EMS 1305 may actuate the switch 1345 in the winter to be connected with the meter 1350 such that the inverter 1360 is providing power to the meter 1350 and the load 1355 while in the summer months the switch 1345 is not connected to the meter 1350. [00203] FIG. 16 is a block diagram of the REPP 1300 of FIG. 13, with the switch 1345 connecting the second ESS 1365 with the first meter 1320 and not connecting the second ESS 1365 with the second meter 1350 or the third load 1370. In this configuration, the RES 1335, the first ESS 1310, and the second ESS 1365 can supply power to the first load 1330 via the grid 1325. For purposes of discussion, this configuration will be referred to herein as “focus mode.” This term, however, is in no way limiting. [00204] In some embodiments, focus mode may be used when greater storage capacity than is provided by the first ESS 1310 is required (or uses) by the first load 1330. For example, the RES output may be great enough or timed such that the first ESS 1310 is unable to time-shift the RES output sufficient to satisfy the power demands of the first load 1330. The second ESS 1365 may assist the first ESS 1310 in time-shifting the RES output to satisfy the power demands of the first load 1330. In some embodiments, focus mode may be used in summer when the RES output is greater than can be time-shifted by the first ESS 1310 and summer mode may be used in fall and spring when the RES output can be time-shifted by the first ESS 1310 alone. [00205] In some embodiments, focus mode may be used when the first load 1330 requires (or uses) energy storage capacity. The second ESS 1365 may be charged to a state of readiness as in winter mode and may provide power to the first load 1330 as demanded. Focus mode, with the second ESS 1365 providing capacity to the first load 1330, may be used in any season. Furthermore, although summer mode and winter mode are described as providing power to the third load 1370 and capacity to the second load 1355, respectively, the EMS 1305 may control the REPP 1300 according to summer mode to provide power to the second load 1355 and capacity to the third load 1370, respectively. [00206] The EMS 1305 may actuate the switch 1345 to modify a configuration of the REPP 1300 to be in summer mode, winter mode, or focus mode. The EMS 1305 may actuate the switch 1345 to electrically decouple the second ESS 1360 from whatever it is connected to, such as the third load 1370, the second meter 1350, or the first meter 1320. The EMS 1305 may actuate the switch 1345, as discussed herein, based on the trigger condition. In some embodiments, the trigger condition may be a termination of a time period. The time period may be a season. For example, the EMS 1305 may actuate the switch 1345 based on summer ending and fall beginning or based on spring ending and summer beginning. In other embodiments, the trigger condition may be the RES output, such as a daily average RES output, falling below or rising above a predefined threshold. In some embodiments, the predefined threshold may be based on power demands of the first load 1330. In an example, the EMS 1305 may actuate the switch 1345 based on the RES output falling below a threshold such that the RES 1335 does not produce enough daily energy to satisfy the power demands of the first load 1330 and satisfy power demands of the second load 1355 and the third load 1370. In another example, the EMS 1305 may actuate the switch 1345 based on the RES output rising above a threshold such that the RES 1335 produces enough daily energy to satisfy the power demands of the first load 1330 and satisfy power demands of the second load 1355 and the third load 1370. In other embodiments, as discussed above, the EMS controller 1305 may actuate the switch 1345 based on data gathered from the data sources 1311 or data gathered from the REPP components coupled to the network 1304 and inputting that data into the predictive algorithm such as, for example, model predictive control (MPC), model-based reinforcement learning (MBRL), adaptive model predictive control (AMPC) or other predictive algorithm/machine learning algorithm. MPC may be implemented with a long short-term memory (LSTM), state space model, or transformer architecture. Some embodiments may use a multi- modal time-series forecasting model (e.g., accounting for weather, wind production, solar production, grid demand, and value of behind-the-meter load outputs), examples including: autoregressive–moving-average (ARMA) models (e.g., Seasonal ARIMA); autoregressive integrated moving average (ARIMA) model; generalized autoregressive conditional heteroskedasticity (GARCH) models; vector autoregression models, Holt-Winters exponential smoothing; state space models; and Kalman filters. [00207] FIG. 17 is a block diagram of the REPP of FIG. 13, with a second switch 1346 connecting the first ESS 1310 with the second meter 1350. The EMS 1305 may connect the first ESS 1310 with the second meter 1350 and the second ESS 1365 with the first meter 1320 in order to swap a use of the first ESS 1310 and the second ESS 1365. The first ESS 1310 may be used to provide capacity to the second load 1355 and the second ESS 1365 may be used to time-shift the RES output to satisfy the power demands of the first load 1330. The EMS 1305 may similarly swap the uses of the first ESS 1310 and the second ESS 1365 in the summer and winter modes, connecting the first ESS 1310 to the third load 1370 and the second ESS 1365 to the first meter 1320 in the summer mode and connecting the first ESS to the second load 1355 and the second ESS 1365 to the first load 1330 in the winter mode. The EMS 1305 may alter the connections of the first ESS 1310 and the second ESS 1365 by actuating the switch 1345 and/or the second switch. Alternating uses of the first ESS 1310 and the second ESS 1360 may be used to levelize a degradation of the first ESS 1310 and the second ESS 1360. ESS degradation may include a reduced total storage capacity, a reduced maximum charge/discharge rate, and/or an increased self- discharge rate. Levelizing the degradation of the first ESS 1310 and the second ESS 1365 may include monitoring a first ESS degradation and a second ESS degradation and altering a first ESS use and a second ESS use such that the first ESS degradation is equal to the second ESS degradation. Different uses for the first ESS 1310 and the second ESS 1365 may result in different levels of degradation. In some embodiments, levelizing the degradation of the first ESS 1310 and the second ESS 1365 may include equalizing a first amount the first ESS 1310 and the second ESS 1365 are used for a first use and a second amount the first ESS 1310 and the second ESS 1365 are used for a second use. For example, an ESS may degrade faster if it is cycled daily than if it were used to provided capacity. In this example, if the first ESS 1310 is cycled daily to time-shift the RES output while the second ESS 1365 is used to provide capacity, the first ESS 1310 will degrade faster. In this example, the first ESS 1310 and the second ESS 1365 may be levelized by cycling the second ESS 1365 daily to time-shift the RES output while using the first ESS 1310 to provide capacity such that the first ESS degradation is equal to the second ESS degradation. Actuating the switch 1345 and the second switch 1346 to alternate uses of the first ESS 1310 and the second ESS 1365 may reduce a difference in degradation rates and/or degradation of the first ESS 1310 and the second ESS 1365. The switch 1345 and the second switch 1346 may be actuated periodically to alternate uses of the first and second ESSs 1310, 1365, such as seasonally or annually. [00208] In some embodiments, one or more functionalities of the system 1300 of FIGS.13-17 can be combined with or replaced by one or more functionalities of any of system 100 of FIG.1, system 400 of FIG.4, system 1000 of FIG.10, and/or system 2000 of FIG.20. Alternatively or in addition, one or more functionalities of the EMS 1305 of FIG.13 can be implemented using one or more functionalities / features of any of controller 200 of FIG.2, controller 1102 of FIG. 11, controller 1802 of FIG. 18, and/or controller 2102 of FIG. 21. Alternatively or in addition, the system 100 of FIG.1 can be configured to perform one or more of method 300 of FIG.3, method 500 of FIG.5, method 600 of FIG.6, method 700 of FIG.7, method 800 of FIG.8, method 1200 of FIG.12, method 1900 of FIG.19, or method 2200 of FIG.22. [00209] FIG. 18 illustrates an embodiment of an EMS controller 1800 that may be the EMS controller 1305 discussed above with reference to Fig.13. While described as a standalone system, those skilled in the art will appreciate that the EMS controller 1800 may be distributed across many computing devices such as in a cloud environment. In the illustrated embodiment, the EMS controller 1800 includes a chassis 1802 that houses the components of the EMS controller 1800, only some of which are illustrated in FIG. 18. For example, the chassis 1802 may house a processing system (not illustrated) and a non-transitory memory system (not illustrated) that includes instructions that, when executed by the processing system, cause the processing system to provide an EMS engine 1804 that is configured to perform the functions of the EMS engines or the EMS controller discussed below. In the specific example illustrated in FIG. 18, the EMS engine 1804 may include an EMS predictive algorithm 1805 that is configured to perform the functions of the energy generation, storage, and distribution predictive algorithms discussed herein. In various embodiments, the energy generation, storage, and distribution predictive algorithm 1805 may ingest data provided by data sources and anticipates energy demand and energy supply, or any other functionality discussed herein. In various embodiments, the energy generation, storage, and distribution predictive algorithm 1805 may include a network simulator to model behavior, which may predict the components being incorporated into the grid by running simulations due to lack of historical data. In other examples, the EMS predictive algorithm 1805 may include model predictive control or other predictive algorithms/machine learning algorithms that would be apparent to one of skill in the art in possession of the present disclosure. [00210] The chassis 1802 may further house a communication system 1806 that is coupled to the EMS engine 1804 (e.g., via a coupling between the communication system 1806 and the processing system) and that is configured to provide for communication through the communication network 1304 as detailed below. The chassis 1802 may also house a storage system 1808 that is coupled to the EMS engine 1804 through the processing system and that is configured to store the rules or other data (e.g., trained models, training data, or the like) utilized by the EMS engine 1804 to provide the functionality discussed below. While a EMS controller 1800 has been illustrated, one of skill in the art in possession of the present disclosure will recognize that other EMS controllers (or other devices operating according to the teachings of the present disclosure in a manner similar to that described below for the EMS controller 1800) may include a variety of components and/or component configurations for providing known computing device functionality, as well as the functionality discussed below, while remaining within the scope of the present disclosure as well. [00211] Figures 6-17 described in U.S. Patent No. 11,621,566, filed October 5, 2022, titled “Seasonal Electrical Resource Allocation”, describe various example power allocations of the REPP, example power deliveries of the REPP, and example ESS state of charges (SOC), the entire contents of which is hereby incorporated by reference in its entirety and used in conjunction of the embodiments of the present disclosure. [00212] In some embodiments, a renewable energy power plant (REPP) includes a renewable energy source (RES), a first meter associated with a first load, a second meter associated with a second load, a first ESS electrically coupled to the RES and the first meter, a second ESS electrically coupled to the RES and the first meter through a switch, a controllable load coupled to the RES through the switch, and a controller configured to set a first charge/discharge for the first ESS and a second charge/discharge for the second ESS such that the REPP delivers power to the first load longer than the RES produces power, in response to a trigger condition, actuate the switch such that the second ESS is electrically coupled to the controllable load, and set a fourth charge/discharge for the second ESS such that the second ESS maintains a portion of its charge in reserve for the controllable load. The trigger condition and determination to actuate the switch is made by a predictive algorithm using machine learning based on data gathered from the REPP or third party sources. [00213] In some embodiments, a non-transitory, processor-readable medium stores instructions that, when executed by a processor, cause the processor to set (1) a first charge/discharge for a first energy storage system (ESS) of a renewable energy power plant (REPP) and (2) a second charge/discharge for a second ESS of the REPP, such that the REPP delivers power to a first load for a first time period that is longer than a first production time period when a renewable energy source (RES) of the REPP produces power. The first ESS is electrically coupled to the RES and to a first meter, and the second ESS is electrically coupled to the RES and to the first meter through a switch. The non-transitory, processor-readable medium also stores instructions to cause the processor to, in response to a first trigger condition being satisfied, cause an actuation of the switch such that the second ESS is electrically coupled to a controllable load. The non-transitory, processor-readable medium also stores instructions to cause the processor to set a third charge/discharge for the first ESS such that the REPP delivers power to the first load for a third time longer than a second production time period when the RES produces power. The non- transitory, processor-readable medium also stores instructions to cause the processor to set a fourth charge/discharge for the second ESS such that the second ESS maintains a portion of its charge in reserve for a second load. The non-transitory, processor-readable medium also stores instructions to cause the processor to, in response to a second trigger condition being satisfied, cause an actuation of the switch such that the second ESS is electrically coupled to a second meter different from the first meter. The non-transitory, processor-readable medium also stores instructions to cause the processor to set a fifth charge/discharge for the second ESS such that the second ESS maintains a portion of its charge in reserve for the second meter, the RES being tuned to satisfy a power delivery parameter of the first load and maintain a portion of charge of the second ESS. [00214] In some implementations, the non-transitory, processor-readable medium also stores instructions to detect that at least one of the first trigger condition or the second trigger condition is satisfied based on a prediction made by a predictive algorithm. The predictive algorithm can include at least one of model predictive control (MPC), model-based reinforcement learning (MBRL), or adaptive model predictive control (AMPC). [00215] In some implementations, the controllable load is behind at least one of the first meter or the second meter. [00216] In some implementations, the non-transitory, processor-readable medium also stores instructions to cause the processor to at least one of determine the first charge/discharge for the first ESS based, at least in part, on a type of energy storage associated with the first ESS, or determine the second charge/discharge for the second ESS at based, at least in part, on a type of energy storage associated with the second ESS. [00217] In some embodiments, a non-transitory, processor-readable medium stores instructions that, when executed by a processor, cause the processor to set, at a renewable power plant (REPP), a first charge/discharge for a first energy storage system (ESS) of the REPP and a second charge/discharge for a second ESS of the REPP, such that the REPP delivers power to a first load for a first time longer than a first production time period when a renewable energy source (RES) of the REPP produces power, the first ESS being electrically coupled to the RES and to a first meter, and the second ESS being electrically coupled to the RES and to the first meter through a switch. The non-transitory, processor-readable medium also stores instructions to cause the processor to determine, based on a prediction generated using a first predictive algorithm, that a first trigger condition is satisfied. The non-transitory, processor-readable medium also stores instructions to cause the processor to, in response to determining that the first trigger condition is satisfied, cause actuation of the switch such that the second ESS is electrically coupled to a controllable load. The non-transitory, processor-readable medium also stores instructions to cause the processor to set a third charge/discharge for the first ESS such that the REPP delivers power to the first load for a third time longer than a second production time period when the RES produces power. The non-transitory, processor-readable medium also stores instructions to cause the processor to set a fourth charge/discharge for the second ESS such that the second ESS maintains a portion of its charge in reserve for a second load. The non-transitory, processor-readable medium also stores instructions to cause the processor to determine, based on a prediction generated using a first predictive algorithm, that a second trigger condition is satisfied. The non-transitory, processor-readable medium also stores instructions to cause the processor to, in response to determining that the second trigger condition is satisfied, cause actuation of the switch such that the second ESS is electrically coupled to the second meter. The non-transitory, processor-readable medium also stores instructions to cause the processor to set a fifth charge/discharge for the second ESS such that the second ESS maintains a portion of its charge in reserve for the second meter. [00218] In some implementations, the controllable load includes at least one of a data center, an artificial intelligence (AI) training center, a cryptocurrency miner, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a water treatment plant, an industrial process heater, or a thermal battery. [00219] In some implementations, the non-transitory, processor-readable medium also stores instructions to cause the processor to operate at least one of the RES, the first ESS, or the second ESS as at least one of a peaker plant for the electric grid or a provider of ancillary services to the electric grid. [00220] In some implementations, the non-transitory, processor-readable medium also stores instructions to operate the controller of the REPP in a first mode as at least one of a baseload, a semi-baseload, a semi-peaker plant, or a peaker plant for the controllable load, and (2) concurrently with operating the controller of the REPP in the first mode, operating the controller of the REPP in a second mode as at least one of a peaker plant, a semi-peaker plant, a semi-baseload, a baseload, or a provider of ancillary services for the electric grid. [00221] In some implementations, at least one of the first trigger condition or the second trigger condition includes an output of at least one of the first RES or the second RES exceeding a predefined threshold such that the at least one of the first RES or the second RES produces enough daily energy to satisfy a power demand of the first load and the second load. [00222] In some implementations, at least one of the determining that the first trigger condition is satisfied or the determining that the second trigger condition is satisfied is based on a model predictive control (MPC) algorithm implemented with one of a long short-term memory (LSTM), a state space model, or a transformer architecture. System and Methods for Smart Renewable Powerplant Serving Multiple Controllable and Uncontrollable Loads [00223] Some aspects of the present disclosure include a process including: a) determining one or more metrics for different time periods of a forecast horizon, wherein the one or more metrics relate to sending energy generated by a first renewable energy system (RES) to: (1) an energy storage system (ESS), (2) a power grid including one or more loads, and (3) and one or more behind-the-meter loads; b) prioritizing: (1) the ESS, (2) the power grid, and (3) the one or more behind-the-meter loads, wherein the prioritization is based on one or more of: (1) the one or more metrics determined in (b), (2) a state of charge of the ESS during the forecast horizon, (3) one or more limits related to energy requirements of the power grid during the forecast horizon, or (4) one or more limits related to energy requirements of the one or more behind-the-meter loads during the forecast horizon; and c) providing instructions to deliver power generated by the first RES to at least one of: (1) the ESS, (2) the power grid, or (3) the one or more behind-the-meter loads based on the prioritization. Theone or more behind-the-meter loads includes a controllable load, and process includes providing, based on the prioritization, instructions to the controllable load to increase or decrease energy requirements. Alternatively or in addition, the one or more loads included on the power grid includes a grid controllable load, and the process includes providing, based on the prioritization, instructions to the grid controllable load to increase or decrease energy requirements. Some embodiments include a machine learning algorithm that determines the prioritization or generates the instructions. [00224] Some aspects of the present disclosure include a tangible, non-transitory, machine- readable medium storing instructions that when executed by a data processing apparatus cause the data processing apparatus to perform operations including the above-mentioned process. [00225] Some aspects of the present disclosure include system, including: one or more processors; and memory storing instructions that when executed by the processors cause the processors to effectuate operations of the above-mentioned process. [00226] As discussed above, renewable energy may be produced in a variety of ways, such as by solar powerplants, wind turbines, geothermal powerplants, hydroelectric powerplants, and various others. The power output of the renewable energy sources (RESs) may vary in a predictable manner or a random manner. For example, solar power production may have seasonal and daily cycles according to the seasons and the passage of the sun across the sky, as well as certain random patterns influenced by the passage of clouds between the solar array and the sun. In the example of wind power production, it may have different seasonal and daily patterns as well as a random component influenced by the passage of weather patterns. There may be times when a renewable energy source (RES) is producing excess amounts of energy relative to the demands of the electrical load, and other times when the RES is not able to meet the demand. [00227] One type of technique used today is to install an energy storage system (ESS) that can absorb energy when the RES's production exceeds the demands of the electrical load, and then deliver energy into the grid when the RES's production is short of the demands of the electrical load. In some cases, the ESS may be charged by the RES when the value of delivering power to the electrical load is relatively low (e.g., excessive production), and may be discharged to supplement any output of the RES when the value of delivering power to the electrical load is relatively high, while remaining within the various power limits of the connection to the electrical load and power limits of the equipment in the RES and ESS. [00228] In addition to charging the ESS, or supplying energy to the electrical grid, the renewable energy sources (RESs) may also be used to serve other types of electric loads or energy- consuming processes that may or may not be off-grid. For example, the RESs may provide energy directly to a load such as an industrial process (e.g., production of “green” hydrogen, production of ammonia, metal smelting, cryptocurrency mining, “vertical” farming, powering server farms, water purification and glass production, etc.), without passing through the electrical grid (e.g., off- grid load or also referred to herein as behind-the-meter loads). [00229] However, the multiple types of electrical loads or industrial processes connected to the RESs may not be correlated where the value of directing power to one or more of the electrical loads may vary over time, and such variations may not be correlated to one another. Therefore, there exists a need to manage and improve the allocation of the amount of energy and power amongst the multiple uncorrelated loads. [00230] To help alleviate some of the inefficiencies and energy and power allocation in grid systems, some embodiments of the present techniques may be used in conjunction with the techniques described in U.S. Patent No.12,119,646, issued October 15, 2024 and titled “Systems and Method for Renewable Powerplants Serving Multiple Loads,” the entire contents of which is hereby incorporated by reference in its entirety for all purposes. [00231] Embodiments of the present disclosure describe serving multiple electric loads with renewable electrical power. In particular, energy may be allocated among multiple electric loads that may not be correlated. In some cases, the multiple electric loads may be uncorrelated or partially correlated. Electric loads or energy consuming processes that are not correlated or partially correlated may generally mean that the values of directing power to the electrical loads or the use of energy are not completely correlated to each other. For instance, the value of directing power to at least one of the electrical loads may vary over time and such variance may be at least partially independent of the value of directing power to another electrical load. In some embodiments of the pre, the uncorrelated or the correlated loads may be controllable or uncontrollable loads. Loads that are controllable loads may include loads where the controller, described herein, can change the demand by either increasing or decreasing power demand at that load. As such, the present disclosure considers both adjusting energy allocation from the RES and adjusting energy demand from one or more of the controllable loads that can either be on the grid or behind-the-meter. [00232] In various embodiments of the present disclosure, the controller may include a predictive algorithm such as, for example, model predictive control (MPC), model-based reinforcement learning (MBRL), adaptive model predictive control (AMPC) or other predictive algorithm/machine learning algorithm. MPC may be implemented with a long short-term memory (LSTM), state space model, or transformer architecture. Some embodiments may use a multi- modal time-series forecasting model (e.g., accounting for weather, wind production, solar production, grid demand, and value of behind-the-meter load outputs), examples including: autoregressive–moving-average (ARMA) models (e.g., Seasonal ARIMA); autoregressive integrated moving average (ARIMA) model; generalized autoregressive conditional heteroskedasticity (GARCH) models; vector autoregression models, Holt-Winters exponential smoothing; state space models; and Kalman filters. The predictive algorithm may predict a priority in a future time interval, and based on the prioritization and total predicted energy storage and generation, the predictive algorithm may determine any demand adjustments on the controllable loads and allocate energy and power to the various loads or the ESS based on a prioritization. [00233] In some embodiments, the prioritization is performed a plurality of times throughout the forecast horizon. In some embodiments, if delivering all the available energy from the ESS to the highest prioritized of: (1) the power grid and (2) the one or more industrial processes is not possible because a maximum energy or power limit has been reached, the controller is then configured or programmed to perform the operation of delivering excess energy to the next highest prioritized of: (1) the power grid and (2) the one or more behind the meter loads and repeat the operation until no excess energy is left. In some cases, the one or more behind the meter loads comprises one or more of the following: hydrogen generation through electrolysis, ammonia production, metal smelting, cryptocurrency mining, data center operation, vertical farming, food production, atmospheric water generation, an AI training system, water purification, direct carbon capture/direct air capture, or other processes that would be apparent to one of skill in the art in possession of the present disclosure. [00234] As such, some embodiments of the present techniques may be used in conjunction with the techniques described in U.S. Patent No.12,119,646 to take those techniques a step further and introduced controllable power components including controllable loads (e.g., uncorrelated loads or correlated loads). Loads may be introduced to the system that are behind-the-meter (e.g., are directly connected to the RESs and not connected to the grid system) or loads that are on the grid system but are controllable by the controller. In some embodiments, these loads on the grid system or behind-the-meter may be uncorrelated with a typical energy consumption profile experienced by the grid for a given day or other time period (e.g., a vertical farming operation, training AI models (and other latency insensitive compute workloads), aluminum smelting, direct carbon capture from air, hydrogen production with electrolyzers by electrolyzing water, or other loads that would be apparent to one of skill in the art in possession of the present disclosure). Controllable loads may include correlated or known loads as well where certain contractual arrangements can be met by virtually integrating them into the network. For example, an office building that may see peak power demand during a hot summer day when air conditioners are operating to cool the office space may be an example of a correlated but controllable load. These loads may be controllable even though they generally correlate with the rest of the grid’s energy usage. As such, these loads may be controllable to operate at different times of the day than the peak time. For example, the office building may use the HVAC system to precool or preheat a building in anticipation of having a reduction in power consumption during peak energy demand times. [00235] In various embodiments, the behind-the-meter loads and other controllable loads on the grid system may be in communication with an energy generation, storage and distribution controller. Behind the meter controllable loads allow an energy producer to increase size and performance of the RES. For example, the RES may be built to generate a larger capacity than what the RES can provide to the grid. This provides economy of scale cost and performance advantages over a system without the behind-the-meter controllable loads. When generation is not at peak (e.g. clouds or early morning or late afternoon or low wind etc.) or when the energy storage system included with the RES is full, the excess energy is absorbed by the behind-the-meter load. Also, when RES generation is low the oversized system can deliver more power to more critical or valuable loads on the grid and fulfill the bandwidth of what the RES can provide to the grid or provide more load or power to the energy storage system. As such, the REPP may be designed for better performance and lower cost, i.e., overall system performance is better such that a more consistent energy supply, capacity, or other ancillary services are provided to the grid. [00236] The energy generation, storage and distribution controller that is used for the networked power plants may include predictive algorithms for balancing energy distribution to the controllable loads. For example, the energy generation and distribution controller may ingest data from various data sources (e.g., a weather forecast, an event schedule, a calendar, historical energy use data, sensor data or other data sources that would be apparent to one of skill in the art in possession of the present disclosure). In other embodiments, the data sources may include state of charge data or analytics of other RES s and their energy storage systems. These other RESs may include energy storage systems that are not on the network and may be those of competitors. As such, a prediction of how much energy storage another RES may be beneficial as to anticipate how much energy will be available for the grid at a certain time so that control of the ESSs can be managed. [00237] The energy generation and distribution controller, using the predictive algorithms trained on historical or simulator data, may then anticipate energy demand for uncontrollable loads on the grid as well as an energy supply on the power plants. Based on the anticipated energy demand and the energy supply, the energy generation and distribution controller may determine whether one or more energy balancing conditions associated with a respective controllable load are satisfied to either increase power distribution to that controllable load or reduce power distribution to that controllable load. For example, in exchange for a better rate on its energy price or some other energy distribution factor that the controllable load desires, the controllable load may allow the energy generation, storage, and distribution controller to reduce energy consumption at that controllable load to reallocate the networked power plant’s energy supply to loads that are not controllable and that may pay a higher premium, are higher prioritized based on various factors (e.g., more necessary / high priority infrastructure such as a hospital, a water distribution plant, critical communication infrastructure, or the like). Furthermore, the controllable loads themselves may be adjusted to reduce or increase energy consumption. [00238] Similarly, the controllable load may include an energy storage system where the energy generation, storage, and distribution controller may increase or decrease power distribution to the energy storage device. Furthermore, more optimal decisions can be made of which energy storage device in an energy storage system to store energy. For example, a zinc air battery may be charged when cheap power is available while a lithium-ion battery may be charged when more expensive power is available, faster response times are anticipated, or when other beneficial conditions are present that would be apparent to one of skill in the art in possession of the present disclosure. As such, a type of energy storage device or other factors associated with the energy storage device may be used to determine when a particular energy storage device is to be charged or how much charge a particular energy storage device is to receive. [00239] In other embodiments, the energy generation and distribution controller may also use the anticipated energy demand and the energy supply to balance the storage of energy generated by the RESs on associated batteries. For example, the energy generation and distribution controller may determine the amount of energy stored on each battery and how those batteries in the power plants are going to distribute the energy in an optimized manner. For example, to preserve the life expectancy of a battery, under normal conditions, that battery may not be completely filled or completely drained as doing so decreases the life expectancy of the battery. However, if the anticipated energy supply and demand indicate a condition where it is more beneficial to fully charge a battery or fully discharge a battery than to consider the life expectancy of the battery, the networked energy generation and distribution controller may cause the battery to be fully charged in anticipation of the future event. For example, if there is an anticipated event that requires a high demand of energy, the energy generation and distribution controller may fully charge the battery. In other embodiments, the networked energy generation and distribution controller may tier the batteries such that a first battery distributes energy based on a first condition, a second battery distributes energy based on a second condition, and a third battery distributes energy based on a third condition. These the conditions may be prioritized based on different levels. For example, the third battery may only distribute energy if the price of energy is above a certain threshold. [00240] In yet other embodiments of the present disclosure, the energy generation and distribution controller may determine when to provide energy storage to power plants that are not included in the RESs such as power plants that are on the grid. The energy generation and distribution controller may determine conditions where the out-of-network power plant may store energy on the RES’s batteries or other ESS. Using the anticipated energy demand and energy storage determinations made by the machine learning algorithms of the networked energy generation and distribution controller, the energy generation and distribution controller may determine when to purchase power from power plants on the grid or provide storage for contracted out-of-network power plants. The energy generation and distribution controller may communicate with an application located at the out-of-network power plant similarly to an application provided at the controllable loads and storage of the networked power plants. As such, the systems and methods of the present disclosure provide more optimal and consistent energy generation, storage, and distribution of energy generated by REPPs. [00241] FIG. 19 depicts an embodiment of a method 1900 of renewable energy generation, storage, and distribution, which in some embodiments may be implemented with at least some of the components of FIGs. 10 and 11 discussed above. As discussed below, some embodiments make technological improvements to REPPs. The method 1900 is described as being performed by the energy generation, storage, and distribution engine 1104 included on the networked energy generation, storage, and distribution controller 1002/1100. Furthermore, it is contemplated that other computer systems in the networked energy generation, storage, and distribution system 1000 may include some or all the functionality of the networked energy generation, storage, and distribution engine 1104. As such, some or all of the steps of the method 1900 may be performed by other actors in the energy generation, storage, and distribution system 1000 and still fall under the scope of the present disclosure. Furthermore, and as mentioned above, the networked energy generation, storage, and distribution controller 1002/1100 may include one or more processors or one or more servers, and thus the method 1900 may be distributed across the those one or more processors or the one or more servers. [00242] The method 1900 may begin at block 1902 where one or more metrics for different time periods of a forecast horizon are determined. In an embodiment, at block 1902, the energy generation, storage, and distribution controller 1104 may determine one or more metrics for different time periods of a forecast horizon. The one or more metrics may relate to sending energy generated by a first renewable energy system (RES) to: (1) an energy storage system (ESS), (2) a power grid including one or more loads, and (3) and one or more behind-the-meter loads. In some embodiments, the one or more metrics may include the opportunity cost/price associated with the one or more components. However, in other embodiments, the one or more metrics may include other information that would be apparent to one of skill in the art in possession of the present disclosure. FIG.11 and the corresponding description of FIG.2 of the U.S. Patent Application No. 17/668,258 schematically illustrates the opportunity cost/price method that is an example of the method 1900, in accordance with some embodiments of the present disclosure and incorporated by reference in its entirety. [00243] The method 1900 may proceed to block 1904 where (1) the ESS, (2) the power grid, and (3) the one or more behind-the-meter loads are prioritized. In an embodiment at block 304, the energy generation, storage, and distribution controller 1104 may prioritize the ESS (e.g., ESS 1007a or 1007b), the power grid 1010 (e.g., load 1014a, 1014b, and 1014c) and the one or more behind-the-meter loads (e.g., controllable loads 1008a and 1008b). The prioritization may is based on one or more of: (1) the one or more metrics determined, (2) a state of charge of the ESS during the forecast horizon, (3) one or more limits related to energy requirements of the power grid during the forecast horizon, (4) one or more limits related to energy requirements of the one or more behind-the-meter loads during the forecast horizon, (5) a limit on the interconnect between the power grid and the RES, or any other information that would be apparent to one of skill in the art in possession of the present disclosure. For example, an order of priority for the multiple loads (e.g., POI, ESS, hydrogen production system, etc.) may be determined based on their respective priority price/cost. The controller 1002 or computer may organize the order of priority for the different loads in a descending order such that the process, ESS, or the power grid, that is associated with the highest opportunity costs/prices has the highest priority. In various embodiments, the prioritization may be determined based on the determinations of the energy generation, storage, and distribution predictive algorithm 1105.The method 1900 may proceed to block 1906 where instructions to deliver power generated by the first RES to at least one of: (1) the ESS, (2) the power grid, or (3) the one or more behind-the-meter loads based on the prioritization are generated and provided. In an embodiment, at block 1906, the energy generation, storage, and distribution engine 1104 may generate and provide instructions to deliver power generated by the first RES to at least one of: (1) the ESS, (2) the power grid, or (3) the one or more behind-the-meter loads based on the prioritization. The instructions may be generated based on determining the total generated energy available in the next time interval or time period. The next time interval may be the upcoming second, minute, hour, day, week, month and the like. In some cases, the total generated energy available may be the sum of the energy expected to be generated by the RES (e.g., power plants 1006a or 1006b and the energy expected to be delivered to the grid 1010 by a remote renewable energy source (e.g., remote wind resource such as power plant 1012). It could also include the amount of energy stored in the ESS 1007a and 1007b. The total generated energy available for the future time interval can be estimated using any suitable method or technique. For instance, the total generated energy available may be estimated using models or daily and/or annual production forecasts as described above. In some embodiments, the energy generation, storage, and distribution predictive algorithm 1105 may perform the determination of the instructions by performing artificial intelligence/machine learning algorithms. [00244] The method 1900 may proceed to block 1908 where instruction for a controllable load to adjust energy requirements based on the prioritization are generated and provided. In an embodiment, at block 1908, the energy generation, storage, and distribution engine 1104 may generate and provide instructions to a controllable load on the grid or behind the meter based on the prioritization. The instructions generated for controllable loads power demand adjustment may be generated based on determining the total generated energy. In some embodiments, the energy generation, storage, and distribution predictive algorithm 1105 may perform the determination of the instructions. [00245] In a specific example of method 1900, the current state of charge of the ESS 1007a or 1007b may be high (e.g., 85%). It may be 10 am on a relatively hot and sunny day and the interconnect between the power grid 1010 and the RES may low. As such, for a solar RES, energy production will be high in the next few hours and energy demand on the grid will be high due to the heat. However, there may be an abundance of solar energy being produced by other producers that is being provided to the grid. As such, the grid may not require as much energy than what the interconnect can provide and the generation of power may be high. Because the ESS has a state of charge of 85%, only a small amount of power can be provided to the ESS. As such, the behind- the-meter controllable load may be designated to have high priority for the next forecast horizon, the ESS having next priority, and the grid low priority. Instructions may be generated and provided to the controllable loads to ramp up energy consumption and to the RES to allocate energy to the ESS, controllable load, and the grid, accordingly. Once the ESS is filled, the priority may change and further adjustments may be made to the energy consumption and distribution to the controllable load and the grid [00246] At another forecast horizon, such as right after sunset when it is still very hot but now power production of the solar plant is low and the amount of power being delivered by third-party solar plants that make up a relatively high percentage of the power to the grid is low, the priority may change where the grid has highest priority because the energy prices are expensive, the behind-the-meter controllable load is medium priority, and the ESS is low priority because now the ESS has to provide the power. As such, adjustments to energy distribution and consumption may be made to accommodate the new forecast horizon. As such, factors such as the interconnection to the grid, the state of charge of ESS, and other factors may contribute to the prioritization. [00247] FIG. 19 and its corresponding description in U.S. Patent No. 12,119,646 shows an example of a priority order method, in accordance with some embodiments, FIG. 4 and its corresponding description in U.S. Patent No. 12,119,646 shows an example of a method with priority order of hydrogen production, POI and ESS in the descending order, and FIG. 5 and its corresponding description in U.S. Patent No. 12,119,646 shows an example of a combination of methods, each of which is incorporated by reference in there entity. [00248] In some embodiments, a system, includes one or more processors; and memory storing instructions that when executed by the one or more processors cause the one or more processors to effectuate operations, comprising: a) determining one or more metrics for different time periods of a forecast horizon, wherein the one or more metrics relate to sending energy generated by a first renewable energy system (RES) to: (1) an energy storage system (ESS), (2) a power grid including one or more loads, and (3) and one or more behind-the-meter loads; b) prioritizing: (1) the ESS, (2) the power grid, and (3) the one or more behind-the-meter loads, wherein the prioritization is based on one or more of: (1) the one or more metrics determined in (b), (2) a state of charge of the ESS during the forecast horizon, (3) one or more limits related to energy requirements of the power grid during the forecast horizon, or (4) one or more limits related to energy requirements of the one or more behind-the-meter loads during the forecast horizon; and c) generating and providing instructions to deliver power generated by the first RES to at least one of: the ESS, the power grid, or the one or more behind-the-meter loads based on the prioritization. [00249] In some embodiments, a system includes a controller configured to be communicatively coupled to a renewable energy system (RES), an energy storage system (ESS), and an electric power grid. The controller is configured to determine at least one metric for a forecast horizon, the at least one metric associated with sending energy generated by the RES to: (1) the ESS, (2) the electric power grid, and (3) at least one behind-the-meter load. The controller is also configured to identify a priority, from a plurality of priorities, for each of (1) the ESS, (2) the electric power grid, and (3) the at least one behind-the-meter load, based on at least one of: (1) the at least one metric, (2) a state of charge of the ESS during the forecast horizon, (3) at least one limit related to an energy parameter of the electric power grid during the forecast horizon, (4) at least one limit related to an energy parameter of the at least one behind-the-meter load during the forecast horizon, or (5) a comparison of cost values for the ESS, the electric power grid, and the at least one behind-the-meter load. The controller is also configured to cause delivery of electrical power generated by the RES to at least one of (1) the ESS, (2) the electric power grid, or (3) the at least one behind-the-meter load, based on the plurality of priorities. [00250] In some implementations, the at least one behind-the-meter load includes a controllable load, and the controller is further configured to provide, based on the plurality of priorities, instructions to the controllable load to increase or decrease an energy parameter of the controllable load. The controllable load can include at least one of a data center, an artificial intelligence (AI) training center, a cryptocurrency miner, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a water treatment plant, an industrial process heater, or a thermal battery. [00251] In some implementations, the electric power grid includes a grid controllable load, and the controller is further configured to provide, based on the plurality of priorities, instructions to the grid controllable load to increase or decrease an energy parameter of the grid controllable load. [00252] In some implementations, the identifying of the priority, from the plurality of priorities, for each of (1) the ESS, (2) the electric power grid, and (3) the at least one behind-the-meter load, is further based on a predictive algorithm implemented using machine learning. The predictive algorithm can include at least one of a model predictive control (MPC), a model-based reinforcement learning (MBRL), an adaptive model predictive control (AMPC), or a multi-modal time-series forecasting model. [00253] In some implementations, the causing delivery of the electrical power generated by the RES to at least one of (1) the ESS, (2) the electric power grid, or (3) the at least one behind-the- meter load is further based on a predictive algorithm implemented using machine learning. The predictive algorithm can include at least one of a model predictive control (MPC), a model-based reinforcement learning (MBRL), an adaptive model predictive control (AMPC), or a multi-modal time-series forecasting model. Systems and Methods Utilizing AC Overbuilt Renewable Electric Generation Resource with Storage Device and Controllable Loads [00254] Some aspects of the present disclosure include a process including converting, by at least one first power inverter coupled between a renewable energy source (RES) and a grid interconnection point on an electric grid, RES direct current (DC) electric power to RES alternating current (AC) electric power, wherein an aggregate output capacity of the at least one first power inverter is sized to exceed a point of grid interconnect (POGI) limit; converting, by at least one second power inverter coupled (i) between an energy storage system (ESS) and the grid interconnection point, and (ii) between the at least one first power inverter and the grid interconnection point, RES AC electric power to ESS DC electric power when charging the ESS with RES AC electric power; converting, by the at least one second power inverter, ESS DC electric power to ESS AC electric power when discharging the ESS AC electric power to the electric grid; and while supplying a first portion of the RES AC electric power to the electric grid, diverting a second portion of the RES AC electric power to the at least one second power inverter and a third portion of the RES AC electric power to a controllable load coupled (i) between the at least one first power inverter and the grid interconnection point, and (ii) between the at least one second power inverter and the grid interconnection point, wherein the second portion and the third portion is in an amount sufficient to avoid supplying RES AC electric power to the electric grid in excess of the POGI limit. [00255] Some aspects of the present disclosure include a tangible, non-transitory, machine- readable medium storing instructions that when executed by a data processing apparatus cause the data processing apparatus to perform operations including the above-mentioned process. [00256] Some aspects of the present disclosure include a system having one or more processors; and memory storing instructions that when executed by the processors cause the processors to effectuate operations of the above-mentioned process. [00257] In recent years, there has been a significant surge in the adoption of renewable electricity generation resources, notably solar photovoltaic (PV) and wind power generators. However, the inherent variability of solar and wind generation, influenced by natural and meteorological conditions, poses challenges to grid stability, including frequency and voltage deviations. As renewable electric generation resources begin to supply a larger portion of the electrical grid and replace known base-load units such as coal-fired and nuclear-powered plants, a host of technical challenges arises. These include grid interconnection, power quality, reliability, stability, protection, and generation dispatch and control. The intermittent nature of solar and wind generation, coupled with rapid fluctuations in output, makes the integration of energy storage devices, such as battery energy storage systems (BESS), an attractive proposition. This integration aims to enhance grid compatibility by smoothing fluctuations and improving the predictability of energy supply from renewable sources. Known renewable energy resources typically exhibit low capacity factors, typically ranging from 15% to 45% depending on location and weather patterns. When these resources replace known fossil-fired baseload power plants, they often underutilize existing transmission infrastructure. This may necessitate the construction of new transmission infrastructure, a process fraught with challenges including obtaining permits, thus increasing costs per megawatt-hour produced and introducing delays and risks to the incorporation of renewable generation into existing grids. [00258] Co-locating renewable generation and energy storage can reduce costs associated with site preparation, permitting, and installation. Moreover, tax benefits may accrue, especially when storage is exclusively charged from on-site renewables, minimizing transmission losses. Energy storage devices offer further benefits, such as arbitrage—charging during low-price periods and discharging during peak demand hours—and load-leveling to optimize generation resource dispatch. However, challenges like battery degradation at full capacity and the need for ancillary services provision complicate their utilization. [00259] Several factors can influence the effective utilization of a BESS. Lithium-based batteries, commonly used in such systems, are prone to accelerated degradation when operating at or near full charge capacity. Grid operators overseeing the deployment of integrated renewable electric generation and storage facilities may stipulate specific battery state of charge (SOC) requirements at particular times throughout the day. SOC represents the percentage of a battery's full capacity available for discharge. Once a battery reaches 100% SOC, it becomes incapable of efficiently absorbing sudden increases in electric power output from associated renewable sources. This scenario may necessitate the curtailment of excess power generation, typically achieved through techniques like clipping in a power inverter, to prevent undesirable impacts on the electrical grid. [00260] Additional factors influencing the effective utilization of a BESS encompass its capacity to deliver and be remunerated for offering ancillary services. Ancillary services play a critical role in maintaining the reliability of the electricity grid by ensuring that frequency, voltage, and power load remain within predefined thresholds. These services encompass various categories, including frequency maintenance (to fulfill demands for spinning reserve, energy balancing, and sheddable loads), voltage compensation (for addressing power factor correction and mitigating energy losses during transport), operational management (encompassing grid monitoring, feed-in management, and redispatch), and supply restoration (facilitating swift grid restarts following blackouts). [00261] The inherent variability and unpredictability of renewable energy resources, such as wind and solar generation, amplify the demand for diverse ancillary services, thereby influencing the scheduling and pricing dynamics of these services. However, if renewable energy producers are solely incentivized based on energy generation, they may lack motivation to provide ancillary services, potentially undermining grid stability and reliability. [00262] An electrical energy generation resource can be linked with transmission resources of an electrical grid at a point of grid interconnection (POGI)., that typically operates at a voltage that is optimal for transmitting electric power over long distances with minimal transmission losses. To uphold reliability and safeguard transmission resources, a POGI limit is established for each electrical energy generation resource, delineating the maximum power that can be supplied to a transmission resource. [00263] To enhance the revenue potential from a photovoltaic energy generation resource in tandem with associated transmission resources of predetermined cost, it is common practice to oversize the aggregate output of a photovoltaic array or other renewable energy source (RES) relative to the POGI limit. This strategic move is motivated by the sporadic occurrence of peak photovoltaic generation, attributable to various factors such as adverse weather conditions, solar conditions, panel cleanliness, PV panel aging, and elevated ambient air temperatures diminishing PV panel output. [00264] While oversizing the photovoltaic array facilitates increased power sales over the year, it concurrently escalates the need to curtail excess power during peak irradiance periods, often accomplished through inverter clipping. Regulations are instrumental in shielding the electric grid from potential failures induced by circuit overloads, transmission line overloads, transformer strains, or instances necessitating circuit breakers to disconnect an over-generating facility. Adherence to such regulations is typically ensured by equipping inverters positioned between a photovoltaic array and a transmission system with a total output capacity equal to the POGI limit, along with a slight allowance for electrical losses between the inverters and the grid interconnection point. [00265] Known renewable generation resources are typically constrained by their capacity factors and load matching capability, which are intricately linked to the availability of the primary driving resource, such as solar irradiance or wind. Owing to their low capacity factors and restricted time availability, known renewable generation resources often fail to fully utilize transmission resources. This underutilization poses a substantial challenge for utilities, given the considerable cost and complexity associated with expanding transmission infrastructure. [00266] In light of these challenges, there is a pressing need for advancements in renewable electrical energy generation resources and energy storage facilities. Additionally, there is a demand for sophisticated control methods to manage these facilities effectively. Furthermore, there is a necessity for streamlined processes to facilitate power delivery transactions for the outputs generated by such facilities. [00267] Systems and methods of the present disclosure provide a renewable energy source (“RES”) (e.g., solar, wind, etc.) and energy storage system (“ESS’) facility or plant, where the combination may be referred to here as RES-ESS or a RES-ESS facility (of which a photovoltaic plus storage or “PV+S” facility is a subset). In various embodiments, the RES-ESS may be coupled directly with a controllable load. As such the controllable load may be defined as being behind- the-meter. The controllable load may be correlated or uncorrelated with a load on the grid. In some embodiments, the controllable load may be on the grid. A RES-ESS facility can reach a desired SOC by charging the ESS with power produced by the RES. In certain embodiments, a RES-ESS facility will reach the desired SOC by prioritizing charging at times when RES generation is high. For example, an ESS may be charged more when more RES generation is available, and an ESS may be charged less (or not at all) when RES generation is limited. The ESS may be discharged when RES generation is limited or unavailable. Further still, to reach the SOC and provide a grid output that is at or near the POGI, the RES may be overbuilt even further and the controllable load may be used to consume excess power generated by the RES. [00268] As discussed in the previous paragraph, in addition to charging the ESS, or supplying energy to the electrical grid, the renewable energy sources (RESs) may also be used to serve other types of electric loads or energy-consuming processes that may or may not be off-grid. For example, the RESs may provide energy directly to a load such as an industrial process (e.g., production of “green” hydrogen, production of ammonia, metal smelting, cryptocurrency mining, “vertical” farming, powering server farms, artificial intelligence training, a data center, water purification and glass production, etc.), without passing through the electrical grid (e.g., off-grid load or also referred to herein as behind-the-meter loads). However, the multiple types of electrical loads or industrial processes connected to the RESs may not be correlated where the value of directing power to one or more of the electrical loads may vary over time, and such variations may not be correlated to one another. Therefore, the overbuilt RES-ESS facility may be further overbuilt by taking advantage of the power requirements of the amount of energy and power amongst the multiple uncorrelated loads, which may also be controllable by using those loads to stabilize the SOC of the ESS and the power on the grid interconnection point. [00269] In various embodiments, loads that are controllable loads may include loads where a controller, described herein, can change the demand by either increasing or decreasing power demand at that load. As such, the present disclosure considers both adjusting energy allocation from the RES and adjusting energy demand from one or more of the controllable loads that can either be on the grid or behind-the-meter. In various embodiments, the behind the meter controllable loads allow an energy producer to increase size and performance of the RES. For example, the RES may be built to generate a larger capacity than what the RES can provide to the grid. This provides economy of scale cost and performance advantages over a system without the behind-the-meter controllable loads. When generation is not at peak (e.g. clouds or early morning or late afternoon or low wind etc.) or when the energy storage system included with the RES is full, the excess energy is absorbed by the behind-the-meter load in addition to the ESS. Also, when RES generation is low the oversized system can deliver more power to more critical or valuable loads on the grid using the stored charge on the ESS and fulfill the bandwidth of what the RES can provide to the grid or provide more load or power to the energy storage system. As such, the RES- ESS system may be designed for better performance and lower cost, i.e., overall system performance is better such that a more consistent energy supply, capacity, or other ancillary services are provided to the grid. [00270] In various embodiments of the present disclosure, the controller may include a predictive algorithm such as, for example, model predictive control (MPC), model-based reinforcement learning (MBRL), adaptive model predictive control (AMPC) or other predictive algorithm/machine learning algorithm. MPC may be implemented with a long short-term memory (LSTM), state space model, or transformer architecture. Some embodiments may use a multi- modal time-series forecasting model (e.g., accounting for weather, wind production, solar production, grid demand, and value of behind-the-meter load outputs), examples including: autoregressive–moving-average (ARMA) models (e.g., Seasonal ARIMA); autoregressive integrated moving average (ARIMA) model; generalized autoregressive conditional heteroskedasticity (GARCH) models; vector autoregression models, Holt-Winters exponential smoothing; state space models; and Kalman filters. The predictive algorithm may predict a priority in a future time interval, and based on the prioritization and total predicted energy storage and generation, the predictive algorithm may determine any demand adjustments on the controllable loads and allocate energy and power to the various loads (on or off the grid) or the ESS based on a prioritization. [00271] The energy generation, storage and distribution controller may include predictive algorithms for balancing energy distribution to the controllable loads. For example, the energy generation and distribution controller may ingest data from various data sources (e.g., a weather forecast, an event schedule, a calendar, historical energy use data, sensor data or other data sources that would be apparent to one of skill in the art in possession of the present disclosure). In other embodiments, the data sources may include state of charge data or analytics of other RES and their ESS. These other RESs may include energy storage systems that are not on the network and may be those of competitors. As such, a prediction of how much energy storage another RES may be beneficial as to anticipate how much energy will be available for the grid at a certain time so that control of the ESSs can be managed. [00272] The energy generation and distribution controller, using the predictive algorithms trained on historical or simulator data, may then anticipate energy demand for uncontrollable loads on the grid as well as an energy supply on the power plants. Based on the anticipated energy demand and the energy supply, the energy generation and distribution controller may determine whether one or more energy balancing conditions associated with a respective controllable load are satisfied to either increase power distribution to that controllable load or reduce power distribution to that controllable load. For example, in exchange for a better rate on its energy price or some other energy distribution factor that the controllable load desires, the controllable load may allow the energy generation, storage, and distribution controller to reduce energy consumption at that controllable load to reallocate the RES’s energy supply to loads that are not controllable and that may pay a higher premium, are higher prioritized based on various factors (e.g., more necessary / high priority infrastructure such as a hospital, a water distribution plant, critical communication infrastructure, or the like). Furthermore, the controllable loads themselves may be adjusted to reduce or increase energy consumption. [00273] Similarly, the controllable load may include an energy storage system where the energy generation, storage, and distribution controller may increase or decrease power distribution to the energy storage device. Furthermore, more optimal decisions can be made of which energy storage device in the ESS to store energy. For example, a zinc air battery may be charged when cheap power is available while a lithium-ion battery may be charged when more expensive power is available, faster response times are anticipated, or when other beneficial conditions are present that would be apparent to one of skill in the art in possession of the present disclosure. As such, a type of energy storage device or other factors associated with the energy storage device may be used to determine when a particular energy storage device is to be charged or how much charge a particular energy storage device is to receive. [00274] In other embodiments, the energy generation and distribution controller may also use the anticipated energy demand and the energy supply to balance the storage of energy generated by the RESs on associated batteries. For example, the energy generation and distribution controller may determine the amount of energy stored on each battery and how those batteries in the power plants are going to distribute the energy in an optimized manner. For example, to preserve the life expectancy of a battery, under normal conditions, that battery may not be completely filled or completely drained as doing so decreases the life expectancy of the battery. However, if the anticipated energy supply and demand indicate a condition where it is more beneficial to fully charge a battery or fully discharge a battery than to consider the life expectancy of the battery, the controller may cause the battery to be fully charged in anticipation of the future event. For example, if there is an anticipated event that requires a high demand of energy, the energy generation and distribution controller may fully charge the battery. In other embodiments, the networked energy generation and distribution controller may tier the batteries such that a first battery distributes energy based on a first condition, a second battery distributes energy based on a second condition, and a third battery distributes energy based on a third condition. These the conditions may be prioritized based on different levels. For example, the third battery may only distribute energy if the price of energy is above a certain threshold. [00275] In yet other embodiments of the present disclosure, the energy generation and distribution controller may determine when to provide energy storage to power plants that are not included in the RESs such as power plants that are on the grid. The energy generation and distribution controller may determine conditions where the out-of-network power plant may store energy on the RES’s batteries or other ESS. Using the anticipated energy demand and energy storage determinations made by the machine learning algorithms of the networked energy generation and distribution controller, the energy generation and distribution controller may determine when to purchase power from power plants on the grid or provide storage for contracted out-of-network power plants. The energy generation and distribution controller may communicate with an application located at the out-of-network power plant similarly to an application provided at the controllable loads and storage of the networked power plants. As such, the systems and methods of the present disclosure provide more optimal and consistent energy generation, storage, and distribution of energy generated by RESs. [00276] FIG. 20 illustrates an example energy generation, storage, and distribution system 2000 in accordance with one or more embodiments. The energy generation, storage, and distribution system 2000 may include an energy generation, storage, and distribution controller 2002; a network 2004; an integrated RES-ESS system 2006 that includes an RES 2009, an ESS 2007, a controllable load 2008, an inverter 2016, and an inverter 2018; a grid 2010; one or more data sources 2011; a load 2014a; a load 2014b; and a controllable load 2014c. The load 2014a, the load 2014b, and the controllable load 2014c may be electrically coupled to the grid 2010. The load 2014a, the load 2014b, and the controllable load 2014c may be remote from each other and have separate power requirements. The load 2014a may have a first power delivery profile which details power requirements for the load 2014a at different times. The load 2014b may have a second power delivery profile which details power requirements for the load 2014b at different times. The controllable load 2014c may have a third power delivery profile which details power requirements for the controllable load 2014c at different times. In some embodiments, the grid 2010 may be a utility grid owned and operated by a single utility or system operator. In other embodiments, the grid 2010 may be a plurality of electrical connections allowing for the transmission of power from the RES-ESS system 2006 to the load 2014a, the load 2014b, and the controllable load 2014c. [00277] The RES 2009 may include a first renewable energy power plant (REPP). Examples of REPPs include, but are not limited to, solar plants, wind plants, geothermal plants, and biomass plants. The RES-ESS may include an energy storage system (ESS) 2007. An example of an ESS is a battery. A battery-based ESS may be called a battery ESS or BESS. The RES 2009 may have a first power output that varies over time. [00278] In some embodiments, the RES may be coupled to an inverter 2016. The inverter 2016 may convert DC power generated by the RES to AC power provided to the grid 2010 at a grid interconnection point. The grid interconnection point has a point of grid interconnect (POGI) limit. The inverter 2016 has an AC power output limit that is greater than the POGI limit. The RES- ESS system 2006 may include an inverter 2018 that may be coupled between the ESS 2007 and the grid 2010 and coupled between the inverter 2016 and the grid 2010. The inverter 2018 may be bidirectional such that it convert RES AC power outputted from the inverter 2016 to DC power that can charge the ESS 2007. Similarly, the inverter 2018 may convert ESS DC power to AC power that can be outputted to the grid 2010. In various embodiments, the inverter 2018 may be optionally build to have an AC power output that is greater than the POGI. The controllable load may be coupled between the inverter 2016 and the grid 2010 and the inverter 2018 and the grid 2010. [00279] The RES 2006 may communicate with the networked energy generation, storage, and distribution controller 2002 via a network 2004. Similarly, the controllable loads 2008 and 2014 and the RES 2009, and the ESS 2007 may communicate with the networked energy generation, storage, and distribution controller 2002 via a network 2004. Further still, the networked energy generation, storage, and distribution controller 2002 may communicate with data sources 2011 via the network 2004. The data sources may include sensors, weather data, local schedules, or any other system data or third-party information that would be apparent to one of skill in the art in possession of the present disclosure. The network 2004 may be any local area network (LAN) or wide area network (WAN). In some embodiments, the network is the internet. In other embodiments, the network is a private communications network. The energy generation, storage, and distribution controller 2002 may include a processor and a memory. [00280] The energy generation, storage, and distribution controller 2002 may control the RES 2009 and cause the RES to direct power to the ESS, the controllable load 2008, and the grid 2010. The controller 2002 may also control the ESS 2007 on when to charge or discharge power received from the inverter 2018 from the RES 2009 or in some embodiments from the grid 2010. The controller 2002 may also control the power demand at the controllable load 2008 and 2014c. While a specific system is described, one of skill in the art in possession of the present disclosure will recognize that other variations, components, multiple RESs, ESSs, and controllable loads may be contemplated without deviating from the scope of the present disclosure. [00281] In some embodiments, one or more functionalities of the system 2000 of FIG.20 can be combined with or replaced by one or more functionalities of any of system 100 of FIG. 1, system 400 of FIG.4, and/or system 1300 of FIGs.13-17. Alternatively or in addition, one or more functionalities of the controller 2002 of FIG. 20 can be implemented using one or more functionalities / features of any of controller 200 of FIG.2, controller 1102 of FIG.11, controller 1802 of FIG.18, and/or controller 2102 of FIG.21. Alternatively or in addition, the system 2000 of FIG.20 can be configured to perform one or more of method 300 of FIG.3, method 500 of FIG. 5, method 600 of FIG.6, method 700 of FIG.7, method 800 of FIG.8, method 1200 of FIG.12, method 1900 of FIG.19, or method 2200 of FIG.22. [00282] FIG. 21 of the present disclosure illustrates an embodiment of a energy generation, storage, and distribution controller 2100 that may be the energy generation, storage, and distribution controller 2002 discussed above with reference to Fig. 20. While described as a standalone system, those skilled in the art will appreciate that the energy generation, storage, and distribution controller 2100 may distributed across many computing devices such as in a cloud environment. In the illustrated embodiment, the networked energy generation, storage, and distribution controller 2100 includes a chassis 2102 that houses the components of the energy generation, storage, and distribution controller 2100, only some of which are illustrated in Fig.21. For example, the chassis 2102 may house a processing system (not illustrated) and a non-transitory memory system (not illustrated) that includes instructions that, when executed by the processing system, cause the processing system to provide an energy generation, storage, and distribution engine 2104 that is configured to perform the functions of the energy generation and distribution engines or the networked energy generation, storage, and distribution controller discussed below. In the specific example illustrated in Fig. 21, the energy generation, storage, and distribution engine 2104 may include an energy generation, storage, and distribution predictive algorithm 2105 that is configured to perform the functions of the energy generation, storage, and distribution predictive algorithms discussed herein. In various embodiments, the energy generation, storage, and distribution predictive algorithm 2105 may ingest data provided by data sources and anticipates energy demand and energy supply, or any other functionality discussed herein. In various embodiments, the energy generation, storage, and distribution predictive algorithm 2105 may include a network simulator to model behavior, which may predict the components being incorporated into the grid by running simulations due to lack of historical data. In other examples, the energy generation, storage, and distribution predictive algorithm 2105 may include model predictive control or other predictive algorithms/machine learning algorithms that would be apparent to one of skill in the art in possession of the present disclosure. [00283] The chassis 2102 may further house a communication system 2106 that is coupled to the energy generation, storage, and distribution engine 2104 (e.g., via a coupling between the communication system 2106 and the processing system) and that is configured to provide for communication through the communication network 2004 as detailed below. The chassis 2102 may also house a storage system 2108 that is coupled to the energy generation, storage, and distribution engine 2104 through the processing system and that is configured to store the rules or other data utilized by the networked energy generation, storage, and distribution engine 2104 to provide the functionality discussed below. While an energy generation, storage, and distribution controller 2100 has been illustrated, one of skill in the art in possession of the present disclosure will recognize that other networked energy generation and distribution controller (or other devices operating according to the teachings of the present disclosure in a manner similar to that described below for the energy generation, storage, and distribution controller 2100) may include a variety of components and/or component configurations for providing known computing device functionality, as well as the functionality discussed below, while remaining within the scope of the present disclosure as well. [00284] FIG. 22 depicts an embodiment of a method 2200 of renewable energy generation, storage, and distribution, which in some embodiments may be implemented with at least some of the components of FIGs. 20 and 21 discussed above. As discussed below, some embodiments make technological improvements to RES-ESS systems that are overbuilt. Some or all of the steps of the method 2200 may be performed by other actors in the energy generation, storage, and distribution system 2000 and still fall under the scope of the present disclosure. Furthermore, and as mentioned above, the networked energy generation, storage, and distribution controller 2002/2100 may include one or more processors or one or more servers, and thus the method 2200 may be distributed across the those one or more processors or the one or more servers. [00285] The method 2200 may begin at block 2202 where RES direct current (DC) electric power is converted to RES alternating current (AC) electric power. In an embodiment, at block 2202, at least one first power inverter 2016 coupled between the renewable energy source (RES) 2009 and a grid interconnection point on an electric grid 2010 may convert the RES direct current (DC) electric power to RES alternating current (AC) electric power. The aggregate output capacity of the at least one first power inverter 2016 is sized to exceed a point of grid interconnect (POGI) limit. [00286] The method 2200 may proceed to block 2204 where RES AC electric power is converted to ESS DC electric power when charging the ESS with RES AC electric power. In an embodiment, at block 2204 the at least one second power inverter 2018 coupled (i) between the energy storage system (ESS) 2007 and the grid interconnection point on the grid 2010, and (ii) between the at least one first power inverter 2016 and the grid interconnection point on the grid 2010 may convert the RES AC electric power to ESS DC electric power when charging the ESS with RES AC electric power. [00287] The method 2200 may proceed to block 2206 where ESS DC electric power is converted to ESS AC electric power when discharging the ESS AC electric power to the electric grid. In an embodiment, at block 2206, the at least one second power inverter 2018 may convert ESS DC electric power to ESS AC electric power when discharging the ESS AC electric power to the electric grid 2010. [00288] The method 2200 may proceed to block 2208 where while supplying a first portion of the RES AC electric power to the electric grid, a second portion of the RES AC electric power is diverted to the at least one second power inverter and a third portion of the RES AC electric power is diverted to a controllable load. In an embodiment, at block 2208, the controller 2002 may divert a portion of the RES AC electric power from being provided to the grid 2010 to the inverter 2018 or the controllable load 2008. As discussed above, the controllable load is coupled (i) between the at least one first power inverter 2016 and the grid interconnection point on the grid 2010, and (ii) between the at least one second power inverter 2018 and the grid interconnection point. The second portion and the third portion is in an amount sufficient to avoid supplying RES AC electric power to the electric grid 2010 in excess of the POGI limit. [00289] In various embodiments of method 2200, the controller may provide instructions to the controllable load 2008 or the controllable load 2014c to adjust their load (e.g., increase or decrease load). This will help balance and allocate power generated by the overbuilt RES-ESS system 2006. For example, the controllable load 2014c may be directed to increase the load when the POGI has not been met but power generation by the RES 2009 is high and the SOC of the ESS 2007 is high. Conversely, when the loads 2014a and 2014b have a demand that is at or near the POGI limit, the controllable load 2014c may be instructed to decrease its demand. [00290] With respect to the controllable load 2008 that is behind the meter, the controller 2002 may direct the controllable load 2008 to increase demand if the SOC of the 2007 is high and the power output of the inverter 2016 is greater than the POGI limit due to the overbuilt RES 2009 generating a lot of power. Conversely, if the SOC of the ESS 2007 is low or the RES 2009 is producing very little to no power and the demand of the grid is high such that ESS 2007 will deplete or come close to falling below a desired SOC, then the controller 2002 may provide instructions to the controllable load 2008 to reduce the power demand. [00291] As such, by adding controllable loads that are included on the grid or in the RES-ESS system, the RES-ESS system may be further overbuilt such that greater economies of scale can be realized. Thus, in certain embodiments of an AC oversized RES-ESS facility, the aggregate output capacity of the at least one first power inverter is sized to exceed the POGI limit by at least 5%, by at least 35%, by at least 55%, by at least 75%, by at least 100%, by at least 150%, by at least 200% or another threshold specified herein. In certain embodiments, the foregoing minimum thresholds may optionally be capped (where appropriate) by values of (A) 120%, (B) 150%, (C) 200%, or the sum of (i) the POGI limit, (ii) a capacity of the ESS, and (iii) the capacity of the controllable load. In certain embodiments, the aggregate output capacity of the at least one first power inverter is sized to equal a sum of (i) the POGI limit, (ii) a capacity of the ESS, and (iii) the capacity of the controllable load. Technical benefits of an AC overbuilt RES-ESS facility include the ability to provide a higher capacity factor (e.g., 60-90% for an AC overbuilt PV-BESS facility, as compared to a range of perhaps 25-45% for a known PV-BESS facility). Such a facility is capable of delivering more renewable energy with existing transmission resources (which is expensive and time-consuming to build). A lower cost of energy may be attained because fixed development project costs may be amortized over more annual megawatt-hours of production. [00292] As noted above, an AC overbuilt RES-ESS facility is also suitable for providing a high level of fixed firm capacity (e.g., at least 70%, at least 80%, at least 90%, at least 95%, or at least 99% of a POGI limit) for a long duration (e.g., at least 6 hours per day, at least 8 hours per day, at least 12 hours per day, at least 16 hours per day, at least 20 hours per day, or 24 hours per day in certain embodiments). In certain embodiments, long-term weather data may be utilized when sizing an ESS and the at least one first inverter to permit the foregoing capacity and duration thresholds to be achieved with a confidence window of at least 90%, at least 95%, at least 98%, or at least 99% over all foreseeable weather conditions. In certain embodiments, the confidence window corresponds to a number of days per month or per year in which the specified fixed firm capacity and long duration is attained. The ability to provide a high level of fixed firm capacity enables an AC overbuilt RES-ESS facility to replace known baseload assets (e.g., gas-fired, coal- fired, or nuclear power plants) and improve grid stability. Furthermore, predictive algorithms and machine learning can further be used to allocate power and control demand of controllable loads to better balance the system so that optimal power is provided to the grid without overloading the grid or reducing power production. [00293] In some embodiments, a method is disclosed for controlling an integrated renewable energy source, energy storage system (RES-ESS) facility configured to supply electric power to an electric grid at a grid interconnection point. The RES-ESS facility includes a renewable energy source (RES) and an energy storage system (ESS) chargeable with electric power produced by the RES. The RES-ESS facility also includes a controllable load directly connected to the RES-ESS such that the controllable load is between the RES and the grid interconnection point. The RES- ESS facility also has a point of grid interconnect (POGI) limit. The RES has a power output that exceeds the POGI limit and provides excess power to the ESS or the controllable load when the RES generates more power than the POGI limit. [00294] In some embodiments, a method includes providing at least one first power inverter between, and coupled to each of, a renewable energy source (RES) and a grid interconnection point of an electric grid, an aggregate output capacity of the at least one first power inverter sized to exceed a point of grid interconnect (POGI) limit. The method also includes providing at least one second power inverter coupled (i) between an energy storage system (ESS) and the grid interconnection point, and (ii) between the at least one first power inverter and the grid interconnection point, the at least one second power inverter configured to (a) convert AC electric power from a renewable energy source (RES) to DC electric power for the ESS when charging the ESS with AC electric power from the RES, and (b) convert DC electric power from the ESS to AC electric power when discharging the ESS to the electric grid. The method also includes, while supplying a first portion of the AC electric power from the RES to the electric grid, diverting a second portion of the AC electric power from the RES to the at least one second power inverter and a third portion of the AC electric power from the RES to a controllable load coupled (i) between the at least one first power inverter and the grid interconnection point, and (ii) between the at least one second power inverter and the grid interconnection point, the second portion and the third portion being in an amount sufficient to avoid supplying AC electric power from the RES to the electric grid in excess of the POGI limit. Optionally, the method can also include generating a forecast signal comprising a time-dependent forecast of energy production of the RES based at least in part on at least one of (a) data from a sky imaging sensor associated with the RES-ESS facility, (b) data from a satellite imaging sensor, or (c) meteorological data. [00295] In some implementations, the method also includes modifying a power allocation associated with the controllable load based on a prediction generated using a predictive algorithm. The predictive algorithm can include at least one of a model predictive control (MPC), a model- based reinforcement learning (MBRL), an adaptive model predictive control (AMPC), or a multi- modal time-series forecasting model. [00296] In some embodiments, a non-transitory, processor-readable medium storing instructions that, when executed by a processor, cause the processor to control a net load of at least one controllable load (CL). The non-transitory, processor-readable medium also stores instructions to cause the processor to cause delivery of a first portion of electric power from at least one of (1) at least one renewable energy source (RES) or (2) at least one energy storage system (ESS), to the at least one CL. The non-transitory, processor-readable medium also stores instructions to cause the processor to cause delivery of a second portion of the electric power from at least one of (1) the at least one RES or (2) the at least one ESS, to an electric grid. The non-transitory, processor- readable medium also stores instructions to cause the processor to, in response to determining that a grid condition of the electric grid exists without electric power generated by the at least one RES exceeding an aggregated power capacity of the at least one ESS and an aggregated power demand of the at least one CL, cause one of an increase or a decrease to a power demand at the at least one CL. [00297] In some implementations, an amount of power generated by the at least one RES exceeds a point of grid interconnect (POGI) limit by a factor of between about 3 and about 6. [00298] In some implementations, the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00299] In some implementations, the non-transitory, processor-readable medium also stores instructions to cause the processor to operate the at least one RES or the at least one ESS as at least one of a peaker plant for the electric grid or a provider of ancillary services to the electric grid, and the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00300] In some implementations, the at least one CL includes a plurality of CLs, and the non- transitory, processor-readable medium also stores instructions to cause the processor to provide instructions to the plurality of CLs to balance an energy distribution associated with the plurality of CLs, and the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00301] In some implementations, the at least one CL includes at least one of a data center, an artificial intelligence (AI) training center, a cryptocurrency miner, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a water treatment plant, an industrial process heater, or a thermal battery. [00302] In some implementations, the non-transitory, processor-readable medium also stores instructions to cause the processor to modify a correlation of a load profile of the at least one CL with the electric grid. [00303] In some embodiments, a method includes controlling, via a processor, a net load of at least one controllable load (CL), and causing delivery, via the processor, of a first portion of electric power from at least one of (1) at least one renewable energy source (RES) or (2) at least one energy storage system (ESS), to the at least one CL. The method also includes causing delivery, via the processor, of a second portion of the electric power from at least one of (1) the at least one RES or (2) the at least one ESS, to an electric grid. The method also includes, in response to determining that a grid condition of the electric grid exists without electric power generated by the at least one RES exceeding an aggregated power capacity of the at least one ESS and an aggregated power demand of the at least one CL, causing, via the processor, one of an increase or a decrease to a power demand at the at least one CL. [00304] In some implementations, an amount of power generated by the at least one RES exceeds a point of grid interconnect (POGI) limit by a factor of between about 3 and about 6. [00305] In some implementations, the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00306] In some implementations, a ratio of the electric power generated by the at least one RES to an aggregate load of the at least one CL is between about 3 and about 6, and the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00307] In some implementations, the method also includes operating, via the processor, the at least one RES or the at least one ESS as at least one of a peaker plant for the electric grid or a provider of ancillary services to the electric grid. The grid condition can be associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00308] In some implementations, the at least one CL includes at least one of a data center, an artificial intelligence (AI) training center, a cryptocurrency miner, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a water treatment plant, an industrial process heater, or a thermal battery. [00309] In some implementations, the method also includes at least one of: causing delivery of power from the electric grid to the at least one CL; causing modification to a correlation of a load profile the at least one CL with the electric grid; or causing modification to a correlation of (1) at least one peak of a net load profile associated with the at least one CL, with (2) at least one peak of a net load profile associated with the electric grid. [00310] In some implementations, the method also includes (1) causing operation of a powerplant in a first mode as at least one of a baseload, a semi-baseload, a semi-peaker plant, or a peaker plant for the at least one CL; and (2) concurrently with causing the operation of the powerplant in the first mode, causing operation of the powerplant in a second mode as at least one of a peaker plant, a semi-peaker plant, a semi-baseload, a baseload, or a provider of ancillary services for the electric grid. [00311] In some embodiments, a system includes: one or more processors; and memory storing instructions that when executed by the one or more processors cause the one or more processors to effectuate operations, comprising: receiving power data from a power data source; generating, using an energy generation, storage, and distribution predictive algorithm and based on the power data, an anticipated power supply and demand profile; determining, based on the anticipated power supply and demand profile, whether a condition exists to issue a control instruction to one or more controllable power components; and providing, in response to determining that the condition exists, the control instruction associated with the condition to the one or more controllable power components. [00312] In some such implementations, the one or more controllable power components includes a controllable load. [00313] In some embodiments, a renewable energy power plant (REPP) comprises: a renewable energy source (RES); a first meter associated with a first load; a second meter associated with a second load; a controllable load that is behind the first meter and the second meter; a first energy storage system (ESS) electrically coupled to the RES and the first meter; a second ESS electrically coupled to the RES and the first meter through a switch; and a controller configured to: set a first charge/discharge for the first ESS and a second charge/discharge for the second ESS such that the REPP delivers power to the first load for a first time longer than a first production time period when the RES produces power; in response to a first trigger condition being satisfied, actuate the switch such that the second ESS is electrically coupled to the controllable load; set a third charge/discharge for the first ESS such that the REPP delivers power to the first load for a third time longer than a second production time period when the RES produces power; set a fourth charge/discharge for the second ESS such that the second ESS maintains a portion of its charge in reserve for the third load; in response to a second trigger condition being satisfied, actuate the switch such that the second ESS is electrically coupled to the second meter; and set a fifth charge/discharge for the second ESS such that the second ESS maintains a portion of its charge in reserve for the second meter, wherein the RES is tuned to satisfy the power delivery requirements of the first load and maintain the portion of charge of the second ESS. [00314] In some such implementations, the trigger condition being satisfied is based on a predication made by a predictive algorithm. [00315] In some embodiments, a method comprises: setting, by a controller of an renewable power plant (REPP), a first charge/discharge for a first REPP electrical storage system (ESS) and a second charge/discharge for a second REPP ESS such that the REPP delivers power to a first load for a first time longer than a first production time period when an REPP renewable energy source (RES) of the REPP produces power, wherein the first ESS is electrically coupled to the RES and to a first meter, and wherein the second ESS is electrically coupled to the RES and to the first meter through a switch; in response to a first trigger condition being satisfied, actuating the switch such that the second ESS is electrically coupled to the controllable load; setting a third charge/discharge for the first ESS such that the REPP delivers power to the first load for a third time longer than a second production time period when the RES produces power; setting a fourth charge/discharge for the second ESS such that the second ESS maintains a portion of its charge in reserve for the third load; in response to a second trigger condition being satisfied, actuating the switch such that the second ESS is electrically coupled to the second meter; and setting a fifth charge/discharge for the second ESS such that the second ESS maintains a portion of its charge in reserve for the second meter, wherein the RES is tuned to satisfy the power delivery requirements of the first load and maintain the portion of charge of the second ESS. [00316] In some embodiments, a system includes: one or more processors; and memory storing instructions that when executed by the one or more processors cause the one or more processors to effectuate operations, comprising: a) determining one or more metrics for different time periods of a forecast horizon, wherein the one or more metrics relate to sending energy generated by a first renewable energy system (RES) to: (1) an energy storage system (ESS), (2) a power grid including one or more loads, and (3) and one or more behind-the-meter loads; b) prioritizing: (1) the ESS, (2) the power grid, and (3) the one or more behind-the-meter loads, wherein the prioritization is based on one or more of: (1) the one or more metrics determined in (b), (2) a state of charge of the ESS during the forecast horizon, (3) one or more limits related to energy requirements of the power grid during the forecast horizon, or (4) one or more limits related to energy requirements of the one or more behind-the-meter loads during the forecast horizon; and c) generating and providing instructions to deliver power generated by the first RES to at least one of: (1) the ESS, (2) the power grid, or (3) the one or more behind-the-meter loads based on the prioritization. [00317] In some such implementations, the one or more behind-the-meter loads includes a controllable load, and wherein the operations further comprise: providing, based on the prioritization, instructions to the controllable load to increase or decrease energy requirements. [00318] In some such implementations, the one or more loads included on the power grid includes a grid controllable load, and wherein the operations further comprise: providing, based on the prioritization, instructions to the grid controllable load to increase or decrease energy requirements. [00319] In some such implementations, the prioritizing and the generating and the providing instructions is performed by a predictive algorithm using machine learning. [00320] In some embodiments, a method comprises: converting, by at least one first power inverter coupled between a renewable energy source (RES) and a grid interconnection point on an electric grid, RES direct current (DC) electric power to RES alternating current (AC) electric power, wherein an aggregate output capacity of the at least one first power inverter is sized to exceed a point of grid interconnect (POGI) limit; converting, by at least one second power inverter coupled (i) between an energy storage system (ESS) and the grid interconnection point, and (ii) between the at least one first power inverter and the grid interconnection point, RES AC electric power to ESS DC electric power when charging the ESS with RES AC electric power; converting, by the at least one second power inverter, ESS DC electric power to ESS AC electric power when discharging the ESS AC electric power to the electric grid; and while supplying a first portion of the RES AC electric power to the electric grid, diverting a second portion of the RES AC electric power to the at least one second power inverter and a third portion of the RES AC electric power to a controllable load coupled (i) between the at least one first power inverter and the grid interconnection point, and (ii) between the at least one second power inverter and the grid interconnection point, wherein the second portion and the third portion is in an amount sufficient to avoid supplying RES AC electric power to the electric grid in excess of the POGI limit. [00321] In some embodiments, a system comprises: at least one renewable energy source (RES) configured to electrically couple to a grid interconnection point of an electric grid, an aggregated alternating current (AC) power output capacity of the at least one RES exceeding a point of grid interconnect (POGI) limit of the grid interconnection point; at least one energy storage system (ESS) that is electrically coupled to the grid interconnection point and the at least one RES and that has an aggregated power capacity that is less than the aggregated AC power output capacity of the at least one RES; and a controller that is communicatively coupled with at least one controllable load (CL), the at least one ESS, and the at least one RES, the controller configured to control a net load profile of the at least one CL such that the net load profile of the at least one CL includes at least one value between a maximum net load value and a minimum net load value of the at least one CL, the controller further configured to: provide a first instruction to at least one of the at least one RES or the at least one ESS to provide a first portion of electric power generated by the at least one RES or stored by the at least one ESS to the at least one CL up to an aggregated power demand; provide a second instruction to at least one of the at least one RES or the at least one ESS to provide a second portion of electric power to the electric grid in response to (A) electric power generated by the at least one RES exceeding an aggregated power capacity and the aggregated power demand, or (B) the controller, using a predictive algorithm and power data, determining that a grid condition exists in a power system forecast; and in response to determining that the grid condition exists without the electric power generated by the at least one RES exceeding the aggregated power capacity and the aggregated power demand, providing a third instruction to the at least one CL to one of increase or decrease a power demand at the at least one CL. [00322] In some such implementations, the aggregated AC power output capacity of the at least one RES exceeds the POGI limit by a factor of between about 3 and about 6. [00323] In some such implementations, the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00324] In some such implementations, the system has an associated capacity factor of at least about 60%. [00325] In some such implementations, a ratio of the power generated by the at least one RES to an aggregate load of the at least one CL is between about 3 and about 6. [00326] In some such implementations, the controller is further configured to operate the at least one RES or the at least one ESS as at least one of a peaker plant for the electric grid or a provider of ancillary services to the electric grid, and the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00327] In some such implementations, the at least one CL includes a plurality of CLs, and the controller is further configured to provide instructions to the plurality of CLs to balance an energy distribution associated with the plurality of CLs. [00328] In some such implementations, the at least one CL includes a data center. [00329] In some such implementations, the at least one CL includes an artificial intelligence (AI) training center. [00330] In some such implementations, the at least one CL includes a cryptocurrency miner. [00331] In some such implementations, the at least one CL includes an electric vehicle (EV) charging station. [00332] In some such implementations, the at least one CL includes a vertical farm. [00333] In some such implementations, the at least one CL includes a hydrogen production facility. [00334] In some such implementations, the at least one CL includes a water treatment plant (including desalination and purification). [00335] In some such implementations, the at least one CL load includes an industrial process heater. [00336] In some such implementations, the at least one CL load includes a thermal battery. [00337] In some such implementations, the controller is further configured to cause delivery of power from the electric grid to the at least one CL load. [00338] In some such implementations, the controller is further configured to select the first instruction such that a correlation of the at least one CL with the electric grid is changed in response to the first instruction. [00339] In some such implementations, the controller is further configured to select the first instruction such that a correlation of (1) at least one peak of a net load profile associated with the at least one CL, with (2) at least one peak of a net load profile associated with the electric grid is changed in response to the first instruction. [00340] In some such implementations, the first instruction is configured to cause a change in a correlation of the at least one CL with the electric grid in response to the at least one first instruction. [00341] In some such implementations, the system is configured to: (1) operate in a first mode as at least one of a baseload, a semi-baseload, a semi-peaker plant, or a peaker plant for the at least one CL, and (2) concurrently with operating in the first mode, operate in a second mode as at least one of a peaker plant, a semi-peaker plant, a semi-baseload, a baseload, or a provider of ancillary services for the electric grid. [00342] In some such implementations, the controller is further configured to provide a fourth instruction to at least one non-renewable energy source (NRES) to cause the at least one NRES to provide a third portion of electric power generated by the at least one NRES to the at least one CL. [00343] In some embodiments, a method of providing power on an RES-ESS-CL system, the method comprises: providing, at a first time and by at least one of an RES or an ESS, power to a point of grid interconnect (POGI) associated with an electric grid, the POGI disposed between at least one CL and the electric grid; providing, at a second time and by the at least one of the RES or the ESS, power to the at least one CL; providing, at a third time, power received from the electric grid at the POGI to the ESS; providing, at a fourth time, power from received from the electric grid at the POGI to the at least one CL; and providing, at a fifth time, no power via the POGI and providing at least one of power from the ESS to the at least one CL, power from the RES to the at least one CL, or power from the RES to the ESS. [00344] In some such implementations, the method further comprises: providing, at the fourth time, power from at least one of the RES or the ESS to the at least one CL. [00345] In some such implementations, the method further comprises: providing, at the fourth time, power from the RES to at least one of the ESS or the at least one CL. [00346] In some such implementations, the providing at the fifth time includes providing (1) power from the ESS to the at least one CL, and (2) one of: power from the RES to the at least one CL or power from the RES to the ESS. [00347] In some such implementations, the providing at the fifth time includes providing (1) power from the RES to the at least one CL, and (2) one of: power from the ESS to the at least one CL or power from the RES to the ESS. [00348] In some such implementations, the providing at the fifth time includes providing (1) power from the RES to the ESS, (2) power from the RES to the at least one CL, and (3) power from the ESS to the at least one CL. [00349] In some such implementations, the method further comprises: providing, at a sixth time, power from at least one non-renewable energy source (NRES) to the at least one CL. [00350] In some embodiments, a system comprises: at least one renewable energy source (RES) configured to electrically couple to a grid interconnection point of an electric grid; at least one energy storage system (ESS) that is electrically coupled to the grid interconnection point and the at least one RES; at least one non-renewable energy source (NRES); and a controller that is communicatively coupled with at least one CL, the at least one ESS, the at least one NRES, and the at least one RES, the controller configured to: provide a first instruction to at least one of the at least one RES, the at least one NRES, or the at least one ESS to provide a first portion of electric power to the at least one CL up to an aggregated power demand; provide a second instruction to at least one of the at least one RES, the at least one NRES, or the at least one ESS to provide a second portion of electric power to the electric grid; and in response to determining that a grid condition exists, provide a third instruction to the at least one CL to one of increase or decrease a power demand at the at least one CL. [00351] In some embodiments, a system comprises: at least one renewable energy source (RES); at least one energy storage system (ESS) that is electrically coupled to a grid interconnection point of an electric grid and to the at least one RES, and that has an aggregated power capacity that is not more than an aggregated power output capacity of the at least one RES; and a controller that is communicatively coupled with at least one controllable load (CL) and with at least one of the at least one ESS or the at least one RES, the controller configured to control a net load profile of the at least one CL and to: provide a first instruction to at least one of the at least one RES or the at least one ESS to provide a first portion of electric power generated by the at least one RES or stored by the at least one ESS to the at least one CL; provide a second instruction to at least one of the at least one RES or the at least one ESS to provide a second portion of electric power to the electric grid; and in response to determining that a grid condition exists without electric power generated by the at least one RES exceeding the aggregated power capacity and the net load of the at least one CL, provide a third instruction to the at least one CL to one of increase or decrease a power demand at the at least one CL. [00352] In some such implementations, the aggregated power output capacity of the at least one RES exceeds a point of grid interconnect (POGI) limit by a factor of between about 3 and about 6. [00353] In some such implementations, the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00354] In some such implementations, the system has an associated capacity factor of at least about 60%, and the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00355] In some such implementations, a ratio of the power generated by the at least one RES to an aggregate load of the at least one CL is between about 3 and about 6, and the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00356] In some such implementations, the controller is further configured to operate the at least one RES or the at least one ESS as at least one of a peaker plant for the electric grid or a provider of ancillary services to the electric grid, and the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00357] In some such implementations, the at least one CL includes a plurality of CLs, the controller is further configured to provide instructions to the plurality of CLs to balance an energy distribution associated with the plurality of CLs, and the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00358] In some such implementations, the at least one CL includes at least one of a data center, an artificial intelligence (AI) training center, a cryptocurrency miner, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a water treatment plant, an industrial process heater, or a thermal battery. [00359] In some such implementations, the controller is further configured to at least one of cause delivery of power from the electric grid to the at least one CL load; select the first instruction such that a correlation of the at least one CL with the electric grid is changed in response to the first instruction; or select the first instruction such that a correlation of (1) at least one peak of a net load profile associated with the at least one CL, with (2) at least one peak of a net load profile associated with the electric grid is changed in response to the first instruction. [00360] In some such implementations, the first instruction is configured to cause results in a change in a correlation of the at least one CL with the electric grid in response to the at least one first instruction. [00361] In some such implementations, the system is configured to: (1) operate in a first mode as at least one of a baseload, a semi-baseload, a semi-peaker plant, or a peaker plant for the at least one CL, and (2) concurrently with operating in the first mode, operate in a second mode as at least one of a peaker plant, a semi-peaker plant, a semi-baseload, a baseload, or a provider of ancillary services for the electric grid. [00362] In some embodiments, a non-transitory, processor-readable medium stores instructions that, when executed by a processor, cause the processor to: control a net load of at least one controllable load (CL); provide a first instruction to at least one of (1) at least one renewable energy source (RES) or (2) at least one energy storage system (ESS), to cause a first portion of electric power generated by the at least one RES or stored by the at least one ESS to be supplied to the at least one CL; provide a second instruction to at least one of the at least one RES or the at least one ESS to provide a second portion of electric power to an electric grid; and in response to determining that a grid condition exists without electric power generated by the at least one RES exceeding an aggregated power capacity of the at least one ESS and an aggregated power demand of the at least one CL, provide a third instruction to the at least one CL to one of increase or decrease a power demand at the at least one CL. [00363] In some such implementations, the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00364] In some such implementations, the non-transitory, processor-readable medium further stores instructions to cause the processor to operate the at least one RES or the at least one ESS as at least one of a peaker plant for the electric grid or a provider of ancillary services to the electric grid. [00365] In some such implementations, the at least one CL includes at least one of a data center, an artificial intelligence (AI) training center, a cryptocurrency miner, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a water treatment plant, an industrial process heater, or a thermal battery. [00366] In some such implementations, the non-transitory, processor-readable medium further stores instructions to cause the processor to (1) operate in a first mode as at least one of a baseload, a semi-baseload, a semi-peaker plant, or a peaker plant for the at least one CL, and (2) concurrently with operating in the first mode, operate in a second mode as at least one of a peaker plant, a semi- peaker plant, a semi-baseload, a baseload, or a provider of ancillary services for the electric grid. [00367] In some embodiments, a non-transitory, processor-readable medium stores instructions that, when executed by a processor, cause the processor to: cause transmission of a signal representing a first instruction to at least one of (1) at least one renewable energy source (RES), (2) at least one non-renewable energy source (NRES), or (3) at least one energy storage system (ESS), to cause a first portion of electric power to be delivered to at least one controllable load (CL), up to an aggregated power demand; cause transmission of a signal representing a second instruction to at least one of the at least one RES, the at least one NRES, or the at least one ESS, to cause a second portion of electric power to be delivered to an electric grid; and in response to determining that a grid condition exists, cause transmission of a signal representing a third instruction to the at least one CL to cause one of an increase or a decrease of a power demand at the at least one CL. [00368] In some such implementations, the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid. [00369] In some such implementations, the at least one CL includes at least one of a data center, an artificial intelligence (AI) training center, a cryptocurrency miner, an electric vehicle (EV) charging station, a vertical farm, a hydrogen production facility, a water treatment plant, an industrial process heater, or a thermal battery. [00370] In some such implementations, the first instruction results in a change in a correlation of the at least one CL with the electric grid in response to the first instruction. [00371] In some embodiments, a non-transitory, processor-readable medium stores instructions that, when executed by a processor, cause the processor to: control a net load of at least one controllable load (CL); cause delivery of a first portion of electric power from at least one of (1) at least one renewable energy source (RES) or (2) at least one energy storage system (ESS) to the at least one CL; cause delivery of a second portion of the electric power from at least one of (1) the at least one RES or (2) the at least one ESS to an electric grid; and in response to determining that a grid condition of the electric grid exists without electric power generated by the at least one RES exceeding an aggregated power capacity of the at least one ESS and an aggregated power demand of the at least one CL, cause one of an increase or a decrease to a power demand at the at least one CL. [00372] One or more embodiments of the present disclosure combine features and/or capabilities from two or more of the inter-related sections titled herein “Twin-Configurable Architecture Renewable Power Plant for High Capacity Factor Servicing of Controllable Loads,” “Networked Energy Generation, Storage, and Distribution,” “Smart Seasonal Electrical Resource Allocation with Controllable Loads,” “System and Methods for Smart Renewable Powerplant Serving Multiple Controllable and Uncontrollable Loads,” or “Systems and Methods Utilizing AC Overbuilt Renewable Electric Generation Resource with Storage Device and Controllable Loads.” For example, any of system 100 of FIG.1, system 400 of FIG.4, system 1000 of FIG.10, system 1300 of FIGs.13-17, and system 2000 of FIG.20 may be combined with subject matter described in the section titled “Twin-Configurable Architecture Renewable Power Plant for High Capacity Factor Servicing of Controllable Loads.” Alternatively or in addition, any of system 100 of FIG. 1, system 400 of FIG.4, system 1000 of FIG.10, system 1300 of FIGs.13-17, and system 2000 of FIG.20 may be combined with subject matter described in the section titled “Networked Energy Generation, Storage, and Distribution.” Alternatively or in addition, any of system 100 of FIG.1, system 400 of FIG.4, system 1000 of FIG.10, system 1300 of FIGs.13-17, and system 2000 of FIG. 20 may be combined with subject matter described in the section titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads.” Alternatively or in addition, any of system 100 of FIG.1, system 400 of FIG.4, system 1000 of FIG.10, system 1300 of FIGs.13-17, and system 2000 of FIG.20 may be combined with subject matter described in the section titled “System and Methods for Smart Renewable Powerplant Serving Multiple Controllable and Uncontrollable Loads.” Alternatively or in addition, any of system 100 of FIG.1, system 400 of FIG.4, system 1000 of FIG.10, system 1300 of FIGs.13-17, and system 2000 of FIG.20 may be combined with subject matter described in the section titled “Systems and Methods Utilizing AC Overbuilt Renewable Electric Generation Resource with Storage Device and Controllable Loads.” [00373] Moreover, any of controller 200 of FIG.2, controller 1102 of FIG.11, controller 1802 of FIG.18, and controller 2102 of FIG.21 may be combined with subject matter described in the section titled “Twin-Configurable Architecture Renewable Power Plant for High Capacity Factor Servicing of Controllable Loads.” Alternatively or in addition, any of controller 200 of FIG. 2, controller 1102 of FIG. 11, controller 1802 of FIG. 18, and controller 2102 of FIG. 21 may be combined with subject matter described in the section titled “Networked Energy Generation, Storage, and Distribution.” Alternatively or in addition, any of controller 200 of FIG.2, controller 1102 of FIG.11, controller 1802 of FIG.18, and controller 2102 of FIG.21 may be combined with subject matter described in the section titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads.” Alternatively or in addition, any of controller 200 of FIG.2, controller 1102 of FIG. 11, controller 1802 of FIG. 18, and controller 2102 of FIG. 21 may be combined with subject matter described in the section titled “System and Methods for Smart Renewable Powerplant Serving Multiple Controllable and Uncontrollable Loads.” Alternatively or in addition, any of controller 200 of FIG. 2, controller 1102 of FIG. 11, controller 1802 of FIG. 18, and controller 2102 of FIG. 21 may be combined with subject matter described in the section titled “Systems and Methods Utilizing AC Overbuilt Renewable Electric Generation Resource with Storage Device and Controllable Loads.” [00374] Moreover, any of method 300 of FIG.3, method 500 of FIG.5, method 600 of FIG.6, method 700 of FIG.7, method 800 of FIG.8, method 1200 of FIG.12, method 1900 of FIG.19, method 2200 of FIG. 22 may be combined with subject matter described in the section titled “Twin-Configurable Architecture Renewable Power Plant for High Capacity Factor Servicing of Controllable Loads.” Alternatively or in addition, any of method 300 of FIG. 3, method 500 of FIG.5, method 600 of FIG.6, method 700 of FIG.7, method 800 of FIG.8, method 1200 of FIG. 12, method 1900 of FIG. 19, method 2200 of FIG. 22 may be combined with subject matter described in the section titled “Networked Energy Generation, Storage, and Distribution.” Alternatively or in addition, any of method 300 of FIG.3, method 500 of FIG.5, method 600 of FIG.6, method 700 of FIG.7, method 800 of FIG. 8, method 1200 of FIG. 12, method 1900 of FIG.19, method 2200 of FIG.22 may be combined with subject matter described in the section titled “Smart Seasonal Electrical Resource Allocation with Controllable Loads.” Alternatively or in addition, any of method 300 of FIG.3, method 500 of FIG. 5, method 600 of FIG.6, method 700 of FIG.7, method 800 of FIG.8, method 1200 of FIG.12, method 1900 of FIG.19, method 2200 of FIG.22 may be combined with subject matter described in the section titled “System and Methods for Smart Renewable Powerplant Serving Multiple Controllable and Uncontrollable Loads.” Alternatively or in addition, any of method 300 of FIG.3, method 500 of FIG.5, method 600 of FIG. 6, method 700 of FIG. 7, method 800 of FIG. 8, method 1200 of FIG. 12, method 1900 of FIG.19, method 2200 of FIG.22 may be combined with subject matter described in the section titled “Systems and Methods Utilizing AC Overbuilt Renewable Electric Generation Resource with Storage Device and Controllable Loads.” [00375] Those skilled in the art will also appreciate that while various items are illustrated as being stored in memory or on storage while being used, these items or portions of them may be transferred between memory and other storage devices for purposes of memory management and data integrity. Alternatively, in other embodiments some or all of the software components may execute in memory on another device and communicate with the illustrated computer system via inter-computer communication. Some or all of the system components or data structures may also be stored (e.g., as instructions or structured data) on a computer-accessible medium or a portable article to be read by an appropriate drive, various examples of which are described above. In some embodiments, instructions stored on a computer-accessible medium separate from computer system 400 may be transmitted to computer system 400 via transmission media or signals such as electrical, electromagnetic, or digital signals, conveyed via a communication medium such as a network or a wireless link. Various embodiments may further include receiving, sending, or storing instructions or data implemented in accordance with the foregoing description upon a computer- accessible medium. Accordingly, the present techniques may be practiced with other computer system configurations, e.g., including cloud-based computer system configurations. [00376] In block diagrams, illustrated components are depicted as discrete functional blocks, but embodiments are not limited to systems in which the functionality described herein is organized as illustrated. The functionality provided by each of the components may be provided by software or hardware modules that are differently organized than is presently depicted, for example such software or hardware may be intermingled, conjoined, replicated, broken up, distributed (e.g. within a data center or geographically), or otherwise differently organized. The functionality described herein may be provided by one or more processors of one or more computers executing code stored on a tangible, non-transitory, machine readable medium. In some cases, notwithstanding use of the singular term "medium," the instructions may be distributed on different storage devices associated with different computing devices, for instance, with each computing device having a different subset of the instructions, an implementation consistent with usage of the singular term “medium” herein. In some cases, third party content delivery networks may host some or all of the information conveyed over networks, in which case, to the extent information (e.g., content) is said to be supplied or otherwise provided, the information may provide by sending instructions to retrieve that information from a content delivery network. [00377] The reader should appreciate that the present application describes several independently useful techniques. Rather than separating those techniques into multiple isolated patent applications, applicants have grouped these techniques into a single document because their related subject matter lends itself to economies in the application process. But the distinct advantages and aspects of such techniques should not be conflated. In some cases, embodiments address all of the deficiencies noted herein, but it should be understood that the techniques are independently useful, and some embodiments address only a subset of such problems or offer other, unmentioned benefits that will be apparent to those of skill in the art reviewing the present disclosure. Due to costs constraints, some techniques disclosed herein may not be presently claimed and may be claimed in later filings, such as continuation applications or by amending the present claims. Similarly, due to space constraints, neither the Abstract nor the Summary of the Invention sections of the present document should be taken as containing a comprehensive listing of all such techniques or all aspects of such techniques. [00378] It should be understood that the description and the drawings are not intended to limit the present techniques to the particular form disclosed, but to the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present techniques as defined by the appended claims. Further modifications and alternative embodiments of various aspects of the techniques will be apparent to those skilled in the art in view of this description. Accordingly, this description and the drawings are to be construed as illustrative only and are for the purpose of teaching those skilled in the art the general manner of carrying out the present techniques. It is to be understood that the forms of the present techniques shown and described herein are to be taken as examples of embodiments. Elements and materials may be substituted for those illustrated and described herein, parts and processes may be reversed or omitted, and certain features of the present techniques may be utilized independently, all as would be apparent to one skilled in the art after having the benefit of this description of the present techniques. Changes may be made in the elements described herein without departing from the spirit and scope of the present techniques as described in the following claims. Headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description. [00379] As used herein, in particular embodiments, the terms “substantially,” “about” or “approximately” when preceding a numerical value indicates the value plus or minus a range of 10%. Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range is encompassed within the disclosure. That the upper and lower limits of these smaller ranges can independently be included in the smaller ranges is also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure. The term “substantially,” when referencing a non-numeric value, generally means “to a great or significant extent.” For example, “substantially curved” can refer to a shape that approximates a curve but may not be perfectly symmetrical or curvilinear. [00380] The indefinite articles “a” and “an,” as used herein in the specification and in the embodiments, unless clearly indicated to the contrary, should be understood to mean “at least one.” [00381] As used throughout this application, the word “may” is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). The words “include”, “including”, and “includes” and the like mean including, but not limited to. As used throughout this application, the singular forms “a,” “an,” and “the” include plural referents unless the content explicitly indicates otherwise. Thus, for example, reference to “an element” or "a element" includes a combination of two or more elements, notwithstanding use of other terms and phrases for one or more elements, such as “one or more.” The term "or" is, unless indicated otherwise, non-exclusive, i.e., encompassing both "and" and "or." Terms describing conditional relationships, e.g., "in response to X, Y," "upon X, Y,", “if X, Y,” "when X, Y," and the like, encompass causal relationships in which the antecedent is a necessary causal condition, the antecedent is a sufficient causal condition, or the antecedent is a contributory causal condition of the consequent, e.g., "state X occurs upon condition Y obtaining" is generic to "X occurs solely upon Y" and "X occurs upon Y and Z." Such conditional relationships are not limited to consequences that instantly follow the antecedent obtaining, as some consequences may be delayed, and in conditional statements, antecedents are connected to their consequents, e.g., the antecedent is relevant to the likelihood of the consequent occurring. Statements in which a plurality of attributes or functions are mapped to a plurality of objects (e.g., one or more processors performing steps A, B, C, and D) encompasses both all such attributes or functions being mapped to all such objects and subsets of the attributes or functions being mapped to subsets of the attributes or functions (e.g., both all processors each performing steps A-D, and a case in which processor 1 performs step A, processor 2 performs step B and part of step C, and processor 3 performs part of step C and step D), unless otherwise indicated. Similarly, reference to “a computer system” performing step A and “the computer system” performing step B can include the same computing device within the computer system performing both steps or different computing devices within the computer system performing steps A and B. Further, unless otherwise indicated, statements that one value or action is “based on” another condition or value encompass both instances in which the condition or value is the sole factor and instances in which the condition or value is one factor among a plurality of factors. Unless otherwise indicated, statements that “each” instance of some collection have some property should not be read to exclude cases where some otherwise identical or similar members of a larger collection do not have the property, i.e., each does not necessarily mean each and every. Limitations as to sequence of recited steps should not be read into the claims unless explicitly specified, e.g., with explicit language like “after performing X, performing Y,” in contrast to statements that might be improperly argued to imply sequence limitations, like “performing X on items, performing Y on the X’ed items,” used for purposes of making claims more readable rather than specifying sequence. Statements referring to “at least Z of A, B, and C,” and the like (e.g., “at least Z of A, B, or C”), refer to at least Z of the listed categories (A, B, and C) and do not require at least Z units in each category. Unless specifically stated otherwise, as apparent from the discussion, it is appreciated that throughout this specification discussions utilizing terms such as “processing,” “computing,” “calculating,” “determining” or the like refer to actions or processes of a specific apparatus, such as a special purpose computer or a similar special purpose electronic processing/computing device. Features described with reference to geometric constructs, like "parallel," "perpendicular/orthogonal," “square,” “cylindrical,” and the like, should be construed as encompassing items that substantially embody the properties of the geometric construct, e.g., reference to "parallel" surfaces encompasses substantially parallel surfaces. The permitted range of deviation from Platonic ideals of these geometric constructs is to be determined with reference to ranges in the specification, and where such ranges are not stated, with reference to industry norms in the field of use, and where such ranges are not defined, with reference to industry norms in the field of manufacturing of the designated feature, and where such ranges are not defined, features substantially embodying a geometric construct should be construed to include those features within 15% of the defining attributes of that geometric construct. The terms "first", "second", "third," “given” and so on, if used in the claims, are used to distinguish or otherwise identify, and not to show a sequential or numerical limitation. As is the case in ordinary usage in the field, data structures and formats described with reference to uses salient to a human need not be presented in a human-intelligible format to constitute the described data structure or format, e.g., text need not be rendered or even encoded in Unicode or ASCII to constitute text; images, maps, and data-visualizations need not be displayed or decoded to constitute images, maps, and data-visualizations, respectively; speech, music, and other audio need not be emitted through a speaker or decoded to constitute speech, music, or other audio, respectively. Computer implemented instructions, commands, and the like are not limited to executable code and can be implemented in the form of data that causes functionality to be invoked, e.g., in the form of arguments of a function or API call. To the extent bespoke noun phrases (and other coined terms) are used in the claims and lack a self-evident construction, the definition of such phrases may be recited in the claim itself, in which case, the use of such bespoke noun phrases should not be taken as invitation to impart additional limitations by looking to the specification or extrinsic evidence. [00382] In this patent, to the extent any U.S. patents, U.S. patent applications, or other materials (e.g., articles) have been incorporated by reference, the text of such materials is only incorporated by reference to the extent that no conflict exists between such material and the statements and drawings set forth herein. In the event of such conflict, the text of the present document governs, and terms in this document should not be given a narrower reading in virtue of the way in which those terms are used in other materials incorporated by reference.

Claims

CLAIMS What is claimed is: 1. A system, comprising: at least one renewable energy source (RES) configured to electrically couple to a grid interconnection point of an electric grid, an aggregated alternating current (AC) power output capacity of the at least one RES exceeding a point of grid interconnect (POGI) limit of the grid interconnection point; at least one energy storage system (ESS) that is electrically coupled to the grid interconnection point and the at least one RES and that has an aggregated power capacity that is less than the aggregated AC power output capacity of the at least one RES; and a controller that is communicatively coupled with at least one controllable load (CL), the at least one ESS, and the at least one RES, the controller configured to control a net load profile of the at least one CL such that the net load profile of the at least one CL includes at least one value between a maximum net load value and a minimum net load value of the at least one CL, the controller further configured to: provide a first instruction to at least one of the at least one RES or the at least one ESS to provide a first portion of electric power generated by the at least one RES or stored by the at least one ESS to the at least one CL up to an aggregated power demand; provide a second instruction to at least one of the at least one RES or the at least one ESS to provide a second portion of electric power to the electric grid in response to (A) electric power generated by the at least one RES exceeding an aggregated power capacity and the aggregated power demand, or (B) the controller, using a predictive algorithm and power data, determining that a grid condition exists in a power system forecast; and in response to determining that the grid condition exists without the electric power generated by the at least one RES exceeding the aggregated power capacity and the aggregated power demand, providing a third instruction to the at least one CL to one of increase or decrease a power demand at the at least one CL.

2. The system of claim 1, wherein the aggregated AC power output capacity of the at least one RES exceeds the POGI limit by a factor of between about 3 and about 6.

3. The system of claim 1, wherein the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid.

4. The system of claim 1, wherein the system has an associated capacity factor of at least about 60%.

5. The system of claim 1, wherein a ratio of the power generated by the at least one RES to an aggregate load of the at least one CL is between about 3 and about 6.

6. The system of claim 1, wherein the controller is further configured to operate the at least one RES or the at least one ESS as at least one of a peaker plant for the electric grid or a provider of ancillary services to the electric grid, and the grid condition is associated with at least one of a price of power associated with the electric grid, a price of ancillary services associated with the electric grid, a curtailment associated with the electric grid, a congestion price associated with the electric grid, or a decongestion value associated with the electric grid.

7. The system of claim 1, wherein the at least one CL includes a plurality of CLs, and the controller is further configured to provide instructions to the plurality of CLs to balance an energy distribution associated with the plurality of CLs.

8. The system of claim 1, wherein the at least one CL includes a data center.

9. The system of claim 1, wherein the at least one CL includes an artificial intelligence (AI) training center.

10. The system of claim 1, wherein the at least one CL includes a cryptocurrency miner.

11. The system of claim 1, wherein the at least one CL includes an electric vehicle (EV) charging station.

12. The system of claim 1, wherein the at least one CL includes a vertical farm.

13. The system of claim 1, wherein the at least one CL includes a hydrogen production facility.

14. The system of claim 1, wherein the at least one CL includes a water treatment plant (including desalination and purification).

15. The system of claim 1, wherein the at least one CL load includes an industrial process heater.

16. The system of claim 1, wherein the at least one CL load includes a thermal battery.

17. The system of claim 1, wherein the controller is further configured to cause delivery of power from the electric grid to the at least one CL load.

18. The system of claim 1, wherein the controller is further configured to select the first instruction such that a correlation of the at least one CL with the electric grid is changed in response to the first instruction.

19. The system of claim 1, wherein the controller is further configured to select the first instruction such that a correlation of (1) at least one peak of a net load profile associated with the at least one CL, with (2) at least one peak of a net load profile associated with the electric grid is changed in response to the first instruction.

20. The system of claim 1, wherein the first instruction is configured to cause a change in a correlation of the at least one CL with the electric grid in response to the at least one first instruction.

21. The system of claim 1, wherein the system is configured to: (1) operate in a first mode as at least one of a baseload, a semi-baseload, a semi-peaker plant, or a peaker plant for the at least one CL, and (2) concurrently with operating in the first mode, operate in a second mode as at least one of a peaker plant, a semi-peaker plant, a semi-baseload, a baseload, or a provider of ancillary services for the electric grid.

22. The system of claim 1, wherein the controller is further configured to provide a fourth instruction to at least one non-renewable energy source (NRES) to cause the at least one NRES to provide a third portion of electric power generated by the at least one NRES to the at least one CL.

23. A method of providing power on an RES-ESS-CL system, the method comprising: providing, at a first time and by at least one of an RES or an ESS, power to a point of grid interconnect (POGI) associated with an electric grid, the POGI disposed between at least one CL and the electric grid; providing, at a second time and by the at least one of the RES or the ESS, power to the at least one CL; providing, at a third time, power received from the electric grid at the POGI to the ESS; providing, at a fourth time, power from received from the electric grid at the POGI to the at least one CL; and providing, at a fifth time, no power via the POGI and providing at least one of power from the ESS to the at least one CL, power from the RES to the at least one CL, or power from the RES to the ESS.

24. The method of claim 23, further comprising: providing, at the fourth time, power from at least one of the RES or the ESS to the at least one CL.

25. The method of claim 23, further comprising: providing, at the fourth time, power from the RES to at least one of the ESS or the at least one CL.

26. The method of claim 23, wherein the providing at the fifth time includes providing (1) power from the ESS to the at least one CL, and (2) one of: power from the RES to the at least one CL or power from the RES to the ESS.

27. The method of claim 23, wherein the providing at the fifth time includes providing (1) power from the RES to the at least one CL, and (2) one of: power from the ESS to the at least one CL or power from the RES to the ESS.

28. The method of claim 23, wherein the providing at the fifth time includes providing (1) power from the RES to the ESS, (2) power from the RES to the at least one CL, and (3) power from the ESS to the at least one CL.

29. The method of claim 23, further comprising: providing, at a sixth time, power from at least one non-renewable energy source (NRES) to the at least one CL.

30. A system, comprising: at least one renewable energy source (RES) configured to electrically couple to a grid interconnection point of an electric grid; at least one energy storage system (ESS) that is electrically coupled to the grid interconnection point and the at least one RES; at least one non-renewable energy source (NRES); and a controller that is communicatively coupled with at least one CL, the at least one ESS, the at least one NRES, and the at least one RES, the controller configured to: provide a first instruction to at least one of the at least one RES, the at least one NRES, or the at least one ESS to provide a first portion of electric power to the at least one CL up to an aggregated power demand; provide a second instruction to at least one of the at least one RES, the at least one NRES, or the at least one ESS to provide a second portion of electric power to the electric grid; and in response to determining that a grid condition exists, provide a third instruction to the at least one CL to one of increase or decrease a power demand at the at least one CL.