WO2015139061A1 - Système de commande de vitesse de montée et procédés utilisant des dispositifs accumulateurs d'énergie - Google Patents

Système de commande de vitesse de montée et procédés utilisant des dispositifs accumulateurs d'énergie Download PDF

Info

Publication number
WO2015139061A1
WO2015139061A1 PCT/US2015/020842 US2015020842W WO2015139061A1 WO 2015139061 A1 WO2015139061 A1 WO 2015139061A1 US 2015020842 W US2015020842 W US 2015020842W WO 2015139061 A1 WO2015139061 A1 WO 2015139061A1
Authority
WO
WIPO (PCT)
Prior art keywords
power
data
ramp
energy storage
battery
Prior art date
Application number
PCT/US2015/020842
Other languages
English (en)
Inventor
Vahid SALEHI POUR MEHR
Kevin Meagher
Brian RADIBRATOVIC
Original Assignee
Power Analytics Corporation
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Power Analytics Corporation filed Critical Power Analytics Corporation
Publication of WO2015139061A1 publication Critical patent/WO2015139061A1/fr

Links

Classifications

    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S40/00—Systems for electrical power generation, transmission, distribution or end-user application management characterised by the use of communication or information technologies, or communication or information technology specific aspects supporting them
    • Y04S40/20—Information technology specific aspects, e.g. CAD, simulation, modelling, system security

Definitions

  • the present disclosure relates generally to power systems and more particularly to methods and devices for limiting power ramps in real time utilizing an energy storage device.
  • PV photovoltaic
  • PV photovoltaic
  • the PV facility should have the ability control the rate of change of power output during some circumstances such as rate of increase/decrease of power when a curtailment of power output is engaged.
  • rate of increase/decrease of power when a curtailment of power output is engaged.
  • ramp rates there are some constrains such as ten percent (10%) per minute rate (based on PV installed capacity or nameplate rating) which should be considered.
  • PV storage inverters include the settings for limiting the rate of power delivery to the grid in response to intermittent PV generation. It is desirable for storage systems to manage their charging/discharging modes such that the rate of change in power delivery to and from the grid remains below this limit even with intermittent PV generation.
  • a power generation system comprises: at least one photo voltaic (PV) unit for generating power; a first data unit receiving and storing irradiance data measured by the PV unit; a second data unit having irradiance forecast data; a controller receiving the data from both data units; and an energy storage device receiving instructions from the controller, wherein the instructions from the controller include ramp up or ramp down messages.
  • PV photo voltaic
  • a power generation method comprises: receiving irradiance forecast data; evaluating the received data; and selectively charging/discharging an energy storage device based on the evaluation.
  • FIG. 1 illustrates an exemplary output power of a photo-voltaic plant for different irradiance profiles
  • FIG. 2 illustrates ramp rate occurrence frequency for two different irradiance profiles
  • FIG. 3 illustrates dynamic model of a Lithium- ion battery
  • FIG. 4 illustrates a ramp-rate limit control logic circuit in accordance with exemplary embodiments
  • FIGs. 5(a) - 5(c) illustrate ramped power, PV plant output power and battery power for a day and for an hour of the day and energy changes of the battery for the day;
  • FIGs. 6(a) and 6(b) illustrate ramped power, PV plant output power and battery power based direct measurement and based on forecast data for a time period
  • FIGs. 7(a) and 7(b) illustrate ramped power, PV plant output power and battery power for an hour and battery energy change based on forecast data for a day;
  • FIG. 8 illustrates energy changes of the battery for a day based on forecast data and on utilizing the control circuit of FIG. 4;
  • FIGs. 9(a) and (b) illustrate a PV plant and a PV dynamic model in accordance with exemplary embodiments
  • FIG. 10 illustrates a PV plant output power with and without battery storage system
  • FIG. 11 illustrates a PB system in accordance with exemplary embodiments
  • FIG. 12 illustrates a system in accordance with exemplary embodiments
  • FIG. 13 illustrates a method in accordance with exemplary embodiments.
  • exemplary embodiments means that a particular feature, structure, or characteristic as described is included in at least one embodiment. Thus, the appearances of these terms and similar phrases in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. The headings provided herein are for convenience only and do not interpret the scope or meaning of the embodiments described herein.
  • Exemplary embodiments disclose methods for controlling the charging and/or discharging modes of storage devices in order to mitigate adverse effects of high ramp rates in point of common coupling (PCC) of PV plant connection to the grid.
  • PCC point of common coupling
  • Such a method can be implemented in real-time systems. It may be based on utilizing instantaneous (real-time) irradiance data and forecast irradiance data for better ramp rate control in order to minimize the battery size and increase battery life.
  • Irradiance data for a proposed PV plant location can be taken into account in determining the size of an energy storage device that is to be implemented within the proposed PV plant.
  • a dynamic model of a PV plant, energy storage devices and realistic sun irradiance data may be implemented to verify the performance of the exemplary methods.
  • Irradiance and insolation are two major measures of solar resource. Irradiance is a measure of solar power on a given plane expressed in W/m (watts per square meter). The output power of a PV plant is generally proportional to the irradiance across the area of the plant.
  • FIG. 1 An exemplary output power of a fifty (50) MW PV plant for three different days with different irradiance profiles is illustrated in FIG. 1.
  • the profiles illustrated include a profile for a clear sunny day, an overcast day and a partly cloudy day.
  • the PV output variability is measured based on computing AC power output changes over multiple time intervals.
  • a ten (10) minute magnified data of this exemplary data for a partly cloudy day is also illustrated in FIG. 1.
  • the three arrows illustrate how the ramp rates are calculated for different time scales.
  • a number of change-in-power ( ⁇ ) observations for four ramp rate intervals may be measured. These rate intervals may include ten (10) seconds, one (1) minute, ten (10) minutes and one (1) hour.
  • the observation data indicates the frequency and extent with which output ramping occurs during daytime hours.
  • ramping events are a concern for PV interconnection even if they rarely occur.
  • Exemplary embodiments disclose novel methods for reducing and mitigating the power ramping issues in real-time by limiting power ramps utilizing an energy storage device.
  • solar irradiance forecast data may be utilized to predict or estimate output power ramps. Therefore, based on available measured irradiance data at five (5) second intervals, the output power ramp rate increase or decrease for different time scales can be estimated or computed.
  • ramp rate occurrence frequency for two different irradiance profiles representing a partly cloudy day and an overcast day can be considered. These profiles are illustrated in FIG. 2.
  • a ten percent (10%) power change of five (5) MW may be applied with various time scales such as one (1) hour, ten (10) minutes, one (1) minute and ten (10) seconds for example.
  • the power change, ⁇ (in MW) may be multiplied by the time step (i.e. 5 second measurement data resolution) and then divided by each of the different time scales to find proper ramp rate to compare with the irradiance data.
  • the computed ramp rate for each of the time scales may be 0.006944 MW (for 1 hour), 0.041666 MW (for 10 minutes), 0.41666 (for 1 minute) and 2.5 MW (for 10 seconds).
  • the frequency of ramp occurrences in the output power for the partly cloudy day and for the overcast day for the time scales specified above are also illustrated in FIG. 2.
  • the ramp occurrence frequency of five (5) MW per one (1) hour ramps is 83.93% of the total daytime from 6 AM to 6 PM.
  • the occurrence frequency is 52.13% of daytime.
  • the occurrence frequency is 15.28% of daytime. There is no ramp with five (5) MW per ten (10) second rating.
  • (1) hour ramps is 82.97% of the total daytime from 6 AM to 6 PM.
  • the occurrence frequency is 42.77% of daytime.
  • the occurrence frequency is 4.9% of daytime.
  • Exemplary embodiments as described herein focus on the ten percent (10%) of nominal power per one (1) minute ramp ratings. This rating needs faster control from AC grid based on the system inertia and available spinning reserve to reduce power ramp effects of PV plant output. The focus on the ten percent (10%) of nominal power for one (1) minute results from typical existing requirements. The selection of this particular ramp rating is exemplary and not limiting. A battery energy storage device may be utilized for mitigating the occurrence of the ramp rate in the five (5) MW per one (1) minute case.
  • An energy storage device may be implemented to meet specific output variability limits at the point of common coupling (PCC). Decreasing ramp rate limits at PCC may be applicable to specific circumstances based on effects on local voltage and frequency, islanded and grid connected operations and cost preference of the energy storage devices.
  • PCC point of common coupling
  • Batteries having a lower cost and lower power requirement may not store large amounts of power. Other batteries that can store large amount of power may have a higher cost.
  • Existing PV systems utilize lead-acid battery products which are inexpensive but not efficient in weight and size.
  • Lithium-ion batteries are utilized in specialized applications such as large PV installation plants. These batteries deliver better performance in a small size without the need for maintenance. A Lithium-ion battery provides greater deep discharge which means more energy can be stored in these systems. A Lithium-ion battery also has a longer life which makes it appropriate for remote locations.
  • a battery For ramp rate application of a PV plant, a battery should have fast response in charging and discharging performance which helps in absorbing fluctuation of PV output power. Factors that are considered may include battery storage capacity, rate of charge and discharge power, state of charge, maximum depth of discharge for longer battery life and transient time response of a storage device. [0046] ynamic model of a Lithium-ion battery that is implemented for studying the transient behavior of energy storage devices is illustrated in FIG. 3. In order to improve power quality and to smooth the PV output power, a real-time ramp-rate controller may be designed for monitoring the sun irradiance or the PV output power.
  • the controller can also take action to absorb or inject active power to the PCC.
  • the controller may send charge or discharge command to the battery based on desired power level.
  • a bidirectional converter control may be used to interlink the DC storage device to the AC utility. This controller can be integrated to the PV inverter control or can be independent from PV control circuit.
  • a ramp-rate limit control logic circuit in accordance with exemplary embodiments is illustrated in FIG. 4.
  • the PV power output or sun irradiance real-time data may provide the input to the logic circuit and ramped up/down data may be output by the logic circuit. Rising or falling ramp rates can be the parameters for this circuit. The output can be subtracted from the input in order to achieve required power signal for a battery controller.
  • the PV output power signal [MW] or sun irradiance data [W/m 2 ] can be used as input for ramp rate control.
  • Real-time data can be used to calculate the required power of a battery bank.
  • FIG. 5A An expected PV output (dashed curve) and smoothed PV output (dotted curve) are illustrated in FIG. 5A for example.
  • the rate of change of the smoothed curve may be limited to five (5) MW per one (1) minute for example.
  • the dotted curve has to be generated in the PCC.
  • the solid line illustrates the power that needs to be injected to the PCC by the available energy storage system.
  • the positive and negative power signs correspond to charge and discharge modes for battery controller.
  • FIG. 5B The expected PV output, the smoothed PV output (limited to 5 MW/min) and the power needed for injection to the PCC for a one hour interval between 10AM -11AM in an exemplary setting are illustrated in FIG. 5B.
  • This particular time range represents the period during which the maximum and minimum requested battery power occurs in this example.
  • the energy storage system should be capable of producing 10.78 MW active power instantly and be capable of consuming 11.03 MW active power instantly. This amount of power is approximately twenty percent (20%) of the PV plant nominal power (of 50 MW).
  • the capacity of the energy storage system also has to correspond to this value (i.e. 0.759 MWh).
  • the size of the battery is approximately 0.8 MWh for full discharge depth but it should be capable of instantly providing approximately 11 MW as discharge/charge power.
  • Typical batteries allow less charge/discharge amount of power but are designed in high capacity range. In order to extend the battery life, they allow limited depth of discharge.
  • Irradiance data may be used to predict PV power generation. It can also be utilized to ramp down (and ramp up) PV output power.
  • the ramp rise event and the battery power request without having any knowledge prior to ramp occurrence is illustrated in FIG. 6A. In this setting, the real-time system should immediately take over this large power production duty with the storage device.
  • the dashed line indicates the PV output and the dotted line represents a ramped power value. The ramped value trails the actual PV output during changes toward an increase or toward a decrease.
  • FIG. 6B An upcoming ramp event that is predicted a minute ahead of the actual ramp event and the beginning of the ramping up according to exemplary embodiments is illustrated in FIG. 6B.
  • the ramped (dotted line) value leads the actual PV output (dashed line) before the actual output value increases or decreases.
  • Both discharging and/or charging modes may be used to decrease requested power amount during this event.
  • the decrease in the battery power exchange results in a reduced need in its size and increases its life.
  • FIG. 7 The application of forecasted data for controlling battery power to start charge or discharge the ramping event fifty (50) seconds prior to the actual ramping event is illustrated in FIG. 7. This time frame is chosen in a way to achieve the smallest power exchange between the battery and the PCC point.
  • the battery output power can be limited to 6.618 MW in discharge mode and 6.85 in charging mode.
  • the battery state of charge (SOC) decreases by 0.759 MWh during the daytime.
  • Control based on forecasted data may be enhanced in a way so as not to use the battery power when there is no predicted ramp event.
  • the controller keeps charging the battery until the maximum power output time of the day (i.e. noon) and then discharges the battery for the rest of the duration of daytime. This keeps the battery in service all day unnecessarily.
  • a real-time controller may be equipped with a bypass controller for the time periods during which the forecasted data does not include (or, predict) an upcoming ramp event. The bypass controller can keep the battery converter disconnected from the circuit.
  • FIG. 8 Keeping the battery power at zero when there is no ramp event will cause less battery discharge at the end of the day. Such reduced discharge can facilitate faster charging back in the morning and increasing the battery life expectancy. This controller will also turn off the battery controller for all sunny days.
  • FIG. 9A The single line diagram and the dynamic PV model used for transient study is illustrated in FIG. 9A. Thirty six (36) PV blocks are distributed in 6 branches with a battery unit for each branch. A detailed structure of a PV is illustrated in FIG. 9B. Each battery includes the dynamic model illustrated in FIG. 3. A time series power flow is used for measuring sun irradiance data every 5 seconds.
  • a transient simulation may be performed to show the effectiveness of usage of battery storages to smooth the power ramp rates for the total PV generation.
  • the output power control signal is calculated for each battery to decrease the ramping rate of the generation of the PVs.
  • the PV plant output power [MW] with (dashed line) and without (solid line) battery storage system is illustrated in FIG. 10 for comparison.
  • the Power Analytics Real-Time model integrates the solar forecasting data with the detailed electrical model of specific photovoltaic inverters and the control of those inverters as well as the simulated and actual charging and discharging of the battery storage. This accurate model also eliminates the variability energy storage so that the use of energy storage is minimized depending on the specific operation requirements of the power network owner or operator.
  • the present disclosure develops a real-time power ramp-rate limiter feature for photovoltaic plants subjected to intense daily power variations.
  • a method in accordance with exemplary embodiments facilitates the smoothing of PV output power at the point of common coupling (PCC) below a requested ramp rate, i.e. 10% Power nom /lmin by using energy storage devices which are controlled by a real-time model based system. Using forecasted sun irradiance/power data enhances the design and helps in selecting smaller storage device.
  • PCC point of common coupling
  • FIG. 11 An exemplary PV system is illustrated in FIG. 11.
  • a plurality of PVs arranged in banks with each bank connecting to the power network via an inverter.
  • a battery energy storage is connected to the PVs and the power system via a bi-directional inverter.
  • the bi-directional inverter facilitates charging and discharging of thee battery energy storage.
  • FIG. 10 A system in accordance with exemplary embodiments is illustrated in FIG.
  • a PV unit provides actual (or real-time) irradiance information to an irradiance data source.
  • the information provided by the PV is actual data measured by the PV.
  • a forecast irradiance data is also provided by a forecasting service or system that may utilize sensors for example.
  • the forecast data may be gathered by a sensor (such as a camera) that is located upstream of a PV generation area. In this context, upstream may indicate that wind and clouds may be flowing from the sensor location toward the PB generation area.
  • the PV provides generated power to a grid for example.
  • the actual and forecast irradiance data may be provided to a ramp rate controller.
  • the ramp rate controller issues charge or discharge commands to the energy (e.g. battery) storage device in the form of ramp up or ramp down messages.
  • the communication may be via wired or wireless means. If the message is a ramp up event, the battery is discharged and if the message is a ramp down event, the battery is charged.
  • Power for charging the device may be from the power generated by the PV and/or from the grid. Discharge from the battery may be provided to the grid.
  • FIG. 1 A method in accordance with exemplary embodiments is illustrated in FIG.
  • Irradiance forecast data is received. If the forecast indicates a ramp down event (i.e. power generation from the PV is decreasing), the energy storage device is charged before the ramping occurs. As the forecast indicates a leveling or ramping up event, the device is discharged during the ramping. If the forecast indicates a ramp up event, the energy storage device discharged before the ramping event. The device is charged during the ramping. In both cases, the process returns to evaluating received irradiance forecast data.
  • a ramp down event i.e. power generation from the PV is decreasing
  • Exemplary systems and methods described herein can be specially constructed for the required purpose such a general purpose computer that is selectively activated or configured by a computer program stored in the computer.
  • the embodiments as described herein may also be embodied as computer readable code on a computer readable medium.
  • the computer readable medium can be any data storage device that can store data that can thereafter be read (and executed) by a computer or computer system.
  • the irradiance data can be stored in network locations remote from the PVs and can be accessed by a network (public, private, etc.) connection that is wired or wireless.

Landscapes

  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

L'invention concerne un système de génération de puissance qui inclut au moins une unité photovoltaïque (PV) permettant de générer de la puissance, une première unité de données recevant et enregistrant des données d'éclairement énergétique mesurées par l'unité PV, une seconde unité de données comportant des données de prévision d'éclairement énergétique, un dispositif de commande recevant les données des deux unités de données et un dispositif accumulateur d'énergie recevant des instructions du dispositif de commande, les instructions du dispositif de commande incluant des messages d'accélération ou de décélération.
PCT/US2015/020842 2014-03-14 2015-03-16 Système de commande de vitesse de montée et procédés utilisant des dispositifs accumulateurs d'énergie WO2015139061A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201461953708P 2014-03-14 2014-03-14
US61/953,708 2014-03-14

Publications (1)

Publication Number Publication Date
WO2015139061A1 true WO2015139061A1 (fr) 2015-09-17

Family

ID=54072531

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/US2015/020842 WO2015139061A1 (fr) 2014-03-14 2015-03-16 Système de commande de vitesse de montée et procédés utilisant des dispositifs accumulateurs d'énergie

Country Status (1)

Country Link
WO (1) WO2015139061A1 (fr)

Cited By (23)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017062914A1 (fr) * 2015-10-08 2017-04-13 Johnson Controls Technology Company Système d'énergie photovoltaïque avec optimisation de fonction de valeur
WO2017062908A1 (fr) * 2015-10-08 2017-04-13 Johnson Controls Technology Company Système d'énergie photovoltaïque à prédiction d'intensité solaire
CN107528344A (zh) * 2017-09-27 2017-12-29 中国电力科学研究院 一种光储一体化发电装置并网运行控制方法及系统
EP3340416A1 (fr) * 2016-12-20 2018-06-27 Siemens Aktiengesellschaft Unité de commande
US10186889B2 (en) 2015-10-08 2019-01-22 Taurus Des, Llc Electrical energy storage system with variable state-of-charge frequency response optimization
US10190793B2 (en) 2015-10-08 2019-01-29 Johnson Controls Technology Company Building management system with electrical energy storage optimization based on statistical estimates of IBDR event probabilities
US10197632B2 (en) 2015-10-08 2019-02-05 Taurus Des, Llc Electrical energy storage system with battery power setpoint optimization using predicted values of a frequency regulation signal
US10222427B2 (en) 2015-10-08 2019-03-05 Con Edison Battery Storage, Llc Electrical energy storage system with battery power setpoint optimization based on battery degradation costs and expected frequency response revenue
US10250039B2 (en) 2015-10-08 2019-04-02 Con Edison Battery Storage, Llc Energy storage controller with battery life model
US10283968B2 (en) 2015-10-08 2019-05-07 Con Edison Battery Storage, Llc Power control system with power setpoint adjustment based on POI power limits
US10418832B2 (en) 2015-10-08 2019-09-17 Con Edison Battery Storage, Llc Electrical energy storage system with constant state-of charge frequency response optimization
US10418833B2 (en) 2015-10-08 2019-09-17 Con Edison Battery Storage, Llc Electrical energy storage system with cascaded frequency response optimization
US10564610B2 (en) 2015-10-08 2020-02-18 Con Edison Battery Storage, Llc Photovoltaic energy system with preemptive ramp rate control
US10594153B2 (en) 2016-07-29 2020-03-17 Con Edison Battery Storage, Llc Frequency response optimization control system
US10700541B2 (en) 2015-10-08 2020-06-30 Con Edison Battery Storage, Llc Power control system with battery power setpoint optimization using one-step-ahead prediction
US10742055B2 (en) 2015-10-08 2020-08-11 Con Edison Battery Storage, Llc Renewable energy system with simultaneous ramp rate control and frequency regulation
US10778012B2 (en) 2016-07-29 2020-09-15 Con Edison Battery Storage, Llc Battery optimization control system with data fusion systems and methods
US11031781B1 (en) 2020-05-04 2021-06-08 8Me Nova, Llc Method for implementing power delivery transaction for potential electrical output of integrated renewable energy source and energy storage system facility
US11159022B2 (en) 2018-08-28 2021-10-26 Johnson Controls Tyco IP Holdings LLP Building energy optimization system with a dynamically trained load prediction model
US11163271B2 (en) 2018-08-28 2021-11-02 Johnson Controls Technology Company Cloud based building energy optimization system with a dynamically trained load prediction model
US11210617B2 (en) 2015-10-08 2021-12-28 Johnson Controls Technology Company Building management system with electrical energy storage optimization based on benefits and costs of participating in PDBR and IBDR programs
US11532943B1 (en) 2019-10-27 2022-12-20 Thomas Zauli Energy storage device manger, management system, and methods of use
US11606062B2 (en) 2019-02-08 2023-03-14 8Me Nova, Llc Coordinated control of renewable electric generation resource and charge storage device

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060276938A1 (en) * 2005-06-06 2006-12-07 Equinox Energy Solutions, Inc. Optimized energy management system
US20080167756A1 (en) * 2007-01-03 2008-07-10 Gridpoint, Inc. Utility console for controlling energy resources
US20080215302A1 (en) * 2006-07-07 2008-09-04 Edsa Micro Corporation Systems and methods for real-time dynamic simulation of uninterruptible power supply solutions and their control logic systems
US20100231045A1 (en) * 2009-02-13 2010-09-16 First Solar, Inc. Photovoltaic Power Plant Output
US20110276269A1 (en) * 2010-05-07 2011-11-10 Hummel Steven G Systems and methods for forecasting solar power
US20120166085A1 (en) * 2010-12-14 2012-06-28 Peter Gevorkian Solar power monitoring and predicting of solar power output
WO2013188517A2 (fr) * 2012-06-13 2013-12-19 S&C Electric Company Intégration photovoltaïque de réseau électrique à l'aide de stockage et de gestion d'énergie distribués

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060276938A1 (en) * 2005-06-06 2006-12-07 Equinox Energy Solutions, Inc. Optimized energy management system
US20080215302A1 (en) * 2006-07-07 2008-09-04 Edsa Micro Corporation Systems and methods for real-time dynamic simulation of uninterruptible power supply solutions and their control logic systems
US20080167756A1 (en) * 2007-01-03 2008-07-10 Gridpoint, Inc. Utility console for controlling energy resources
US20100231045A1 (en) * 2009-02-13 2010-09-16 First Solar, Inc. Photovoltaic Power Plant Output
US20110276269A1 (en) * 2010-05-07 2011-11-10 Hummel Steven G Systems and methods for forecasting solar power
US20120166085A1 (en) * 2010-12-14 2012-06-28 Peter Gevorkian Solar power monitoring and predicting of solar power output
WO2013188517A2 (fr) * 2012-06-13 2013-12-19 S&C Electric Company Intégration photovoltaïque de réseau électrique à l'aide de stockage et de gestion d'énergie distribués

Cited By (39)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10591178B2 (en) 2015-10-08 2020-03-17 Con Edison Battery Storage, Llc Frequency response optimization based on a change in battery state-of-charge during a frequency response period
US10554170B2 (en) 2015-10-08 2020-02-04 Con Edison Battery Storage, Llc Photovoltaic energy system with solar intensity prediction
WO2017062908A1 (fr) * 2015-10-08 2017-04-13 Johnson Controls Technology Company Système d'énergie photovoltaïque à prédiction d'intensité solaire
US11156380B2 (en) 2015-10-08 2021-10-26 Johnson Controls Technology Company Building control systems with optimization of equipment life cycle economic value while participating in IBDR and PBDR programs
US10186889B2 (en) 2015-10-08 2019-01-22 Taurus Des, Llc Electrical energy storage system with variable state-of-charge frequency response optimization
US10190793B2 (en) 2015-10-08 2019-01-29 Johnson Controls Technology Company Building management system with electrical energy storage optimization based on statistical estimates of IBDR event probabilities
US10197632B2 (en) 2015-10-08 2019-02-05 Taurus Des, Llc Electrical energy storage system with battery power setpoint optimization using predicted values of a frequency regulation signal
US10222427B2 (en) 2015-10-08 2019-03-05 Con Edison Battery Storage, Llc Electrical energy storage system with battery power setpoint optimization based on battery degradation costs and expected frequency response revenue
US10222083B2 (en) 2015-10-08 2019-03-05 Johnson Controls Technology Company Building control systems with optimization of equipment life cycle economic value while participating in IBDR and PBDR programs
US10250039B2 (en) 2015-10-08 2019-04-02 Con Edison Battery Storage, Llc Energy storage controller with battery life model
US10283968B2 (en) 2015-10-08 2019-05-07 Con Edison Battery Storage, Llc Power control system with power setpoint adjustment based on POI power limits
US10389136B2 (en) 2015-10-08 2019-08-20 Con Edison Battery Storage, Llc Photovoltaic energy system with value function optimization
US10418832B2 (en) 2015-10-08 2019-09-17 Con Edison Battery Storage, Llc Electrical energy storage system with constant state-of charge frequency response optimization
US10742055B2 (en) 2015-10-08 2020-08-11 Con Edison Battery Storage, Llc Renewable energy system with simultaneous ramp rate control and frequency regulation
US11258287B2 (en) 2015-10-08 2022-02-22 Con Edison Battery Storage, Llc Using one-step ahead prediction to determine battery power setpoints
US11210617B2 (en) 2015-10-08 2021-12-28 Johnson Controls Technology Company Building management system with electrical energy storage optimization based on benefits and costs of participating in PDBR and IBDR programs
US10564610B2 (en) 2015-10-08 2020-02-18 Con Edison Battery Storage, Llc Photovoltaic energy system with preemptive ramp rate control
US11296511B2 (en) 2015-10-08 2022-04-05 Con Edison Battery Storage, Llc Energy storage controller with battery life model
WO2017062914A1 (fr) * 2015-10-08 2017-04-13 Johnson Controls Technology Company Système d'énergie photovoltaïque avec optimisation de fonction de valeur
US10700541B2 (en) 2015-10-08 2020-06-30 Con Edison Battery Storage, Llc Power control system with battery power setpoint optimization using one-step-ahead prediction
US10418833B2 (en) 2015-10-08 2019-09-17 Con Edison Battery Storage, Llc Electrical energy storage system with cascaded frequency response optimization
US11009251B2 (en) 2015-10-08 2021-05-18 Con Edison Battery Storage, Llc Electrical energy storage system with variable state-of-charge frequency response optimization
US10855081B2 (en) 2015-10-08 2020-12-01 Con Edison Battery Storage Llc Energy storage controller with battery life model
US10778012B2 (en) 2016-07-29 2020-09-15 Con Edison Battery Storage, Llc Battery optimization control system with data fusion systems and methods
US10594153B2 (en) 2016-07-29 2020-03-17 Con Edison Battery Storage, Llc Frequency response optimization control system
US11258260B2 (en) 2016-07-29 2022-02-22 Con Edison Battery Storage, Llc Battery optimization control system with data fusion systems and methods
US10554045B2 (en) 2016-12-20 2020-02-04 Siemens Aktiengesellschaft Control unit
EP3340416A1 (fr) * 2016-12-20 2018-06-27 Siemens Aktiengesellschaft Unité de commande
CN107528344A (zh) * 2017-09-27 2017-12-29 中国电力科学研究院 一种光储一体化发电装置并网运行控制方法及系统
US11163271B2 (en) 2018-08-28 2021-11-02 Johnson Controls Technology Company Cloud based building energy optimization system with a dynamically trained load prediction model
US11159022B2 (en) 2018-08-28 2021-10-26 Johnson Controls Tyco IP Holdings LLP Building energy optimization system with a dynamically trained load prediction model
US11606062B2 (en) 2019-02-08 2023-03-14 8Me Nova, Llc Coordinated control of renewable electric generation resource and charge storage device
US11641177B2 (en) 2019-02-08 2023-05-02 8Me Nova, Llc Coordinated control of renewable electric generation resource and charge storage device
US11532943B1 (en) 2019-10-27 2022-12-20 Thomas Zauli Energy storage device manger, management system, and methods of use
US11081887B1 (en) 2020-05-04 2021-08-03 8Me Nova, Llc Systems and methods utilizing AC overbuilt renewable electric generation resource and charge storage device providing desired capacity factor
US11031781B1 (en) 2020-05-04 2021-06-08 8Me Nova, Llc Method for implementing power delivery transaction for potential electrical output of integrated renewable energy source and energy storage system facility
US11588329B2 (en) 2020-05-04 2023-02-21 8Me Nova, Llc Method for implementing power delivery transaction for potential electrical output of integrated renewable energy source and energy storage system facility
US11831161B2 (en) 2020-05-04 2023-11-28 8Me Nova, Llc Systems and methods utilizing AC overbuilt renewable electric generation resource and charge storage device providing desired capacity factor
US12088100B2 (en) 2020-05-04 2024-09-10 8Me Nova, Llc Method for controlling integrated renewable electric generation resource and charge storage system providing desired capacity factor

Similar Documents

Publication Publication Date Title
WO2015139061A1 (fr) Système de commande de vitesse de montée et procédés utilisant des dispositifs accumulateurs d'énergie
CN109494723B (zh) 一种微电网系统及其控制与发电量预测方法
Eltamaly et al. Load management as a smart grid concept for sizing and designing of hybrid renewable energy systems
Ranaweera et al. Optimization of operational cost for a grid-supporting PV system with battery storage
Root et al. Using battery energy storage to reduce renewable resource curtailment
Hemmati et al. Short-term bulk energy storage system scheduling for load leveling in unit commitment: modeling, optimization, and sensitivity analysis
Atwa et al. Adequacy evaluation of distribution system including wind/solar DG during different modes of operation
US9568900B2 (en) Systems and methods for regulating an alternative energy source that is decoupled from a power grid
US9639904B2 (en) Systems and methods for minimizing energy costs for a power consumption system that has access to off-grid resources
TW202236785A (zh) 用於控制整合可再生發電資源之方法及提供所需容量因子之電荷儲存系統
CN112425022A (zh) 大型可再生峰化装置、紧密耦合的太阳能光伏和储能
KR101871236B1 (ko) 태양광 연계형 멀티 ess의 충방전 밸런싱 제어장치
CN104220945A (zh) 用于从分布式能源调度电能的方法和设备
Moumouni et al. Power “smoothing” of a commercial-size photovoltaic system by an energy storage system
Salehi et al. Ramp rate control of photovoltaic power plant output using energy storage devices
Ru et al. Exact sizing of battery capacity for photovoltaic systems
Gao et al. Annual operating characteristics analysis of photovoltaic-energy storage microgrid based on retired lithium iron phosphate batteries
Dawn et al. Efficient approach for establishing the economic and operating reliability via optimal coordination of wind–PSH–solar‐storage hybrid plant in highly uncertain double auction competitive power market<? show [AQ ID= Q1]?>
Liu et al. Evolution towards dispatchable PV using forecasting, storage, and curtailment: A review
Benavides et al. Experimental validation of a novel power smoothing method for on-grid photovoltaic systems using supercapacitors
Abadi et al. Active management of LV residential networks under high PV penetration
CN115313508A (zh) 一种微电网储能优化配置方法、装置及存储介质
Zupančič et al. Advanced peak shaving control strategies for battery storage operation in low voltage distribution network
Chen et al. Grid resilience with high renewable penetration: A PJM approach
Wang et al. Prospects for renewable energy: Meeting the challenges of integration with storage

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 15761913

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

32PN Ep: public notification in the ep bulletin as address of the adressee cannot be established

Free format text: NOTING OF LOSS OF RIGHTS PURSUANT TO RULE 112(1) EPC (EPO FORM 1205A DATED 31.03.2017)

122 Ep: pct application non-entry in european phase

Ref document number: 15761913

Country of ref document: EP

Kind code of ref document: A1