AU2021105856A4 - Bi-level energy management system with grid reinforcement for solar energy - Google Patents

Abstract

Bi-level energy management system with grid reinforcement for solar energy Abstract: Renewable energy's commercial and ecological advantages have grown in importance during the last decade. Localized energy markets can help with the energy transformation by allowing the quick multiplication of RES, improving the power grid's natural renewable energy hosts capacity. This invention presents energy management systems for the smart city that allows customers to trade local energy via renewables units, a CSF, as well as a power grid. This invention develops two optimizing strategies for distributing extra locally generated energy. The very first framework maximizes sellers' and the buyers' aggregate revenue, whereas the secondary, a game theory model, maximizes customer use at the lesser level and indeed the yield of the shared storage facility at the greater level. An extensive study is being conducted to look at the advantages of energy distribution to increase entire revenue. Instead of comprehending the deterministic variance of unknown parameters, this identical solution plan is proposed as a Lyapunov augmentation, which relies only on the real pricing of unknown factors like as load variations and renewable generation. The suggested system can follow the rapid changes in RTP and the solar production efficiently, according to rigorous modeling using real-time information from four DUs. The supremacy of the developed energy managing portfolio is demonstrated when compared to two benchmark techniques, namely centralized process, and a greedy algorithm. 1

Description

Bi-level energy management system with grid reinforcement for solar energy

Networks Field of the Invention:

This invention involves the development of the two-level system for management of the energy. This invention uses grid reinforcement for this development. This invention mainly focuses on the solar energy for is energy management system.

Background of the Invention:

An energy supply and needs have increased very fast in such times of a smart grid environment, one among the top crucial matters in a smart grid environment, a common problem is the emerging of an Energy Management System(EMS) to attain the target resulting in reduced consumption of energy, maintain the needs and the energy levels are balanced, reducing the charges for energy, RES usage has been increased. Therefore, energy management inside a smart grid is indeed a difficult undertaking because ofnumerous informative unknowable's regarding elements that vary with time, like as loading needs, energy costs, and the quantity with energy generated. For illustrate, the quantity of energy produced by a renewable energy source (RES) including a photovoltaic (PV) system is heavily impacted by changing the climate.

The unpredictability of meteorological circumstances makes predicting the quantity of energy generated challenging, resulting to difficulties in energy planning. During recent times, several smart grid research organizations have worked on enhancing EMS during a variety of settings. Several research, involving energy management under Thermostatically Controlled Loads, have been conducted (TCLs). Such studies attempted to determine the best TCL controlling measures in attempt to minimizing the consumption of energy in a smart grid or even to satisfy the requirements of auxiliary activities while ensuring comfort conditions for user. According to the United States Energy Information Administration, structures consumed over 20% of energy provided globally in the year 2016. The proportion is anticipated to rise every year till 2040 via an aggregate of 1.5 percentage. Several research organizations have constantly tried to create EMS with smart energy architecture to plan for just a substantial quantity of buildings which consumes energy. A smart energy structure computerizes its energy activity to achieve perfect energy usage is an information technology (IT) oriented structure.

Approximation Reinforcement Learning (RL) technique for administration of a networked Microgrids (MG) in the electric distributor's bi-level capacity. In practice, the cooperated agent possesses little or no awareness of both the MG assets behavior and complex model underlying a Point of Common Coupling (PCC).Obstructs traditional optimization methods for a limited MG power administration challenge which renders the distribution networks undetectable. To address this issue, we introduced a bi-level RL structure in a cost related setting. The cooperative agent conducts functional approximating to anticipate the behavior of organizations under imperfect knowledge of a MG parametric designs at the upper layer, whereas every MG offers a power flow restrained optimum reaction to the price signals just at lowest layer. After that, the functional approximating technique is employed inside an adaptable RL framework to optimize a price signal when system loads, and solar production differ. Numerical studies have shown that, when related to prior research in the research, the suggested that privacy-preserving learning system is more adaptable and faster.

The energy management system to develop smart energy building that was linked to such an outside grid, and distribution resources of energy such as the renewable energy sources, systems for energy storage, the vehicle-to-grid station. The smart grid makes it easier to control the energy in produced in electrical grid system. In comparison to previous randomized and the non-learning-based techniques, the outcomes of mathematical simulated world driven on information obtained in real settings demonstrate that now the suggested energy management method steadily decreases energy expenditures via the learning processes.

Three current distributed learning framework and then reviewed the applications suggested for those but in the power systems yet. Machine learning approaches also found various applications for a power technology in the recent years, including capacity forecasting, voltage regulation, power grade monitoring, an anomaly detection, and so on. Distributed learning is indeed a branch of the machine learning and descendent of the area of multi-agent systems Distributed learning is defined as cooperatively decentralized machine learning method that is meant to manage huge amounts of data, tackle complicated learning issues, and enhance confidentiality. Furthermore, as comparing with a completely centralized methods, this can lessen the danger of a singular points of failures and minimize the bandwidth and the central capacity needs. It outlines the methodologies, advantages, and problems of a distributed learning framework in a power system, as well as identifying literature shortages for additional research.

A system for energy management of smart city that enables localized energy trading by supplying customers with alternative energy unit, the central storing facilities, and a power grid. Renewable energy has grown its importance in terms of both economic and ecological advantages during the last decades. Local energy marketplaces play an important part in the energy transformation by allowing the fast multiplication of renewable energy supplies, therefore improving renewable energy of power grid's carrying capability.

A data-driven strategy that uses a reinforcement learning to control the optimum energy usage of a digital house equipped with a roofing solar photovoltaic device, an energy storage device, and the smart home gadgets. The suggested methodology differs from previous model-based optimizing techniques for a home energy management devices in the following ways: (1) A model-free Q-learning technique is used to schedule energy usage for a single controlled household gadget (such as an air conditioner or a washing machine), and also the recharging and discharge of an energy storing device and (2) The use of an artificial neural network to forecast the inside temperatures aids the suggested Q-learning method in properly learning the link between both the inside temp as well and indeed that energy usage of an air conditioner.The suggested Q-learning home energy management method, when combined with an artificial neural network model, lowers the customer power cost while maintaining the desired level of comfort (also including interior temperature) appliance functioning characteristics. These simulators depict a single house with the solar photovoltaic system such as a washing machine, and an AC, and a moment priced energy storing system. The studies demonstrate that the suggested method saves 14 percent on power bills when compared to the present optimization approach.

Objects of the Invention:

1. This invention involves the development of the two-level system for management of the energy. This invention uses grid reinforcement for this development. This invention mainly focuses on the solar energy for is energy management system.

2. A bidirectional power and telecommunications link connects the region, but every customer is personally linked to both the utility grid. The suggested community-based energy management systems with interaction protocols and methodologies, we feel, might be effectively implemented. 3. The Central Storage Facility is charged using electricity generated mostly via entity's photovoltaic system. Customer could purchase power directly from Central Storage Facility, or it could be resold to just the grid at a Market Clearing Price (MCP).

Summary of the Invention:

Community-Based Energy Management System:

This invention presents a Community Energy Management System (CB-EMS) to allow localized P2P trade amongst community's users using DER devices, a Central Storage Facility (CSF) owned by the group, and a power grid. The major novel contributions of this work is to define the interactions issue for local power distribution as a Stackelberg game depending on bi-level optimization in order to maximize the utilities of all the parties involved. These invention's significant achievements are as follows:

1. Develop and deploy a CB-EMS system enabling allotment of resources coordination and peer-to-peer energy trade.

2. Create a bi-level enhancement energy transfer model that maximizes CSF profits just at top and the customer utility functions at the bottom.

3. Develop a utility function for customer P2P sharing that considers their choices, comforts, and desire to pay.

4. Create a Renewable Energy Resources price structure for the Central Storage Facility and assess the effect of various utilities and Central Storage Facility pricing schemes upon total revenue.

Throughout this part, the CB-EMS' suggested framework is described. The proposed framework for both the CB-EMS is depicted in Figure 1 as a systems overall view. The set U represents the community, which is made up of various users. To ensure consistent power usage throughout the day, certain nearby users are classified as small companies with high loads in the middle of the day, whereas residential customers have their high loads throughout the later afternoon. Each consumer, u 2 U, as thought to get a collection of power-hungry loads, P. Shift able loads "n" and base load "n" make up these loads.

Base loads are devices which are operated continuously throughout the day, like refrigerators and hospital instruments. A bidirectional power and telecommunications link connects the region, but every customer is personally linked to both the utility grid. A smart meter records customer data such as consumption, production, and energies traded, and transmit the data to the regional controllers only through communication link. The controllers are in charge of managing the numerous appliances on site.

Because the amount of power exchanged is smaller, there are fewer power outages and fewer communication link breakdowns and the latency difficulties. The suggested community-based energy management systems with interaction protocols and methodologies, we feel, might be effectively implemented. For more effective schedule, the controller may alter the consuming trends of shift able loads like an electric vehicle and dishwasher. On-site solar cells have been deployed on the roofs of the customers, but again no battery.

User's initially use personally manufactured PV power to fulfill their personal need, then exchange the surpluses with neighbors, a community solar farm, or by using the grid. The amount of power transferred between participants inside the game is determined by the grid pricing, real need, and the purchasers' readiness to pay for a renewable energy generated from diverse sources. The Central Storage Facility is managed by a distinct body. For the sake of this research, it is believed that perhaps the Central Storage Facility can only be collected by the object's Photovoltaic panels or additional electricity supplied by customers' Renewable Energy Sources, not through the grid. The Central Storage Facility is charged using electricity generated mostly via entity's photovoltaic system. Customer could purchase power directly from Central Storage Facility, or it could be resold to just the grid at a Market Clearing Price (MCP). Owing of the reduced feed-in tariff, transferring RES produced excess power to this same load by customers is often the final choice.

Consumers' goal would be to lower their utilities bills by utilizing the cheapest energy. The most expensive alternative is to purchase electricity from the grid. First, per day planning model based upon Mixed-Integer Linear Programming (MILP) is created to optimize energy utilization and hence reduce energy expenses without sacrificing customer pleasure.

Zero indicates plan for shift able load' after the user's specified events time has passed. The approach allows consumers to acquire an energy consumption overview for shift able equipment. Those schedules are used as a standard whenever the CSF Company and competition set tariffs for energy to also be exchanged again. In adding to the day optimizing model, the invention presents two more optimization strategies. By combining consumer utility and CSF entity profits, the original design maximized local RES generation.

The utility function, that represents the opinions of the prosumers, defines them. The optimizing approach considers the excess power produced by customers and the Central Storage Facility to optimize aggregates connected to the system. Since trading, excess power is resold to the grid just at MCP, PMCP. The income split between the Central Storage Facility and the prosumers is determined by the price plan used. The next optimization is a bi-level approach in which the Central Storage Facility owner is informed of customer desires, energy usage schedules, and the onsite power production.

In the suggested Stackelberg game, each localized energy markets agents understand their Renewable Energy Sources output and demands at 30 minutes time span in advance. The Stackelberg game's leadership is the Central Storage Facility owner, and the following are the customers. The CSF enterprise seeks to maximize revenue after applying the Stackelberg game, whereas the prosumers try to maximize their utility functions. This is a good income model for the Central Storage Facility Company, but that also means that resources are allocated inefficiently.

Detailed description of the Invention:

CB-EMS Optimization Problem The optimization model for the central building management system (CB-EMS) is given in this part. CB-EMS, like virtual energy trading software, serves as aggregators.

Q(Ct, Pbuy,tt)CRTP tbuy,t e EN C,t5,t t- l EN (1)

Here,CRTP,t is the actual cost of the energy bought from either the nearby DNO in $/kilo Watt per hour and Pbuy,t refers to the quantity of energy bought from the nearby DNO in kW. CB-EMS will encourage DUs to control load by finding the proper VRTEP settings for each customer. Because the occupiers vary from one another, the VRTEP parameters derived should be uneven. However, to re-establish trust in the suggested energy management approach, VRTEP readings must not exceed a certain threshold. Similarly, CB-EMS must consider the relevant restrictions while determining VRTEP value:

EIEN C,t= CRTP,t, Vt (2) o C,t CRTP,t,v G N (3)

Condition (2) would unequally divide the real RTP amongst these DUs, while condition (3) will prevent the VRTEP values from exceeding the real RTP. CB-EMS, like any other energy distributor, is responsible for ensuring actual stockpile balancing by optimising battery power (Pb,t,kW) and DNO utilization. The CB-EMS should minimise the lengthy net payoff, much as the DU optimizing framework did earlier. As a result, the optimizing framework would be transformed into a stochastic optimization system, as indicated in the diagram below. min lim 1 - E[Q(Clt, Puy,t, ,t)] (4) CI,tVI,Pb,t,Pbuy,t T-ao T Related with (2) and (3), Sb,(t+1) = So,t + Pb,tAt (5) S < S,t Smax and - Pb Pb,t P (6) lEN -l,t + bt -r,t =Pbuy,t (7) Pbuy,t Pbuy (8)

Solution of CB-EMS Optimization Problem The CB-EMS optimum problem, like a DU problem, includes a time averaged stochastic target function. The lengthy net payoff stated in equations (9) is heavily influenced by charging mode, which should be controlled to prevent charging at higher prices and disposing at lower prices. However, making a judgement about peaking as well as off price times in actual time is a difficult process. As a result, it is prudent to restrict the batteries unit's functioning by keeping its energy state consistent to avoid unexpected charging or draining. As a result, the battery unit uses a virtualized queue Ib,t to collect the power supply exchange data. Ib,(t+1) = Ib,t + P,tAt (9)

So each charging and discharging action updated the batteries queue, which uses the really same data as the high battery indicator, St.To keep the battery from running out, the average waiting line should really be consistent, as demonstrated below. lim E[lIb,t]/t = 0 (10) t->oo0

Hence for the status of the battery queuing, the Lyapunov function is used Ib,t is denoted as 9N||(Ib,t) = 0.5 Ib2,t (11)

For every time interval, the conditioned Lyapunov drifting is defined by A(Ib,t) = E[91(Ib,(t+1)) - N(Ib,t Ib,t (12)

Because the minimizing of this drifting and the target functional are incompatible, the accompanying multiple objectives minimizing problems is used,

Wt = A(Ib,t) + GE [Q(ct, Pbuy,t,t) Ib,t], G> 1 (13)

Here, G represents the positive tradeoffs stable

Every slot of time t, Wt has upper bounds is denoted as,

Wt !; A + Ib,t E [Pb,tAt Ib,t] + GE [Q(ci,t, Pbuy,t, 5I,t) Ib,t] (14)

Here, A=0.5(Pb) 2 At 2 (15)

Proof: The Wtdenoted by upper bound is decreased, to decrease the real value of Wt. As a result, the CB-EMS optimization issue is renamed,

minA Z(A,5t) = (Ib,tPb,tAt) + GQ(ct, Pbuy,tt) (16)

Respect to the equation above Feasibility strategy of the sets CB-EMS is represented by:

FCB ld,t Pbuy,t> bI0,t N RNPb,t Pbuy,t GR (17)

Claims (3)

Bi-level energy management system with grid reinforcement for solar energy Claims: The invention of development of method for the dual-level energy management system using the solar energy with the use of grid reinforcement has the following claims:

1. This invention involves the development of the two-level system for management of the energy. This invention uses grid reinforcement for this development. This invention mainly focuses on the solar energy for is energy management system. From claim 1, A bidirectional power and telecommunications link connects the region, but every customer is personally linked to both the utility grid. From claim 1, A smart meter records customer data such as consumption, production, and energies traded, and transmit the data to the regional controllers only through communication link.

2. A bidirectional power and telecommunications link connects the region, but every customer is personally linked to both the utility grid. i. From claim 2, The suggested community-based energy management systems with interaction protocols and methodologies, we feel, might be effectively implemented. ii. From claim 2, To ensure consistent power usage throughout the day, certain nearby users are classified as small companies with high loads in the middle of the day, whereas residential customers have their high loads throughout the later afternoon.

3. The Central Storage Facility is charged using electricity generated mostly via entity's photovoltaic system. i. From claim 3, Customer could purchase power directly from Central Storage Facility, or it could be resold to just the grid at a Market Clearing Price (MCP). ii. From claim 3, The approach allows consumers to acquire an energy consumption overview for shift able equipment. Those schedules are used as a standard whenever the CSF Company and competition set tariffs for energy to also be exchanged again.