Community Energy Storage Systems Research Articles

A Stackelberg Game Theory Model for Integrated Community Energy Storage Systems

A Stackelberg Game Theory Model for Integrated Community Energy Storage Systems

Deep Reinforcement Learning-Based Explainable Pricing Policy for Virtual Storage Rental Service

The shared community energy storage system (CESS) can reduce energy storage costs by exploiting the complementarity of end-users and economies of scale. To further improve the economic feasibility of the CESS, we propose a novel business model and pricing method for the virtual storage rental service (VSRS). In this model, the rental users aim to minimize the electricity bill by renting the virtual capacity and optimizing its operation, while the CESS operator seeks to maximize the revenue from the combination of energy arbitrage and the VSRS. The pricing problem and the optimal operation problems of the CESS and users’ virtual batteries are modeled as a bi-level optimization problem. Next, the proposed problem is solved through transformer-based deep deterministic policy gradient (TDDPG) method and mixed-integer linear programming (MILP) due to the non-convexity and non-continuity of the original problem. The post-hoc interpretability of the policy network is provided based on the Shapley value to reveal the importance of different input features for decision-making. Numerical simulations suggest that the proposed VSRS could benefit the CESS operator and users. Moreover, the explanation based on the Shapley value could effectively generate an implicit solution for understanding the policy network.

Deep learning based optimal energy management framework for community energy storage system

This paper proposes a deep learning-based integrated framework for multiple cooperative households to achieve optimal energy distribution. The corresponding energy generation and consumption problems are formulated by a long short-term memory algorithm is combined with an optimization algorithm to produce an optimal solution. In this study, a PV-community energy storage system (CESS) integrated is considered where the scheduling decision of the CESS and utility grid can be subsequently achieved through formulated constraints. The test results demonstrate the efficacy and robustness of the proposed system that achieves superior performance on effective renewable energy usages of maximum 31.74% in a home environment.

Community energy storage system: Deep learning based optimal energy management solution for residential community

The concept of community energy storage system (CESS) is required for the efficient and reliable utilization of renewable energy and flexible energy sharing among consumers. This paper proposes a novel approach to assess the practical benefits of CESS deployment in a residential community by decreasing the daily electricity cost and maximizing the self-consumption of PV energy. To this end, a deep-learning-based forecasting model, namely a bi-directional long short-term memory model, is implemented to predict the operational constraints and dependency. Furthermore, a hybrid optimization technique that comprises a clustering and optimization algorithm is developed in which the clustering algorithm ensures appropriate combinations of user groups to develop optimal control policies. Finally, the forecasting model is integrated with the hybrid optimization algorithm to find the optimal solution involving PV-CESS energy utilization. Numerical analyses are performed using real historic data of the energy demand and PV generation for three consecutive days considering different scenarios. The results demonstrate that the electricity costs and self-consumption associated with the CESS are lower and greater than those of an individual ESS system, respectively, with the daily electricity cost decreasing by 21.89%, 13.81%, and 7.66% in the three analyzed scenarios.

Integration of Local and Central Control Empowers Cooperation among Prosumers and Distributors towards Safe, Efficient, and Cost-Effective Operation of Microgrids

The advent of energy communities will revolutionize the energy market. However, exploiting their full potential requires innovations in the structure and management of low-voltage grids. End users shall be aggregated within microgrids, where their physical interaction is possible and coordinated operation of power sources and energy storage systems can be achieved. Moreover, meshed network topologies will enable multiple paths for the power flow. The combination of smart control and meshed networks can dramatically improve microgrid performance in terms of power quality, efficiency, and resilience to transients and faults. Ubiquitous control of the power flow becomes possible, as well as active fault clearing and isolation of subgrids without tripping circuit breakers. This paper proposes a control approach that pursues such goals without requiring modification of control and communication hardware implemented in commercial inverters. Instead, a revision of control firmware, integrated with local measurements, allows retrofitting existing plants to improve microgrid operation. Further improvements may derive from the installation of community power sources and energy storage systems, which can extend microgrid operation to pursue demand response and islanding. The potential of the proposed control methods is demonstrated by simulation considering a standard microgrid under different operating conditions.

Two-Stage Community Energy Trading Under End-Edge-Cloud Orchestration

The end-edge-cloud orchestration of the virtual power plant (VPP) enables the edge server to timely serve community users. By deploying the community energy storage system (CESS) and the community peer-to-peer (P2P) market, prosumers can form energy communities to achieve self-sufficiency of energy and independence from fuel-based power generators. This article proposed a two-stage community energy trading model under end-edge-cloud orchestration. The community P2P trading is the first stage where the edge server can execute the automatic bidding process for multiple buyers and sellers based on the real-time users’ energy profiles and the Bayesian-game-based pricing mechanism. The trading between the retailer and energy communities is the second stage where the edge server can dynamically update the optimal operation of the CESS based on the dynamic pricing mechanism. An original centralized optimization problem is decomposed into subproblems for each stakeholder and solved through the alternating direction method of multipliers (ADMM). Considering ADMM needs multiple information exchanges, a general form of the communication-censored ADMM for sharing problems is proposed to decrease the communication cost. Numerical simulations prove that the proposed mechanism can effectively increase transaction efficiency, avoid the new demand peak brought by the utilization of the CESS, and decrease the communication cost.

A Reputation Management System for the Fair Utilization of Community Energy Storage Systems

This paper investigates the implementation of a community energy storage system (CESS) in a neighborhood consisting of households with flexible and inflexible loads, as well as photovoltaic power generation. The system incorporates overlay services (OS) such as fairness management, increasing the fairness level while maximizing CESS utilization, and reputation management, reducing the impact of households trying to take advantage of the system. Additionally, a credit scheme for households, referred to as the quota system, is introduced, allowing households to only import from the battery the amount of energy they have previously contributed. This quota system includes a way to trade credit with other households, as part of the OS. The case study shows that the introduction of the OS on top of a CESS of 50 kWh results in a drop in peak transformer load from 235.7% to 166.9%, with the peak load at 264.3% without a CESS present. Additionally, it is shown that introducing these OS in a CESS does not significantly impact the mean household cost.

Control and Hardware-in-the-Loop Simulation of Community Energy Storage Systems Based on Repurposed Electric Vehicle Batteries

Control and Hardware-in-the-Loop Simulation of Community Energy Storage Systems Based on Repurposed Electric Vehicle Batteries

An Online Scheduling Algorithm for a Community Energy Storage System

In this paper, we consider a community energy storage (CES) system that is shared by various electricity consumers who want to charge and discharge the CES throughout a given time span. We study the problem facing the manager of such a CES who must schedule the charging, discharging, and capacity reservations for numerous users. Moreover, we consider the case where requests to charge/discharge the CES arrive in an online fashion and the CES manager must immediately allocate charging power and energy capacity to fulfill the request or reject the request altogether. The objective of the CES manager is to maximize the total value gained by all of the users of the CES while accounting for the operational constraints of the CES. We discuss an algorithm titled COmmunityEnergyScheduling that acts as a pricing mechanism based on online primal-dual optimization as a solution to the CES manager’s problem. The online algorithm estimates the dual variables (prices) in real-time to allow for requests to be allocated or rejected immediately as they arrive. Furthermore, the proposed method promotes charging and discharging cancellations to reduce the CES’s usage at popular times and is able to handle the inherent stochastic nature of the requests to charge/discharge stemming from randomness in users’ net load patterns and weather uncertainties. Additionally, we are able to show that the algorithm is able to handle any adversarially chosen request sequence and will always yield total welfare within a factor of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\frac {1}{\alpha }$ </tex-math></inline-formula> of the offline optimal welfare.

Grid-connected photovoltaic battery systems: A comprehensive review and perspectives

Due to the target of carbon neutrality and the current energy crisis in the world, green, flexible and low-cost distributed photovoltaic power generation is a promising trend. With battery energy storage to cushion the fluctuating and intermittent photovoltaic (PV) output, the photovoltaic battery (PVB) system has been getting increasing attention. This study is conducted to comprehensively review the PVB system studies with experimental and simulation studies, concerning mathematical modelling, system simulation, evaluation, capacity and operation strategy optimization. The system profitability, storage system choice and grid influence are also discussed, with the multi-objective optimization in large-scale systems with various participants expected as the future trend. In addition, several highlights of this topic are discussed in detail, including model predictive control, demand-side management, community energy storage system, peer-to-peer energy market scheme and mutual impacts on grid and system, emphasizing system flexibility, optimization complexity, large-scale simulation and the utility grid influences. Furthermore, some challenges and perspectives for future research are presented, with respect to DC distribution system revolution, large-scale vehicle to grid designs and impacts, multi-energy network operation and participation in the carbon transaction market. It is expected that this study could provide useful references and suggestions for researchers in this field of system design and power management of distributed solar PV.

A hierarchical approach for P2P energy trading considering community energy storage and PV‐enriched system operator

The requirements to reduce dependence on fossil fuels and minimize harmful emissions necessitate using renewable energy more effectively. In this regard, a Peer-to-Peer (P2P) energy trading market in which excess power is sold to neighbors is emerging in place of the typical market where excess power is sold back to the grid. In this study, a bi-level optimal bidding strategy is proposed in which community energy storage systems (CES) and community photovoltaic (CPV) generation are considered. Moreover, individual PV and CES are taken into account for each household. While optimal scheduling of prosumers is carried out at the lower level, the local clearing price considering the profit of Energy Sharing Provider (ESP) is obtained at the upper level. Furthermore, various case studies consisting of Peer-to-Grid (P2G), P2P, and CES-supported P2P are created under different storage capacities. The devised mixed-integer linear programming (MILP) based model is tested using each case study, validating the proposed model and proving its effectiveness.

Community energy storage operation via reinforcement learning with eligibility traces

The operation of a community energy storage system (CESS) is challenging due to the volatility of photovoltaic distributed generation, electricity consumption, and energy prices. Selecting the optimal CESS setpoints during the day is a sequential decision problem under uncertainty, which can be solved using dynamic learning methods. This paper proposes a reinforcement learning (RL) technique based on temporal difference learning with eligibility traces (ET). It aims to minimize the day-ahead energy costs while maintaining the technical limits at the grid coupling point. The performance of the RL is compared against an oracle based on a deterministic mixed-integer second-order constraint program (MISOCP). The use of ET boosts the RL agent learning rate for the CESS operation problem. The ET effectively assigns credit to the action sequences that bring the CESS to a high state of charge before the peak prices, reducing the training time. The case study shows that the proposed method learns to operate the CESS effectively and ten times faster than common RL algorithms applied to energy systems such as Tabular Q-learning and Fitted-Q. Also, the RL agent operates the CESS 94% near the optimal, reducing the energy costs for the end-user up to 12%.

A Study on Control Strategies for Aggregated Community Energy Storage Systems in Medium Voltage Distribution Networks

Community Energy Storage Systems (CESSs) emerge as an innovative way to integrate batteries into Low Voltage (LV) and Medium Voltage (MV) distribution networks to provide ancillary services and improve the quality of energy received by the end user. However, since CESSs are still emerging technologies, there is much research space in this field for proposing innovative and economic control algorithms for such devices. Thus, this paper presents a study of four control strategies applied to an MV distribution network, i.e., peak shaving, line losses control, line congestion reduction, and system voltage control, through an Aggregated Community Energy Storage System (ACESS), which is represented as the sum of multiple CESSs connected in LV systems, viewed from MV side. The proposed strategies are based on Model Predictive Control (MPC), a technique that, using the future demand forecast data, calculates the dispatch of ACESS. The results show that the proposed control strategies are effective for the designed goals, although some improvements can be made on the voltage control algorithm. Moreover, for estimating the service lifetime of the ACESS after applying the control algorithms, the Rainflow Counting Algorithm (RCA) is applied, showing that, regardless of the control strategy, the degradation is inversely proportional to the storage capacity.

A priority-based approach for peer-to-peer energy trading using cooperative game theory in local energy community

The energy sector is undergoing a paradigm shift to integrate the increasing volume of embedded renewable energy generation and create Local Energy Communities (LEC). Peer-to-Peer (P2P) energy trading is an encouraging paradigm used to increase usage of renewable energy, decrease consumers' electricity bills, and provide revenue to prosumers. It also improves the usage of Distributed Energy Resources (DERs) in the smart grid and reduces transmission and distribution losses. However, challenges such as unpredictability and intermittency of DER's may result in instability of P2P energy trading. In our work, we propose a cooperative game theory framework to expedite stable trading algorithms and incentivize individual users. This trading algorithm offers various priorities at each time interval depending on parameters such as geographic location, maximum energy demand, maximum energy generated, and pricing mechanism. We have considered a grand coalition whose objective is to maximize the coalition's social welfare and ensure a win-win approach for both consumers and prosumers. Hence the grand coalition made by the cooperative game is in Nash equilibrium as no peer wants to perform the merge and split from its current location. In the proposed algorithm, LEC includes 100 players (50 prosumers and 50 consumers), a community energy storage system (CES), and 15 Electric Vehicle charging points. The best operational output priority was also evaluated in this work for each time interval with associated distributed solar PV and CES. Results strongly support that using the best suitable priority for each time interval is beneficial rather than having one priority for an entire day. An economic analysis to distribute the revenue generated from the grand coalition in a fair manner is analyzed in this work. From the economic evaluation, it is apparent that prosumers have high revenue, and consumers save electricity bills when using the proposed algorithm.

Prioritized Replay Dueling DDQN Based Grid-Edge Control of Community Energy Storage System

This paper develops a new prioritized replay dueling DDQN (PRD-DDQN) method for grid-edge control of community energy storage system with good robustness to uncertainties and fast convergence speed while achieving good control performance. The control problem is first formulated as a Markov decision process (MDP) considering the current time interval, state of charge of the CESS, the price signals, and the pre-trading power between MG and CESS/utility grid. Unlike the double deep Q-network-based method to solve this MDP, the proposed PRD-DDQN endows the agent with a powerful capability of learning by interacting with a more complex environment. This is due to the collaboration of the dueling structure and prioritized replay policy based on the sum-tree. As a result, the control accuracy, model robustness, and algorithm convergence speed are significantly enhanced. Besides, the proposed algorithm supports minute-level and multi-agent parallel control. Comparison results with a deterministic model-based method and other deep reinforcement learning-based methods demonstrate the effectiveness and superiority of the proposed approach.

Joint investment of community energy storage systems in distribution networks using modified Nash bargaining theory

The emergence of renewable energy technologies in distribution networks and microgrids has raised the importance of integrating energy storage systems into these grids. However, their high investment costs deter decision makers from effectively expanding these assets. In this paper, a cooperative community storage expansion plan is proposed as a bargaining problem between distribution company and private microgrids to jointly invest in energy storage systems. By doing so, each party takes a quota of shared investment costs and thus the burden of high investment costs is alleviated. A modified version of the Nash bargaining theory approach is proposed to implement the cooperative framework in a fair manner. Two cases of non-cooperation and cooperation are distinctively defined and the merits of cooperation are illustrated. The lead acid battery is considered as the storage candidate, for which a novel linearized lifetime model and replacement approach is also proposed. The bargaining results presented on a distribution network test case indicate that through the proposed cooperation, all players receive positive surpluses by decreasing their costs or increasing their revenues. Thus, both distribution company and microgrids would have incentives to participate in the proposed cooperation. Moreover, the superiority of the proposed modified Nash bargaining theory compared to conventional Nash bargaining theory in terms of cooperation fairness is illustrated. Finally, the ability of the proposed cooperative community storage expansion plan to effectively manage cooperative storage installations and replacements is demonstrated.

A novel energy cooperation framework for community energy storage systems and prosumers

Energy trading between community energy storage systems (CESSs) and prosumers has received much attention recently. But few studies have considered the impact of network constraints on energy trading and how to share profits equitably. To address these issues, this paper proposes an efficient energy cooperation framework for CESSs and prosumers. Firstly, an energy cooperation platform is introduced as a manager to interact with all players and the distribution system operator (DSO), improving the interaction efficiency and having better scalability. Secondly, we present a bi-level energy trading model: at the lower level, each prosumer and CESS interact with the platform to determine their shared power profiles; at the upper level, the platform and DSO calibrate the shared scheme from the lower level with network constraints and generate trading adjustments to guide the lower level energy trading. Furthermore, a profit-sharing mechanism based on the asymmetric Nash bargaining model is designed, where the contribution rates are determined by the shared energy of players. More importantly, to avoid players' cheating behavior, the player's revenue before allocation is calculated by the platform. Additionally, the adaptive alternating directional multiplier method (ADMM) is employed for solving energy trading problems, where the penalty parameters can be adaptively adjusted to accelerate the convergence rate. Finally, the case studies verify the feasibility and fairness of the energy cooperation framework.

Day-Ahead Optimal Power Flow for Efficient Energy Management of Urban Microgrid

This study deals with the urban microgrid energy management that is dedicated to individual and collective self-consumption by providing flexibility to the distribution grid (DG). The proposed urban community microgrid is interconnected to a DG and it consists of an association of centralized storage units (called community energy storage system), community intermittent renewable generation, and intelligent energy management system (EMS). One of the main advantages of urban microgrid is that, in case of faults in the DG, it can cut existing interconnections and continue to supply the responsible community in the island mode. In this study, the developed urban microgrid EMS is based on the predictive control management through the day-ahead optimal power flow (DA-OPF) strategy. The main contributions of this work can be defined by two points. The first point is related to a development of the DA-OPF strategy for the urban microgrid based on the intelligent deep learning data forecasting and the mixed-integer nonlinear programming optimization methods. The second point concerns a development of an optimization function integrating the concept of ancillary services of DG flexibility. Experimental results and economical evaluation are presented in this article. By using the proposed strategies, it results in an important electricity price reduction for the considered urban microgrid, compared to a conventional distribution system and basic operation schemes.

Network-Aware Demand-Side Management Framework With A Community Energy Storage System Considering Voltage Constraints

This paper studies the feasibility of integrating a community energy storage (CES) system with rooftop photovoltaic (PV) power generation for demand-side management of a neighbourhood while maintaining the distribution network voltages within allowed limits. To this end, we develop a decentralized energy trading system between a CES provider and users with rooftop PV systems. By leveraging a linearized branch flow model for radial distribution networks, a voltage-constrained leader-follower Stackelberg game is developed wherein the CES provider maximizes revenue and the users minimize their personal energy costs by trading energy with the CES system and the grid. The Stackelberg game has a unique equilibrium at which the CES provider maximizes revenue and the users minimize energy costs at a unique Nash equilibrium. A case study, with realistic PV power generation and demand data, confirms that the energy trading system can reduce peak energy demand and prevent network voltage excursions, while delivering financial benefits to the users and the CES provider. Further, simulations highlight that, in comparison with a centralized system, the decentralized energy trading system provides greater economic benefits to the users with less energy storage capacity.

Improving the feasibility of household and community energy storage: A techno-enviro-economic study for the UK

Rooftop photovoltaics (PV) have become widely adopted by domestic customers in tandem with energy storage systems to generate clean energy and limit import from the grid, however most applications struggle to achieve profitability. The level at which energy storage is deployed, be it household energy storage (HES), or as a community energy storage (CES) system, can potentially increase the economic feasibility. Furthermore, the introduction of a Time-of-Use (TOU) tariff enables households to further reduce their energy costs through demand side management (DSM). Here we investigate and compare the performance of HES and CES with DSM. The results suggest that TOU tariffs can effectively shave peak demand by up to 30% and lower energy bills by at least 20%, but do not improve self-consumption or self-sufficiency rate. This study indicates that all cases considered are environmentally friendly and can pay back the total CO2 emissions associated with the manufacturing within 8 years. However, the levelised cost of storage (LCOS) is still beyond a household's affordability, ranging from £0.4 to £2.03 kWh−1, though CES is proven more effective at improving self-consumption for consumers and shaving peak demand for network operators. The feasibility can be improved by 1) combining different services and tariffs to obtain more revenues for households; 2) more legislative and financial support to reduce system costs; and 3) more innovative business models and policies to optimise revenues with existing resource.