电力交易在社区的应用分析

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To fully utilize the energy on the user side and establish a new integrated energy trading system to realize energy transactions among users, it is imperative to conduct research on the architecture and pricing models of energy trading systems. Based on the study of the application of blockchain technology in energy trading, this paper constructs a peer-to-peer (P2P) energy trading system using blockchain technology, enabling users to conduct energy transactions without the involvement of a third party. A dynamic energy pricing method based on game theory according to the supply–demand ratio (SDR) is proposed in this paper. The pricing model considers user satisfaction and energy supply–demand comprehensively, introduces the concept of game theory, and constructs an optimized microgrid trading model under the P2P information interaction state. This paper also discusses the application scenarios and operation processes of the P2P energy system, and carries out relevant tests. The test results show that the system has high performance and efficiency, and can meet the needs of energy trading. Finally, through simulation examples, it is proved that the pricing model proposed in this paper provides users with significant benefits and technical support, and can serve as a reference for the application of blockchain in P2P energy trading.

This paper proposes a novel game-theoretic model for peer-to-peer (P2P) energy trading among the prosumers in a community. The buyers can adjust the energy consumption behavior based on the price and quantity of the energy offered by the sellers. There exist two separate competitions during the trading process: 1) price competition among the sellers; and 2) seller selection competition among the buyers. The price competition among the sellers is modeled as a noncooperative game. The evolutionary game theory is used to model the dynamics of the buyers for selecting sellers. Moreover, an M-leader and N-follower Stackelberg game approach is used to model the interaction between buyers and sellers. Two iterative algorithms are proposed for the implementation of the games such that an equilibrium state exists in each of the games. The proposed method is applied to a small community microgrid with photo-voltaic and energy storage systems. Simulation results show the convergence of the algorithms and the effectiveness of the proposed model to handle P2P energy trading. The results also show that P2P energy trading provides significant financial and technical benefits to the community, and it is emerging as an alternative to cost-intensive energy storage systems.

A key aspect of multi-energy microgrids (MEMGs) is the capability to efficiently convert and store energy in order to reduce the costs and environmental impact. Peer-to-peer (P2P) energy trading is a novel paradigm for decentralised energy market designs. In this paper, we investigate the external P2P energy trading problem and internal energy conversion problem within interconnected residential, commercial and industrial MEMGs. These two problems are complex decision-making problems with enormous high-dimensional data and uncertainty, so a multi-agent deep reinforcement learning approach combining the multi-agent actor-critic algorithm with the twin delayed deep deterministic policy gradient algorithm is proposed. The proposed approach can handle the high-dimensional continuous action space and aligns with the nature of P2P energy trading with multiple MEMGs. Simulation results based on three real-world MG datasets show that the proposed approach significantly reduces each MG’s average hourly operation cost. The impact of carbon tax pricing is also considered.

This paper proposes bilateral contract networks as a new scalable market design for peer-to-peer energy trading. Coordinating small-scale distributed energy resources to shape overall demand could offer significant value to power systems, by alleviating the need for investments in upstream generation and transmission infrastructure, increasing network efficiency and increasing energy security. However, incentivising coordination between the owners of large-scale and small-scale energy resources at different levels of the power system remains an unsolved challenge. This paper introduces real-time and forward markets, consisting of energy contracts offered between generators with fuel-based sources, suppliers acting as intermediaries and consumers with inflexible loads, time-coupled flexible loads and/or renewable sources. For each type of agent, utility-maximising preferences for real-time contracts and forward contracts are derived. It is shown that these preferences satisfy full substitutability conditions essential for establishing the existence of a stable outcome—an agreed network of contracts specifying energy trades and prices, which agents do not wish to mutually deviate from. Important characteristics of energy trading are incorporated, including upstream–downstream energy balance and forward market uncertainty. Full substitutability ensures a distributed price-adjustment process can be used, which only requires local agent decisions and agent-to-agent communication between trading partners.

The power grid is rapidly transforming, and while recent grid innovations increased the utilization of advanced control methods, the next-generation grid demands technologies that enable the integration of distributed energy resources (DERs)—and consumers that both seamlessly buy and sell electricity. This paper develops an optimization model and blockchain-based architecture to manage the operation of crowdsourced energy systems (CESs), with peer-to-peer (P2P) energy trading transactions (ETTs). An operational model of CESs in distribution networks is presented considering various types of ETT and crowdsourcees. Then, a two-phase operation algorithm is presented: Phase I focuses on the day-ahead scheduling of generation and controllable DERs, whereas Phase II is developed for hour-ahead or real-time operation of distribution networks. The developed approach supports seamless P2P energy trading between individual prosumers and/or the utility. The presented operational model can also be used to operate islanded microgrids. The CES framework and the operation algorithm are then prototyped through an efficient blockchain implementation, namely, the IBM Hyperledger Fabric. This implementation allows the system operator to manage the network users to seamlessly trade energy. Case studies and prototype illustration are provided.

In recent years, the rapid growth of active consumers in the distribution networks transforms the modern power markets’ structure more independent, flexible, and distributed. Specifically, in the recent trend of peer-to-peer (P2P) transactive energy systems, the traditional consumers became prosumers (producer+consumer) who can maximize their energy utilization by sharing it with neighbors without any conventional arbitrator in the transactions. Although a distributed energy pricing scheme is inevitable in such systems to make optimal decisions, it is challenging to establish under the influence of non-linear physical network constraints with limited information. Therefore, this paper presents a distributed pricing strategy for P2P transactive energy systems considering voltage and line congestion management, which can be utilized in various power network topologies. This paper also introduces a new mutual reputation index as a product differentiation between the prosumers to consider their bilateral trading willingness. In this paper, a Fast Alternating Direction Method of Multipliers (F-ADMM) algorithm is realized instead of the standard ADMM algorithm to improve the convergence rate. The effectiveness of the proposed approach is validated through software simulations. The result shows that the algorithm is scalable, converges faster, facilitates easy implementation, and ensures maximum social welfare/profit.

This paper proposes two novel strategies for determining the bilateral trading preferences of households participating in a fully Peer-to-Peer (P2P) local energy market. The first strategy matches between surplus power supply and demand of participants, while the second is based on the distance between them in the network. The impact of bilateral trading preferences on the price and amount of energy traded is assessed for the two strategies. A decentralized fully P2P energy trading market is developed to generate the results in a day-ahead setting. After that, a permissioned blockchain-smart contract platform is used for the implementation of the decentralized P2P trading market on a digital platform. Actual data from a residential neighborhood in the Netherlands, with different varieties of distributed energy resources, is used for the simulations. Results show that in the two strategies, the energy procurement cost and grid interaction of all participants in P2P trading are reduced compared to a baseline scenario. The total amount of P2P energy traded is found to be higher when the trading preferences are based on distance, which could also be considered as a proxy for energy efficiency in the network by encouraging P2P trading among nearby households. However, the P2P trading prices in this strategy are found to be lower. Further, a comparison is made between two scenarios: with and without electric heating in households. Although the electrification of heating reduces the total amount of P2P energy trading, its impact on the trading prices is found to be limited.

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Peer-to-peer (P2P) energy trading has emerged as a next-generation energy-management mechanism for the smart grid that enables each prosumer (i.e., an energy consumer who also produces electricity) of the network to participate in energy trading with other prosumers and the grid. This poses a significant challenge in terms of modeling the decisionmaking process of the participants' conflicting interests and motivating prosumers to participate in energy trading and cooperate, if necessary, in achieving different energy-management goals. Therefore, such a decisionmaking process needs to be built on solid mathematical and signal processing principles that can ensure an efficient operation of the electric power grid. This article provides an overview of the use of game-theoretic approaches for P2P energy trading as a feasible and effective means of energy management. Various game- and auction-theoretic approaches are discussed by following a systematic classification to provide information on the importance of game theory for smart energy research. This article also focuses on the key features of P2P energy trading and gives an introduction to an existing P2P testbed. Furthermore, the article gives specific game- and auction-theoretic models that have recently been used in P2P energy trading and discusses important findings arising from these approaches.

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It is expected that peer to peer energy trading will constitute a significant share of research in upcoming generation power systems due to the rising demand of energy in smart microgrids. However, the on-demand use of energy is considered a big challenge to achieve the optimal cost for households. This paper proposes a blockchain-based predictive energy trading platform to provide real-time support, day-ahead controlling, and generation scheduling of distributed energy resources. The proposed blockchain-based platform consists of two modules; blockchain-based energy trading and smart contract enabled predictive analytics modules. The blockchain module allows peers with real-time energy consumption monitoring, easy energy trading control, reward model, and unchangeable energy trading transaction logs. The smart contract enabled predictive analytics module aims to build a prediction model based on historical energy consumption data to predict short-term energy consumption. This paper uses real energy consumption data acquired from the Jeju province energy department, the Republic of Korea. This study aims to achieve optimal power flow and energy crowdsourcing, supporting energy trading among the consumer and prosumer. Energy trading is based on day-ahead, real-time control, and scheduling of distributed energy resources to meet the smart grid’s load demand. Moreover, we use data mining techniques to perform time-series analysis to extract and analyze underlying patterns from the historical energy consumption data. The time-series analysis supports energy management to devise better future decisions to plan and manage energy resources effectively. To evaluate the proposed predictive model’s performance, we have used several statistical measures, such as mean square error and root mean square error on various machine learning models, namely recurrent neural networks and alike. Moreover, we also evaluate the blockchain platform’s effectiveness through hyperledger calliper in terms of latency, throughput, and resource utilization. Based on the experimental results, the proposed model is effectively used for energy crowdsourcing between the prosumer and consumer to attain service quality.

This paper proposes a peer-to-peer (P2P) energy trading scheme that can help a centralized power system to reduce the total electricity demand of its customers at the peak hour. To do so, a cooperative Stackelberg game is formulated, in which the centralized power system acts as the leader that needs to decide on a price at the peak demand period to incentivize prosumers to not seek any energy from it. The prosumers, on the other hand, act as followers and respond to the leader’s decision by forming suitable coalitions with neighboring prosumers in order to participate in P2P energy trading to meet their energy demand. The properties of the proposed Stackelberg game are studied. It is shown that the game has a unique and stable Stackelberg equilibrium, as a result of the stability of prosumers’ coalitions. At the equilibrium, the leader chooses its strategy using a derived closed-form expression, while the prosumers choose their equilibrium coalition structure. An algorithm is proposed that enables the centralized power system and the prosumers to reach the equilibrium solution. Numerical case studies demonstrate the beneficial properties of the proposed scheme.

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The proliferation of distributed energy resources (DERs) and the large‐scale electrification of transportation are driving forces behind the ongoing evolution for transforming traditionally passive consumers into prosumers (both consumers and producers) in coordinated power distribution network (PDN) and urban transportation network (UTN). In this new paradigm, peer‐to‐peer (P2P) energy trading is a promising energy management strategy for dynamically balancing the supply and demand in electricity markets. In this paper, we propose the application of Blockchain (BC) to electric vehicle charging station (EVCS) operations to optimally transact energy in a hierarchical P2P framework. In the proposed framework, a decentralised privacy‐preserving clearing mechanism is implemented in the transactive energy market (TEM) in which BC's smart contracts are applied in a coordinated PDN and UTN operation. The effectiveness of the proposed TEM and its solution approach are validated via numerical simulations which are performed on a modified IEEE 123‐bus PDN and a modified Sioux Falls UTN.

In recent years, virtual power plants (VPPs) have been undergoing a rapid development to aggregate mushrooming distributed energy resources. In this paper, a distributed robust algorithm for VPPs’ peer-to-peer (P2P) energy trading is proposed which can improve the robustness against communication failures such as packet losses and computing node failures in the cyber layer. Firstly, a P2P energy trading framework suitable for VPPs located in the unbalanced distribution network managed by the distribution system operator (DSO) is proposed, which is a bi-level problem with the DSO standing at the higher level to solve the power flow model and VPP operators (VPPOs) following at the lower level to decide the bilateral trading quantity and prices. Herein, an enhanced linearized DistFlow model is developed to address the unbalanced distribution network. Then, a distributed algorithm wherein the DSO and VPPOs engage in an iterative negotiation process based on relaxed alternating direction method of multipliers (R-ADMM) is derived, which renders the VPPOs’ parallel computation and preserves privacy. Furthermore, a robust version of the algorithm against communication failures (DRAACF) is developed. The economy, robustness, computational efficiency and scalability of the proposed approach are validated by simulation analyses based on the modified IEEE 13-bus and IEEE 123-bus distribution networks.

Due to the rapid development of demand response management and distributed energy resources, prosumers are becoming more proactive, which also promotes the emergence of peer-to-peer (P2P) energy trading mechanisms. However, we find that it is quite difficult to simultaneously achieve high computational efficiency, decentralized operations and solution optimality in a P2P energy trading mechanism, which is called the “P2P energy trading trilemma”. In this paper, we first propose a novel negotiation mechanism for P2P energy trading that can maximize social welfare in a decentralized manner and respect physical network constraints. Then, the local optimization problem is transformed into a closed-form alternating update algorithm (AUA), so that the computational efficiency can be greatly improved. Therefore, our proposed mechanism solves the “P2P energy trading trilemma” to a certain extent. Another challenge is that the locational marginal price (LMP)-based market mechanism cannot satisfy the incentive-compatible property. To encourage prosumers to cooperate in a lack-of-trust environment, a novel incentive-compatible mechanism is proposed for P2P energy trading using consensus mechanism inspired by proof of solution (PoSo) and smart contract (SC). Finally, we simulate the functionality of the mechanism in terms of convergence performance, reliability, scalability, computational efficiency, and SC operations.

Several recent studies have suggested Blockchain for Peer-to-Peer energy trading (P2P-ET) to achieve better security, privacy and fast payment settlement. Most of them however rely on either public Blockchains (which have low performance) or permissioned blockchains (which have low decentralization level and do not provide byzantine fault tolerance). Moreover, these solutions have limitations when capturing the business model of existing energy trading systems. This article proposes a Unified permissioned blockchain-based P2P-ET Architecture (UBETA) that integrates three different types of energy markets and provides a unified energy trading and payment settlement model. The UBETA system is based on an enterprise Ethereum Blockchain, known as Hyperledger Besu, and Istanbul Byzantine Fault Tolerance (IBFT) consensus algorithm. We compared the performance of the proposed IBFT-based system with three existing systems (i.e., Ethereum Clique, Ethereum Proof of Work and Hyperledger Fabric’s Raft) using specific performance metrics (i.e., read/write transaction latency, read/write transaction throughput and fail rate). The experiments were carried out on a network size of up to 60 nodes and a real energy trading data set from the Western Australian energy market was used. The experiment results indicate that the IBFT-based system has 15x lower latency and nearly 2x throughput compared to existing Proof of Work based P2P-ET solutions. Moreover, the system provides better scalability and success rate than existing Raft based P2P-ET systems: the fail rate of the IBFT-based system only increased by 11% while that of Raft increased by 20% when increasing the number of nodes from 20 to 60. In addition, the proposed unified energy trading model provides lower latency and reduces the number of blockchain transactions compared to the non-unified counterpart.

Peer-to-peer (P2P) energy trading is one of the promising approaches for implementing decentralized electricity market paradigms. In the P2P trading, each actor negotiates directly with a set of trading partners. Since the physical network or grid is used for energy transfer, power losses are inevitable, and grid-related costs always occur during the P2P trading. A proper market clearing mechanism is required for the P2P energy trading between different producers and consumers. This paper proposes a decentralized market clearing mechanism for the P2P energy trading considering the privacy of the agents, power losses as well as the utilization fees for using the third party owned network. Grid-related costs in the P2P energy trading are considered by calculating the network utilization fees using an electrical distance approach. The simulation results are presented to verify the effectiveness of the proposed decentralized approach for market clearing in P2P energy trading.

In a prosumer rich environment, most of the peers are likely to be equipped with distributed resources based facilities, this results in network sustainability enhancement if the fairness of energy trading be optimally managed through the network. Developing a decentralized energy pricing methodology while considering the proper arrangement of the network is inevitable. This paper proposes a new approach for conducting effective negotiations between peers in a novel peer-to-peer (P2P) architecture to contrive a fair energy trading mechanism by enhancing the authority of benefiting the grid for all peers through their transactions. The proposed methodology increases the market participants’ welfare as well as improves the system’s operational conditions and derives a dynamic network usage price (NUP) in contrast to conventional P2P and available works. Also, fairness in energy trading is developed here for P2P trading by addressing the derived network utilization welfare function in which those transactions that impose negative impacts on the network condition are effectively prevented to enhance the fairness of the grid without sacrificing accessibility to generation for demands. Requiring low computational burdens as well as low information exchange are other innovations that are achieved by implementing the proposed algorithms. The effectiveness and performance of the proposed approach are verified by means of extensive simulation studies and the obtained results show fast convergence, scalability, readily implemented, and guaranteeing maximum peer’s welfare as well as optimal network fairness of the proposed approach.

The concept of Prosumer has enabled consumers to actively participate in Peer-to-Peer (P2P) energy trading, particularly as Renewable Energy Source (RES)s and Electric Vehicle (EV)s have become more accessible and cost-effective. In addition to the P2P energy trading, prosumers benefit from the relatively high energy capacity of EVs through the integration of Vehicle-to-X (V2X) technologies, such as Vehicle-to-Home (V2H), Vehicle-to-Load (V2L), and Vehicle-to-Grid (V2G). Optimization of an Energy Management System (EMS) is required to allocate the required energy efficiently within the cluster, due to the complex pricing and energy exchange mechanism of P2P energy trading and multiple EVs with V2X technologies. In this paper, Deep Reinforcement Learning (DRL) based EMS optimization method is proposed to optimize the pricing and energy exchanging mechanisms of the P2P energy trading without affecting the comfort of prosumers. The proposed EMS is applied to a small-scale cluster-based environment, including multiple (6) prosumers, P2P energy trading with novel hybrid pricing and energy exchanging mechanisms, and V2X technologies (V2H, V2L, and V2G) to reduce the overall energy costs and increase the Self-Sufficiency Ratio (SSR)s. Multi Double Deep Q-Network (DDQN) agents based DRL algorithm is implemented and the environment is formulated as a Markov Decision Process (MDP) to optimize the decision-making process. Numerical results show that the proposed EMS reduces the overall energy costs by 19.18%, increases the SSRs by 9.39%, and achieves an overall 65.87% SSR. Additionally, numerical results indicates that model-free DRL, such as DDQN agent based Deep Q-Network (DQN) Reinforcement Learning (RL) algorithm, promise to eliminate the energy management complexities with multiple uncertainties.

A novel Peer-to-peer (P2P) energy trading scheme for a Virtual Power Plant (VPP) is proposed by using Smart Contracts on Ethereum Blockchain Platform. The P2P energy trading is the recent trend the power society is keen to adopt carrying out several trial projects as it eases to generate and share the renewable energy sources in a distributed manner inside local community. Blockchain and smart contracts are the up-and-coming phenomena in the scene of the information technology used to be considered as the cutting-edge research topics in power systems. Earlier works on P2P energy trading including and excluding blockchain technology were focused mainly on the optimization algorithm, Information and Communication Technology, and Internet of Things. Therefore, the financial aspects of P2P trading in a VPP framework is focused and in that regard a P2P energy trading mechanism and bidding platform are developed. The proposed scheme is based on public blockchain network and auction is operated by smart contract addressing both cost and security concerns. The smart contract implementation and execution in a VPP framework including bidding, withdrawal, and control modules developments are the salient feature of this work. The proposed architecture is validated using realistic data with the Ethereum Virtual Machine (EVM) environment of Ropsten Test Network.

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Abstract Peer-to-peer trading in energy networks is expected to be exclusively conducted by the prosumers of the network with negligible influence from the grid. This raises the critical question: how can enough prosumers be encouraged to participate in peer-to-peer trading so as to make its operation sustainable and beneficial to the overall electricity network? To this end, this paper proposes how a motivational psychology framework can be used effectively to design peer-to-peer energy trading to increase user participation. To do so, first, the state-of-the-art of peer-to-peer energy trading literature is discussed by following a systematic classification, and gaps in existing studies are identified. Second, a motivation psychology framework is introduced, which consists of a number of motivational models that a prosumer needs to satisfy before being convinced to participate in energy trading. Third, a game-theoretic peer-to-peer energy trading scheme is developed, its relevant properties are studied, and it is shown that the coalition among different prosumers is a stable coalition. Fourth, through numerical case studies, it is shown that the proposed model can reduce carbon emissions by 18.38% and 9.82% in a single day in Summer and Winter respectively compared to a feed-in-tariff scheme. The proposed scheme is also shown to reduce the cost of energy up to 118 ¢ and 87 ¢ per day in Summer and Winter respectively. Finally, how the outcomes of the scheme satisfy all the motivational psychology models is discussed, which subsequently shows its potential to attract users to participate in energy trading.

Peer-to-peer (P2P) energy trading among multi-microgrids has emerged as a promising paradigm to facilitate more efficient supply-demand balancing within local areas. However, existing works still exhibit limitations in terms of trading architecture and pricing schemes. In addition, the existing multi-agent deep reinforcement learning (MADRL) methods suffer from computational overload caused by the exploration of joint and hybrid action space during centralized training. In this paper, we propose a P2P energy trading paradigm based on hierarchical hybrid MADRL to maximize the trading profits among multiple heterogeneous MGs. First, we design a novel hierarchical structure of the MC agent to model the coupled interaction between flexible demands scheduling and autonomous quotation. Then, a P2P market that employs an improved mid-market rate (IMMR) pricing scheme is proposed to incentivize participation in local trading. Furthermore, to handle hybrid discrete-continuous action space and reduce computational complexity, we propose a hierarchical hybrid multi-agent double deep Q-network and deep deterministic policy gradient (hh-MADDQN-DDPG) algorithm to split the optimal policy learning-workload into a sequence of two sub-tasks. The DDQN for flexible demands scheduling and DDPG for energy trading. Numerical results of simulation I demonstrate that our hh-MADDQN-DDPG with IMMR increases 25% of the trading profits averaged over the baselines. Results of simulation II show that our hh-MADDQN-DDPG provides higher profits compared with the existing methods while maintaining better computational performance and scalability.

This paper presents a resilience-driven framework leveraging advanced control technologies, particularly a Markov chain approach, to enhance the robustness of peer-to-peer (P2P) energy trading networks under Low Probability High Impact Events (LPHIE) such as climate change, cyberattacks, and natural disasters. The proposed framework addresses critical challenges in maintaining energy system stability by integrating renewable energy sources, optimizing energy exchange, and quantifying system resilience through novel metrics. A key contribution lies in the development of a multi-layered strategy that incorporates fuzzy logic for dynamic fault mitigation and a three-tier energy management system combining solar PV, battery storage, and electric vehicles. Additionally, the framework utilizes blockchain technology to ensure transparency, security, and fairness in energy trading, while promoting inclusivity and equity among diverse prosumer groups. By advancing the state-of-the-art in resilience modeling, this study provides a robust foundation for sustainable energy management in decentralized systems, offering practical insights into mitigating the impacts of LPHIE and advancing the democratization of energy resources.

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In a smart grid, each residential unit with renewable energy sources can trade energy with others for profit. Buyers with insufficient energy meet their demand by buying the required energy from other houses with surplus energy. However, they will not be willing to engage in the trade if it is not beneficial. With the aim of improving participants’ profits and reducing the impacts on the grid, we study a peer-to-peer (P2P) energy trading system among prosumers using a double auction-based game theoretic approach, where the buyer adjusts the amount of energy to buy according to varying electricity price in order to maximize benefit, the auctioneer controls the game, and the seller does not participate in the game but finally achieves the maximum social welfare. The proposed method not only benefits the participants but also hides their information, such as their bids and asks, for privacy. We further study individual rationality and incentive compatibility properties in the proposed method’s auction process at the game’s unique Stackelberg equilibrium. For practical applicability, we implement our proposed energy trading system using blockchain technology to show the feasibility of real-time P2P trading. Finally, simulation results under different scenarios demonstrate the effectiveness of the proposed method.

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The decentralization of energy generation and consumption has become increasingly vital, with peer-to-peer (P2P) energy trading promoting resilience, and sustainability. This paper introduces INTELTRADE, a system addressing the energy sustainability and pricing challenges in P2P trading. INTELTRADE integrates a sophisticated coalition formation mechanism and an optimal pricing policy. Initially, the Approximate INTELTRADE (AINTELTRADE) mechanism uses matching theory to quickly determine the initial buyer-seller matches by ignoring prosumers’ externalities. This output feeds the Accurate INTELTRADE (AccINTELTRADE) mechanism, which refines the coalition formation using game theory while considering the prosumers’ externalities. Additionally, a non-cooperative game among energy-selling prosumers is formulated, deriving a unique Nash Equilibrium to maximize the sellers’ profits. INTELTRADE’s efficacy and scalability are validated through simulations with real-world data. A detailed comparative evaluation demonstrates INTELTRADE’s superiority in meeting the buyers’ energy demands and optimizing the sellers’ profits.

Peer-to-Peer (P2P) energy sharing enables prosumers within a community microgrid to directly trade their local energy resources such as solar photovoltaic (PV) panels, small-scale wind turbines, electric vehicle battery storage among each other based on an agreed cost-sharing mechanism. This paper addresses the energy cost minimization problem associated with P2P energy sharing among smart homes which are connected in a residential community. The contribution of this paper is threefold. First, an effective Home Energy Management System (HEMS) is proposed for the smart homes equipped with local generation such as rooftop solar panels, storage and appliances to achieve the demand response (DR) objective. Second, this paper proposes a P2P pricing mechanism based on the dynamic supply-demand ratio and export-import retail prices ratio. This P2P model motivates individual customers to participate in energy trading and ensures that not a single household would be worse off. Finally, the performance of the proposed pricing mechanism, is compared with three popular P2P sharing models in the literature namely the Supply and Demand Ratio (SDR), Mid-Market Rate (MMR) and bill sharing (BS) considering different types of peers equipped with solar panels, electric vehicle, and domestic energy storage system. The proposed P2P framework has been applied to a community consisting of 100 households and the simulation results demonstrate fairness and substantial energy cost saving/revenue among peers. The P2P model has also been assessed under the physical constrains of the distribution network.

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This paper introduces a dynamic tariff algorithm designed for islanded community microgrids in Sri Lanka, aiming to optimize energy distribution through real-time pricing and demand response integration. The algorithm accounts for solar generation, battery storage, and supply-demand mismatches to calculate a fair market price that reflects operational realities. By adjusting prices based on a demand factor, the system incentivizes consumers to shift or reduce consumption during peak periods, thereby enhancing grid stability and reducing dependency on diesel generators. The proposed model supports a more sustainable, responsive, and economically efficient energy market tailored for decentralized microgrid environments.

Peer-to-peer (P2P) electricity trading market can effectively promote distributed energy resources (DER) electricity consumption in blockchain environment. Promoting the consumption of DER electricity should consider not only the supply side, but also the demand side. For the demand side, different users have different preference, which should be considered in the trading process. In this study, we propose a P2P electricity trading method of community microgrid considering user preference differences in blockchain environment. Firstly, the model structure and trading rules are introduced. Then the P2P trading process is described in detail. Finally, the proposed model is simulated on the Hyperledger Fabric platform. Experimental results show that the proposed method can effectively promote the consumption of DER electricity, reduce the electricity cost of users, and meet user preference.

Decentralized energy systems can be an alternative to stabilizing the power system in a rapidly changing power market environment. In this regard, it is very important to level the significant gap between electricity loads and power generation, which is caused by expanding renewable energy resources. This study investigates an electricity control strategy to encourage forming a microgrid and to level the load profile that the microgrid optimizes based on its individual objective. To address the problems encountered by two players at different decision levels, this study introduces a bilevel optimization model that considers two players’ objectives. In the proposed model, the first player is called the grid system operator, and the decisions of the player are subsidy rates for distributed generators and an energy storage system. The second player is called the community microgrid, and the major decisions of the player are the configuration and operation of the microgrid. To solve the problem, an efficient algorithm is developed based on Karush–Kuhn–Tucker (KKT) conditions and a decomposition approach. Numerical experiments show that the peak load can be reduced by setting an adequate subsidy rate.

This work fits in the context of community microgrids, where members of a community can exchange energy and services among themselves, without going through the usual channels of the public electricity grid. We introduce and analyze a framework to operate a community microgrid, and to share the resulting revenues and costs among its members. A market-oriented pricing of energy exchanges within the community is obtained by implementing an internal local market based on the marginal pricing scheme. The market aims at maximizing the social welfare of the community, thanks to the more efficient allocation of resources, the reduction of the peak power to be paid, and the increased amount of reserve, achieved at an aggregate level. A community microgrid operator, acting as a benevolent planner, redistributes revenues and costs among the members, in such a way that the solution achieved by each member within the community is not worse than the solution it would achieve by acting individually. In this way, each member is incentivized to participate in the community on a voluntary basis. The overall framework is formulated in the form of a bilevel model, where the lower level problem clears the market, while the upper level problem plays the role of the community microgrid operator. Numerical results obtained on a real test case implemented in Belgium show around 54% cost savings on a yearly scale for the community, as compared to the case when its members act individually.

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This paper proposes a new framework for the optimal operation of a microgrid aggregator (MGA) that participates in an oligopoly electricity market. This aggregator obtains an optimal bidding (power and price) strategy for a multigrid (MG) system, i.e., a community MG. Consequently, the granted quantity, i.e., power in the electricity market, is deployed to optimally schedule the MG’s resources to meet demand. As such, as per three-level optimization, the independent system operator (ISO) clears the market with the goal of maximizing the social welfare in the first stage and determining the hourly market price as well as players’ credited power. In the second-level optimization process, the players select the optimal coefficient supply function equilibrium according to the power granted from the market. In third-level optimization, an optimal scheduling for MGs’ resources and demand would be obtained according to the won power in the market to maximize the aggregator profit. In addition, a price-taker MGA is simulated for comparison with the price–maker MGA to highlight the advantage of the proposed technique. Furthermore, a bidding strategy based on game theory is proposed to obtain the optimal price and power of the oligopoly market players and maximize all players’ profits. Finally, a test system including three generators is created to evaluate the performance of the devised bidding strategy. The results show that the proposed bidding strategy can optimally calculate the focal point of the Nash equilibrium (NE) in the oligopoly electricity market.

From a long-term investment point of view, community microgrid planning and operation will need to ensure resource allocation efficiency, facilitated by appropriate revenue flows, to achieve optimum outcomes for all stakeholders including the community, investors and the providers. Drawing insights and experience from traditional electricity sector planning and infrastructure planning in general, this paper discusses a number of strategic options in microgrid market design, including monopolistic model, customer cooperation model and prosumer competition model, and assesses their potential suitability to deliver such outcomes. This provides a conceptual framework for the evaluation of the available strategic options and the identification of challenges in community microgrid investment planning, that can guide communities, public and private investors looking into achieving more efficient and more reliable electricity supply through community microgrids or clean energy.

A growing number of prosumers have entered the local power market in response to an increase in the number of residential users who can afford to install distributed energy resources. The traditional microgrid trading platform has many problems, such as low transaction efficiency, the high cost of market maintenance, opaque transactions, and the difficulty of ensuring user privacy, which are not conducive to encouraging users to participate in local electricity trading. A blockchain-based mechanism of microgrid transactions can solve these problems, but the common single-blockchain framework cannot manage user identity. This study thus proposes a mechanism for secure microgrid transactions based on the hybrid blockchain. A hybrid framework consisting of private blockchain and consortium blockchain is first proposed to complete market transactions. The private blockchain stores the identifying information of users and a review of their transactions, while the consortium blockchain is responsible for storing transaction information. The block digest of the private blockchain is stored in the consortium blockchain to prevent information on the private blockchain from being tampered with by the central node. A reputation evaluation algorithm based on user behavior is then developed to evaluate user reputation, which affects the results of the access audit on the private blockchain. The higher a user’s reputation score is, the more benefits he/she can obtain in the transaction process. Finally, an identity-based proxy signcryption algorithm is proposed to help the intelligent management device with limited computing power obtain signcryption information in the transaction process to protect the transaction information. A system analysis showed that the secure transaction mechanism of the microgrid based on the hybrid blockchain boasts many security features, such as privacy, transparency, and imtamperability. The proposed reputation evaluation algorithm can objectively reflect all users’ behaviors through their reputation scores, and the identity-based proxy signcryption algorithm is practical.

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This article presents the optimal operation of a community of “non-positive aggregate energy importing” DC microgrids in a local energy-sharing environment. DC microgrid community aims to minimize its social cost by participating in a day-ahead and peer-to-peer energy market, and local hydrogen market. Based on the price signals from the grid, the DC microgrids determine their power exchange with the grid and schedule its dispatchable sources (fuel cell and microturbine), renewable power generation (solar and wind), energy storage (battery energy storage system and hydrogen storage system), and flexible loads (electrolyzer, thermostatically-controlled load, and plug-in electric vehicle) in a privacy-preserving decentralized manner. Further, each DC microgrid has bidding price flexibility when participating in peer-to-peer transactions. Simulation studies are carried out on a community of three six-bus DC microgrid systems. Fixed-price peer-to-peer trading increases social welfare by $\sim 74.23\%$ compared to no peer-to-peer trading. Bidding price flexibility adds $\sim 1.83\%$ to social welfare, reduces renewable curtailment by $\sim 2.58\%$, raises cumulative peer-to-peer transactions by $\sim 13.42\%$, and lowers the average peer-to-peer price by $\sim 30.47\%$. Increasing the DC microgrid's hydrogen storage system size further improves social welfare. Transitioning from no peer-to-peer trading to fixed and then flexible bidding price peer-to-peer trading increases DC microgrid energy diversion from the grid towards the DC microgrid cluster's self-sufficiency.

As the penetration rate of distributed energy in the distribution network continues to increase, new power trading models in the microgrid environment have become an important way to improve the efficiency of new energy consumption. This study, targeting community microgrids with distributed generation and energy storage, has constructed a market-oriented operation framework based on peer-to-peer ($\mathbf{P 2 P}$) trading. This model adopts a continuous two-way auction mechanism, allowing microgrid users to achieve two-way trading of electricity through bidding. In terms of the participation of the energy storage system, the model uses a predictive control method to dynamically optimize the charging and discharging strategies, providing scientific bidding decision-making basis for market entities. At the same time, an improved zero-information bidding algorithm with adaptive learning ability is introduced to effectively enhance the autonomous decision-making ability of each participant. Simulation results based on actual microgrid cases show that the proposed distributed trading mechanism can significantly improve the comprehensive economic benefits of community users and provide strong support for the large-scale development of new energy.

The concept of "energy prosumer" is a relatively new phenomenon resulting from distributed energy production through photovoltaic (PV) technology, which has blurred the line between energy producers and consumers. Blockchain technology has facilitated secure and cost-effective energy transactions among consumers, prosumers, and utilities, automating the process. This study aims to develop an agent-based modeling (ABM) simulation framework for energy exchange, demonstrating the power profiles of households and the operation of blockchain operations. The simulation was conducted in the Education City Community Housing (ECCH) microgrid, using a multi-agent framework for a transactive energy (TE) distributed energy resource (DER) that requires blockchain technology. The current blockchain-based local energy market (LEM) aims to balance supply and demand using precise short-term energy generation forecasts and home consumption estimates. This study evaluated the accuracy of state-of-the-art energy forecasting methods in predicting household energy generation and consumption. It examined the impact of forecasting errors on market outcomes under different supply scenarios. Although LSTM models may provide low forecasting errors, the researchers found that the prediction process needs modification for a LEM built on a blockchain. This study stands out from previous research by forecasting the timeline of smart meters in general.

This article proposes a novel platform for microgrid (MG) prosumers that can actively trade energy with the power grid and each other directly. This platform provides Peer-to-Peer (P2P) energy trading among MG prosumers to achieve a win–win outcome in the presence of emerging energy resources, such as electric vehicles (EVs), energy storage systems (ESSs), and demand response programs (DRPs). P2P energy trading, from the power system perspective, can facilitate energy balance locally and self-sufficiency. Despite the conventional noncooperative game that players act individually, prosumers can cooperate in exchanging energy and reaping economic benefits in the proposed model. To this end, a bargaining cooperative game is adopted due to MGs autonomy and self-interest. Besides, to avoid prosumers’ private data launching, the fast-alternating direction method of multipliers is introduced to solve MGs' energy management problem in a decentralized manner. A robust optimization method is also applied to manage electricity market uncertainty, allowing MGs operators to decide how much risk they want to consider by adjusting the uncertainty budget parameter. The numerical results show that MGs can actively trade with each other and achieve economic benefits. Moreover, many of the utilized technologies such as ESS, DRP, and EVs assist MGs in sharing more energy with each other.

The current electricity market is better described as an oligopoly than a market of perfect competition from which, in fact, it may be rather far. The increasing penetration of residential distributed energy resources has led to a significant number of prosumers in the electricity market. Microgrid community, a group of single controllable entity prosumers, is a promising component of the smart grid which will potentially yield a free electricity market. In this paper, we present a novel formation for a residential community microgrid that includes a coalition of prosumer households with solar photovoltaic systems. These households are connected through a virtual power bank that consists of households’ storage batteries and that mediates the communications between the households and the main grid. Using an application of mean field game theory, we find Nash Equilibrium strategies under which such sharing could minimize a linear combination of the households’ energy generation cost, energy consumption cost and revenue of sold energy. The resulting approach is tested on a case study of a constructed community micro-grid in Montreal, Quebec, Canada. The proposed mean field game approach can help decrease the aggregated cost and the individual energy cost. A comparative analytical study on the benefit of sharing was also performed, demonstrating that each prosumer is expected to have at least a 40 percent reduction on their individual cost if belonging to a microgrid community of 100 prosumers located in Montreal city.

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Blockchain technology, smart contract and microgrid systems have facilitated innovations and breakthroughs in the electricity industry. The once bundled electricity market dominated by a few key players is now gradually becoming unbundled to a more consumer-centric market due to these new technologies. To facilitate the use of community microgrids, this study develops a new trend in peer-to-peer energy trading. In this work, a model for a smart microgrid system, a decentralized energy trading platform based on blockchain, and smart contract technologies is proposed, considering an islanded community microgrid network of energy prosumers. Smart meters were used to ensure the bi-directional flow of data and power, thereby giving prosumers control over their power usage. Storage and validation of participants’ data are stored on the blockchain network, which has a strong feature of decentralization, transparency, security and data immutability. The smart contract automatically executes power delivery and transfer of tokens from the buyer’s wallet to the seller’s energy wallet based on the transaction logic and protocol. A web-user interface was designed to enable the ease of transactions by market participants and the web-user interface was designed on React.Javascript while the smart contract codes were done on the solidity programming language. Algorithms were also developed for the market trade operations in real time and sets of mathematical equations were formulated for energy pricing based on the supply and demand philosophies to curtail over-pricing and underpricing of energy. 

Microgrid planning philosophies are changing from islanding in the case of abnormal conditions to independent sustainability for constant secure, reliable operation. Large independent operators foresee a shift from gigantic-grid bulk generation and transmission to distributed generation (DG) from smaller, interlinked grid clusters. Customers and the community will benefit, but system operators and utilities will face challenges. Introduced to enhance electricity exchange and the energy market structure in such grids, transactive energy networks favor customers and DG owners, establish a utility business model, and enable power system innovation. Planners are also increasingly interested in blockchains for secure transactions. Blockchain-based transactive energy markets promise flexibility, transparency, security, competition, and superlative low-cost reliability, offering ideal energy-trading solutions in isolated microgrids and distribution-level markets. This article presents the development and case-study validation of a comprehensive transactive energy market framework with linked blockchain and power system layers, a novel market structure based on an end-user marginal price, and an adapted blockchain that fits intrinsic power system requirements. A new slot-ahead electricity market model is established through integration with the modified blockchain. With blockchain operation, monetary funds are managed equitably so that wallet billing rates for customers, utilities, and DG owners match broadcast smart-meter data.

By combining distributed generation, battery storage, and smart meters, microgrid performance can be enhanced. As a result, distributed energy resources (DER) can be intelligently controlled via online platforms. Through integration, prosumers that use decentralized resources like wind power have replaced conventional power customers. Due to the growth and digitalization of the power distribution infrastructure, a new method of exchanging electricity in community microgrids called peer-to-peer (P2P) intratrading has emerged. Blockchain technology is used because it is transparent, secure, and completes transactions quickly. Prosumers, consumers, and owners of renewable energy sources may now trade energy more easily amongst one another. This study develops the P2P paradigm to create a self-sufficient community microgrid system for trading energy. Incorporating peer-to-peer energy trades and a battery backup system, the suggested technique uses blockchain to simulate a decentralized microgrid energy market. The microgrid P2P market's clearing price is established by taking into account customers' expected reactions to price changes, incentivizing consumers and prosumers to alter their patterns of energy usage. A cryptocurrency called Cosmos that is mined and distributed using blockchain technology is also included in the P2P market idea. The results show how automated P2P commerce and adjustable energy storage enable end users to save energy and become more independent. This paper provides a sustainable microgrid energy market model and suggests an approach to improve security by incorporating blockchain technology into current energy management systems. In summary, the paper offers a design paradigm for an independent microgrid system that makes use of distributed energy sources. It demonstrates how end users may gain from energy efficiency and independence while highlighting the possibilities of P2P energy trade and storage variety offered by blockchain. This thorough method provides information on creating a sustainable microgrid energy market and illustrates how blockchain technology can be used to increase the safety of energy management systems.

This article proposes a system architecture for an energy community (EC) in a smart city for the optimal management of interconnected energy hubs (EHs) supervised by an Energy Community Manager (ECM). Considered EHs include building EH, microgrid EH, and electric vehicle EH. A dedicated peer-to-peer market is assumed to be present in the EC that allows the energy exchange among these EHs that are interconnected through an electricity network (E-N) and a district heating network (DH-N). The overall decision problem has been formalized as a constrained optimization problem, and a new distributed optimization algorithm, Nesterov-Proximal Atomic Coordination (NST-PAC), is proposed ensuring the privacy of information exchange, with Nesterov’s acceleration-based iterations that lead to a fast solution. The overall approach is evaluated using two case studies characterized by an IEEE 13-bus system as the E-N and nine nodes as the DH-N and by an IEEE 123-bus system as the E-N and 30 nodes as the DH-N. Different comparisons have been performed to show the advantages of the system architecture compared to the state of the art in practice and other recent approaches suggested in the literature in terms of savings, convergence speed, and scalability.

This paper proposes a peer to peer (P2P), blockchain based energy trading market platform for residential communities with the objective of reducing overall community peak demand and household electricity bills. Smart homes within the community place energy bids for its available distributed energy resources (DERs) for each discrete trading period during a day, and a double auction mechanism is used to clear the market and compute the market clearing price (MCP). The marketplace is implemented on a permissioned blockchain infrastructure, where bids are stored to the immutable ledger and smart contracts are used to implement the MCP calculation and award service contracts to all winning bids. Utilizing the blockchain obviates the need for a trusted, centralized auctioneer, and eliminates vulnerability to a single point of failure. Simulation results show that the platform enables a community peak demand reduction of 46%, as well as a weekly savings of 6%. The platform is also tested at a real-world Canadian microgrid using the Hyperledger Fabric blockchain framework, to show the end to end connectivity of smart home DERs to the platform.

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This paper presents a conceptual marketplace that would enable electricity transaction among and within microgrids serving residential communities. First, some insights into the unique characteristics that define such a market are provided. Then, a validation of the market and business models for electricity transaction is presented. Finally, a framework for trustworthy market participation by the stakeholders (i.e. buyers and sellers) is presented by introducing the concept of reputation score. The research focuses on establishing design characteristics of a realistic market structure and provides an approach for future work on microgrid electricity market modeling.

Blockchain is an emerging technology that due to its unique features, such as decentralization, elimination of intermediaries, immutability and increased security, accuracy and transparency has been highly regarded in various industries, including the smart grid. Creating an electricity market for energy exchanges between producers and consumers in a microgrid is important because of the increasing tendency to use renewable energy such as solar cells. This paper presents two pricing mechanisms based on the Mid-Market Rate and the auction to find the optimal price of energy exchanges, which increases the profit from sales for producers and reduces the cost of purchase for consumers. This paper also proposes three smart contracts for peer-to-peer energy trading which are responsible for executing energy exchanges and enabling the recording of transaction information on the Ethereum network in an encrypted manner with great precision and transparency.

Within the global transition to sustainable energy systems, renewable energy technologies, particularly wind and photovoltaic generation, have emerged as critical components for achieving carbon peaking and neutrality targets. Nevertheless, the inherent stochastic variability of renewable energy generation combined with bidirectional power fluctuation patterns induced by time-varying load profiles introduces critical operational barriers to maintaining microgrid security and stability. To address this issue, this study designs a novel electricity market-driven bilevel optimization scheduling framework for microgrid clusters. At the upper layer, a virtual demand response (VDR) mechanism enabling day-ahead economic dispatch is proposed for each microgrid. At the lower layer, an electricity market-driven coordination scheme is established, incorporating a Shapley value-based cooperative bidding mechanism to optimize energy storage utilization for real-time power compensation. Finally, numerical simulations demonstrate that the cluster imbalance metric under the proposed framework can be reduced to near-zero levels, significantly decreasing the interaction volume between microgrids and distribution grids.

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In this paper, we consider an electricity market with a batch of energy prosumers (EPs), who can trade energy with the day-ahead market (DAM) and energy balancing market (EBM). To coordinate the behavior of EPs, an energy aggregator (EA) is established, who is responsible for making aggregation strategies for the EPs to improve the benefit of the whole EP community. Specifically, two pricing schemes are provided by the EA: pay-as-you-go (PAYG) scheme and lump sum (LS) scheme, which can be flexibly selected by EPs based on their own preferences. By considering the dual-pricing principle of the EBM, a stochastic Stackelberg game between the EPs and EA is formulated and a two-level optimization algorithm is introduced based on the proposed feasible region partitioning (FRP) method. The performance of the algorithm is demonstrated with a two-settlement market model in the simulation.Note to Practitioners—This work aims to optimize the social cost of an EP community in a two-settlement electricity market. In contrast to the existing works, the main innovations and the resulting challenges lie in both the market modelling and the theoretical approach. In particular, different from the existing uniform-pricing schemes, this work explores the realization of mixed-pricing scheme, which can introduce more customized factors into the decision-making process of EPs. In addition, to adapt to a wider range of practical EBMs, the dual-pricing principle of the balancing energy is considered, which leads to a stochastic Stackelberg game where the social cost function is nondeterministic with respect to the strategy of EA. To address the aforementioned challenges, a two-level optimization algorithm is introduced based on the proposed FRP method, which enriches the existing methods for Stackelberg games. The proposed market model can be more capable of addressing some practical issues in the sense that the mixed-pricing scheme can adapt to the different preferences of market participants and the self-centric instinct of EPs is considered based on Nash games. In addition, the dual-pricing principle of the EBM is motivated by many real electricity markets and can be more general than the single-pricing counterpart.

An auction-based single-sided bidding energy transaction mechanism in a grid-connected microgrid (MG) using a multi-agent system allows for better profit sharing for all stakeholders. This can replace existing bilateral contractual trade between the stakeholders. In the bilateral contractual energy trade model, energy transactions are either long-term, medium-term, or short-term agreements or bilateral negotiations between the stakeholders based on physical limits. Whereas in an auction-based mechanism, the energy transaction is in real time based on bidding strategies and supply-demand mismatches among the stakeholders. This work proposes a single-sided auction mechanism (SSAM) to clear the market based on the asking price of the seller and the supply-demand mismatch of the microgrid. In addition, the new two bidding algorithms, namely the linear bidding algorithm (LBA) and the fuzzy logic-based bidding algorithm (FLBA), are developed for sellers to select the ‘ask’ quotes. The proposed auction-based, single-sided bidding energy transaction mechanism is tested and validated in the existing Malnad College of Engineering (MCE) grid-tied MG (bilateral contractual) trading model, Hassan-573201, Karnataka, India. The energy market simulation results yield promising findings, highlighting the advancement of proposed SSAM and bidding strategies in boosting the profit margin of sellers.

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This paper centers around enhancement of electricity trading volume within a decentralized, three-phase local electricity market (LEM) encompassing both single-phase and three-phase loads. The extent of trading volume is constrained by limitations inherent in line flow, voltage magnitude, and voltage unbalance parameters. To address these limitations, a Flexible Step Voltage Regulator (FSVR) is employed, and its parameters are automatically adjusted to enhance said limits. An optimization problem is formulated to get the parameters of FSVR to ensure network constraints are always satisfied. Through a series of simulation studies conducted on the IEEE 13-bus network, the utility of FSVR in improving the trading volume is quantified and presented.

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Local electricity markets (LEM) have recently attracted great interest as an effective solution to the challenging problem of distributed energy resources’ (DER) management. However, LEM designs combining the market functions of local energy trading and flexibility services (FS) provision to wider system operators have not attracted sufficient attention. In the context of addressing this research gap, this paper firstly provides a new model-based system-centric formulation for the coordination of such a LEM, which provides a theoretical optimality benchmark. Compared to previous formulations, it considers the time-coupling operating characteristics of flexible DERs, and optimizes the two market functions simultaneously. Furthermore, this paper explores for the very first time a model-free prosumer-centric coordination approach for such a LEM, in order to address the practical limitations of model-based system-centric approaches. This is achieved through a new multi-agent deep reinforcement learning method which combines the beneficial properties of the multi-actor-attention-critic and the prioritized experience replay approaches. Case studies on a real-world, large-scale setting validate that the proposed LEM design successfully encapsulates the economic benefits of both local energy trading and FS provision functions, and demonstrate that the proposed learning method outperforms previous methods.

In this work, a new approach is proposed for designing electricity trading price functions in local market (LM) inside a microgrid. Unlike traditional pricing models that only consider the electricity profile, the proposed model not only takes into account the profile of electricity in LM but also acquaint with a preference parameter of the price deciding entity in pricing schemes for designing the two dimensional (2-D) trading price model. The mechanism for managing electricity sharing in LM is introduced in this work and the prosumers with solar photovoltaic installations participate in this LM. At a particular time slot, a prosumer’s status as a buyer or a seller of electricity is determined by its electricity profile. A local market aggregator (LMA) that owns the microgrid, acts as the price deciding entity in LM. The profit model of LMA and the utility model of prosumers are defined. Three different situations in LM have been formulated for the design of trading price functions. The equilibrium solution for each situation is obtained using Cournot model. To design the pricing models in Situation 1, it is considered that both buyers and sellers participate in LM. However, players are limited to only sellers and buyers in Situation 2 and 3, respectively. A comparative study is conducted to evaluate the advantages of the proposed 2-D price models in terms of improving the profit margin of the LMA.

The present work is an attempt to propose a design methodology for the electricity sharing management (ESM) in local market (LM) inside a microgrid. Prosumers with solar photovoltaic (PV) installations participate in LM in which the central entity is a local market operator (LMO). Each PV prosumer in LM plays the role of either a buyer or a seller of electricity at a particular time slot depending upon the circumstances and own electricity profile (EP). The ESM is introduced in this work with the consideration that LMO owns a solar PV plant. Unlike the EP based traditional pricing schemes, present work proposes the design for 2-dimensional (2-D) electricity trading price function (ETPF) in LM. The ETPF, in present work, not only considers the EP of LMO at a particular time slot but also acquaints with a preference parameter in pricing schemes to improve profit margin of LMO. Two different situations are taken care of in this work. The ETPF in Situation 1 and 2 are designed with the assumption that players in LM are limited to the seller role prosumers and the buyer role prosumers, respectively. Equilibrium solutions for the aforesaid situations in LM have been derived using Cournot model. A comparative study has been carried out to accentuate the benefits achieved by LMO in terms of profit improvement using the designed 2-D ETPF over its 1-D counterpart. A comparative study on the cost benefit analysis with the designed ETPF in aforesaid situations has also been presented in this work.

European Union legislation stimulates more participation of prosumers in future local electricity markets (LEMs). These LEMs could require bidding for or by individuals, which brings the responsibility of deviating from agreed-upon bids closer to prosumers. Who should bear this responsibility is still being determined because the costs for energy suppliers could become too significant, and it is unrealistic to expect prosumers to plan all their devices. Non-routine utilization, using devices sporadically and unpredictably, represents freedom of device usage but could significantly contribute to deviations. Understanding the impact of this utilization on deviations will help to determine the responsibilities of deviations in LEMs better. Therefore, this study aims to take a first step and see the impact of non-routine utilization on LEM trading deviations by simulating this utilization for various appliances and DERs in combination with an LEM. Non-routine utilization of wet appliances contributes the most to deviations (15072 kWh or 4.99% or yearly consumption). However, it is comparable to distribution grid losses, and strategic use of home energy management systems can alleviate deviations caused by non-routine utilization by up to 100%.

The increasing need for grid stability and efficiency has led to a growing interest in integrating small-scale household flexibility into local electricity markets. In Switzerland, flexibility trading mechanisms primarily involve Distribution System Operators (DSOs), Balance Responsible Parties (BRPs), and Flexibility Service Providers (FSPs). While residential flexibility is often overlooked, its aggregated potential can substantially enhance market efficiency and support grid operations. This study explores the integration of household flexibility through an aggregator and its impact on local flexibility markets. Using real-world transformer-level scenarios, we analyse bid structures while considering load profiles, headroom constraints, and buffer zones. The findings illustrate how aggregated residential flexibility can be effectively structured within market frameworks to optimize power flows, mitigate peak loads, and provide additional value to grid operators and market participants. Furthermore, we demonstrate how a bidding platform for electricity markets can be systematically tested with realistic bidding structures that reflect the interactions between key stakeholders, including BRPs, DSOs, Transmission System Operators (TSOs), and FSPs. Additionally, we assess the extent to which power transformers approach their operational limits and explore strategies to alleviate constraints caused by peak loads. Finally, we examine the potential of existing flexibility in feed-in peaks and how it can be leveraged by market participants to enhance grid stability and overall efficiency.

As the global energy landscape undergoes a paradigm shift towards decentralization and sustainability, local electricity markets have emerged as a promising avenue for efficient energy exchange among prosumers. This conference paper presents a comprehensive study focused on optimizing system costs within local electricity markets through peer-to-peer energy transactions. Our primary objective is to investigate the impact of photovoltaic and battery storage adoption on system costs, shedding light on the potential economic benefits of these technologies in diverse scenarios. The study encompasses a rigorous analysis of various cases and scenarios to provide a holistic understanding of the dynamics involved in local electricity markets. By simulating a range of scenarios, we explore the implications of different levels of PV and battery storage adoption, evaluating their capacity to mitigate system costs effectively. Our findings reveal that, in the best-case scenario, substantial cost savings of 24.42% can be achieved through the integration of PV and battery storage systems into the local electricity market. This research contributes to the growing body of knowledge on local electricity markets, emphasizing the pivotal role of peer-to-peer energy transactions in shaping the future of energy distribution. Furthermore, it provides valuable insights for policymakers, utilities, and consumers seeking to optimize their participation in decentralized energy systems while promoting sustainable energy practices.

In the future power system, an increasing number of distributed energy resources will be integrated including intermittent generation like photovoltaic (PV) and flexible demand like electric vehicles (EVs). It has been long thought to utilize the flexible demand to absorb the PV output locally with the technical solution proposed while an effective commercial arrangement is yet to be developed due to the significant uncertainty associated with local generation. This paper proposes a peer-to-peer (P2P) local electricity market model incorporating both energy trading and uncertainty trading simultaneously. The novelty is to match the forecast power with demand having time flexibility and the uncertain power with demand having power flexibility. This market enables more PV uncertainty to be balanced locally rather than propagating to the upper layer system. In the test case, 55.3% of PV forecast error can be balanced locally in the proposed joint market. In comparison, 43.6% of PV forecast error is balanced locally when the forecast power and uncertain power are traded separately in a day-ahead market and a real-time market. The proposed P2P market can also motivate PV owners to improve forecast accuracy.

Local energy communities are an essential part of the future decarbonized energy systems as they promise to increase participation of end-users and provide incentives for an increased utilization of local variable renewable energy and flexibility potentials. Decarbonizing the building sector will increase the use of sector-coupling technologies and introduce additional demands and flexibility potentials. Energy communities are often linked to local trading concepts or markets, but so far have excluded the effects and potentials of sector-coupled heat generation. In this work, electricity trading in communities with various technologies is formulated as a mixed complementarity problem to study their interaction and local prices. The investigation of a case study—a community with diverse end-users and a high share of renewable energy—reveals the benefits of energy communities, namely the reduction of total cost and CO2 emissions, as well as the increase of communal self-consumption. With the access to local electricity markets, the economic feasibility of combined heat and power units and heat pumps also increases. The community as a whole greatly benefits from heterogeneity of energy demand and generation characteristics of peers; and in a perfect market, each peer is also better-off participating. Therefore, the formation of local energy communities including markets should be inclusive and encourage participation of peers with sector-coupling technologies.

To address the challenge that distributed energy resources (DERs), due to their small individual capacities and dispersed nature, cannot directly participate in electricity markets, thereby enhancing the economic efficiency of system operation, this paper proposes an optimal energy trading strategy for local electricity markets (LEM) incorporating multiple distributed energy aggregators (DERA). The proposed strategy positions the distribution system operator (DSO) as a neutral trading platform that does not generate profits, constructing a two-level optimisation model with multiple DERA that aims to minimise the total system cost. The upper layer DSO coordinates transactions between the upper-level wholesale electricity market (WEM) and the LEM, while the lower layer multiple DERAs optimise their internal resource outputs based on local price signals. Simulation results demonstrate the effectiveness of the proposed model compared to traditional fixed-price bilateral contracts in promoting distributed energy integration, optimising resource allocation, and enhancing overall system economics. This provides both theoretical foundations and practical pathways for designing electricity market mechanisms at the distribution level.

The present article aims at modeling a day-ahead local electricity market (DA LEM) for transactive energy trading at the distribution level. In this regard, a wide range of distributed energy resources (DERs) in the form of multiple aggregators (AGs) participates in the DA LEM in order to trade energy with the distribution system operator (DSO), the operator of the market. On the other hand, the DSO, as the owner of the system, has the responsibility to procure the required energy of its customers with respect to the technical constraints of the distribution network. To settle the designed local market, a Stackelberg gamebased approach is exploited in this research work. In the raised Stackelberg scheme, the leader of the game, the DSO, seeks to maximize its expected profit, while followers of the game, DER AGs, tend to minimize their operating costs. Ultimately, to evaluate the proposed framework, a typical case study is implemented on a modified IEEE-33 bus test system.

No abstract available

Energy communities (ECs) members have a high degree of heterogeneity. Agent‐based modelling (ABM) allows for the modelling of each entity as an agent with distinct characteristics and decision rules. This is particularly useful when the heterogeneity of agents affects outcomes. Market‐based models handle competition and supply–demand interactions and often rely on aggregate or average behaviours, which might overlook important nuances due to individual differences. In this paper, we combine ABM with reinforcement learning (RL) and market models to trade the surplus and demand at the local electricity markets (LEMs) level embedding PyMarket and Mesa packages. It explores integrating electric vehicles, heating and flexibility as vectors to expand ECs using an RL agent to optimally schedule these devices for efficient bidding. Two strategies are proposed: S1‐RL agent predicts only the bidding price and S2‐RL agent predicts both price and quantity. The RL agent in S2 optimizes load to increase demand during local generation intervals, utilizing local surplus more effectively. Financially, S2 outperforms S1, offering more flexibility and optimization in LEMs trading. In summer, the financial savings (FST) increase from 11.58% in S1 to 17.88% in S2, while, in winter, they increase from 4.21% to 5.38%. The efficiency of trading is better, and the traded quantity doubles in summer in S2 compared to S1.

The integration of distributed energy resources (DER), such as solar photovoltaic (PV) panels and electric vehicle (EV) parking facilities, has significantly improved power grid flexibility and efficiency. These resources not only enhance grid management but also enable consumers to reduce costs and generate additional income by trading surplus energy through peer-to-peer (P2P) energy trading. However, the effective utilization of these resources requires adopted and developed optimization strategies to manage operational uncertainties, particularly the variability of renewable energy production, which directly influences electricity market prices. This paper presents an optimization approach for managing risks posed by various uncertain parameters such as the renewable energy-based consumer systems, the availability of EVs in the parking lots, and market prices. In this paper, the uncertainties posed from the renewable resources and EV availability in the parking lots addressed through stochastic programming while the electricity market prices are managed by robust optimization. Electricity prices, as uncertain parameters, can significantly impact decision variables. Since consumers are price-takers rather than price-makers, they have no direct control over market prices, making it crucial for risk-averse decision-makers to address price volatility effectively. By analyzing uncertainties and modeling various scenarios, the proposed approach ensures optimal decision-making under different risk conditions. To evaluate its effectiveness, the model is validated on a case study and also on through a factorial sensitivity analysis with 27 scenarios in a microgrid with multiple households. The optimal configuration (10 homes, 150% PV, 200% ESS) achieves a 38.3% cost reduction compared to the baseline This study provides a comprehensive analysis of the simulation outcomes and real-world implementation, offering valuable insights into improving P2P energy trading in the face of price fluctuations.

The peer-to-peer energy trading has been achieved among nodes in industrial Internet of Things. To establish a secure private market, some meaningful works propose the concept of the energy chain, where one block is added in a linear and chronological order once the trading pair of nodes (buyer and seller) has a valid transaction verified by data audit (e.g., a hash value). Since the buyer applies virtual coins from the credit bank to buy others’ surplus energy, a considerable credit utility is obtained if all nodes are encouraged to meet local power loads out of self-interest. However, such frequent transactions have huge operational overhead, including a long chain maintaining many blocks and an expensive energy transportation cost between trading pairs. To solve these challenging issues, our method enables nodes to satisfy their power loads through local stored energy (self-sufficiency), before participating as sellers if they still have considerable surplus electricity. Without transactions made by some self-sufficient nodes, the operational overhead can be mitigated in a more secure environment. Taking the classic Internet of energy as a case study, we demonstrate the effectiveness of our solutions, and it can achieve a good tradeoff between credit utility and operational overhead.

Local electricity markets (LEMs) have recently emerged as a promising paradigm towards realizing the value of distributed energy resources (DERs), by enabling a) provision of flexibility services (FS) by the DERs to system operators, and b) local energy trading among prosumers. However, existing work has only explored these two market functions in silos and has not comprehensively considered the effect of regulated charges. The recent UK innovation project Liverpool Energy Xchange aims at addressing these gaps, by designing a LEM that enables co-optimized local energy trading and FS provision. This paper firstly outlines the market design aspects of the proposed LEM. Secondly, it presents a quantitative validation of its benefits, based on a test case involving actual UK market data and operating data from prosumer sites in Liverpool, demonstrating a 27% reduction of the yearly net electricity cost. Finally, it discusses barriers the project has identified towards the realization of LEMs.

No abstract available

With the rapid integration of distributed renewable energy into distribution networks, micro-market electricity trading is characterized by multi-participant involvement, temporal flexibility, and complex combinations. These features make the clearing problem challenging in high-dimensional discrete spaces, often leading to computational difficulties and efficiency bottlenecks. To address this issue, this paper proposes a Cognitive Learning and Cooperative Particle Swarm Optimization algorithm (CLC-PSO). In the alternating grouping evolution process, the algorithm introduces a cognitive learning mechanism to construct and refine local structural cognition, enabling the identification, transmission, and utilization of high-quality solution fragments. This helps to alleviate search stagnation caused by sparse fitness signals. Furthermore, by integrating cooperative evolution with a probabilistic update strategy, CLC-PSO effectively enhances global search efficiency and solution quality. Simulation results demonstrate that the proposed method significantly improves convergence speed and solution stability in large-scale, complex trading scenarios, thereby verifying its applicability and advantages for micro-market clearing optimization.

The necessity of end-user engagement in power systems have become a reality in recent times. One of the solutions to this engagement is the creation of local energy markets. The distribution systems operators are compelled to investigate and optimize their asset investment cost in reinforcement of grids by introducing smart grid functionalities in order to avoid investments. The congestion management is the one of the most promising strategies to deal with the network issues. This paper presents a local electricity market or flexibility negotiation as a strategy in order to help the distribution system operator in congestion management. The local market is performed considering an asymmetric action model and is coordinated by an aggregator. A case study is presented considering a simulation that uses a low voltage network with 17 buses, which includes 9 consumers and 3 prosumers, all domestic users. Results show that using the proposed market model, the network congestion is avoided by taking advantage from the trading of consumers flexibility.

Emerging smart grid technologies and increased penetration of renewable energy sources (RESs) direct the power sector to focus on RESs as an alternative to meet both baseload and peak load demands in a cost-efficient way. A key issue in such schemes is the design and analysis of energy trading techniques involving complex interactions between an aggregator and multiple electricity suppliers (ESs) with RESs fulfilling a certain demand. This is challenging because ESs can be of various categories, such as small/medium/large scale, and they are self-interested and generally have different preferences toward trading based on their types and constraints. This article introduces a new contract theoretic framework to tackle this challenge by designing optimal contracts for ESs. To this end, a dynamic pricing scheme is developed such that the aggregator can utilize to incentivize the ESs to contribute to both baseload and peak load demands according to their categories. An algorithm is proposed that can be implemented in a distributed manner by trading partners to enable energy trading. It is shown that the trading strategy under a baseload scenario is feasible, and the aggregator only needs to consider the per unit generation cost of ESs to decide on its strategy. The trading strategy for a peak load scenario, however, is complex and requires consideration of different factors, such as variations in the wholesale price and its effect on the selling price of ESs, and the uncertainty of energy generation from RESs. Simulation results demonstrate the effectiveness of the proposed scheme for energy trading in the local electricity market.

Electric power systems are transitioning towards a decentralized paradigm with the engagement of active prosumers (both producers and consumers) through using distributed multi-energy sources. This paper proposes a novel Blockchain based peer-to-peer trading architecture which integrates negotiation-based auction and pricing mechanisms in local electricity markets, through automating, standardizing, and self-enforcing trading procedures using smart contracts. The negotiation of the volume and price of the peer-to-peer electricity trading among prosumers is modeled as a cooperative game, and the interaction between a retailer and its ensemble of prosumers is modeled as a Stackelberg game. The flexibility provision from residential heating systems is incorporated into the energy scheduling of prosumers. Case studies demonstrate that the proposed architecture in local electricity markets helps improve local energy balance. Flexibility from the residential heating systems enables prosumers to be more responsive to the variation of retail electricity prices. The proposed model reduces 41.24% of average daily electricity costs for individual prosumers or consumers compared to the case without the peer-to-peer electricity trading.

This paper proposes a decentralized scheduling framework for peer-to-peer (P2P) transactive energy trading between prosumers and the local electricity market (LEM). In this approach, prosumers are modeled in the form of networked microgrids (NMG) and smart parking lots (SPL), which can exchange energy in a P2P framework. At the same time, each of the prosumers exchanges information and energy with the LEM in a decentralized approach based on the distribution locational marginal price (DLMP). The LEM is modeled by an active radial electricity distribution network (EDN), considering a high penetration of renewable energy sources (RES) and the feeder power losses in a convex optimization model. In proposed P2P transactive energy model, the alternating direction method of multipliers (ADMM) has been used to achieve the equilibrium point between peers. The distributionally robust optimization (DRO) method has been used to model the uncertain behavior of RES in NMGs, SPLs, and the EDN. In addition, the impact of applying smart charging strategies for electric vehicles (EV) in the proposed P2P transactive energy model has been investigated. To confirm the functionality of the model, 12-bus, 6-bus, and 4-bus networks have been used to model NMGs, while the EDN is embodied by a standard IEEE 33-bus and 123-bus test systems.

This article proposes peer-to-peer trading for multiple integrated energy systems (IESs). In this trading framework, electricity and hydrogen gas are traded in two different forms: electricity is transmitted by the electrical power lines while the hydrogen is transported by trailers via the road transportation network. To optimize this multi-energy trading, a Nash bargaining problem is formulated and solved by the alternating direction method of multipliers (ADMM), which decomposes the trading into two subproblems. The first problem is a local operation problem in a single IES, which aims to fulfill the local operation of IESs and negotiate the traded energy amount and arrival time. The second problem is a price bargain to negotiate the energy prices between IESs. Subsequently, considering the difficulty in solving the trading problem caused by hydrogen delivery through the road transportation network with time delay and service continuity, a novel linearized vehicle transportation model is established to realize the mutual conversion between delivery time frame and geographical area. Finally, the proposed transaction mechanism and method are tested and verified based on a three IESs case located in eastern France. Also, the analysis regarding the optional trading frame, the efficiency of the improved hydrogen transportation model, and the impact of external hydrogen prices are carried out to prove the effectiveness and economy of the proposed frame.

No abstract available

Electric power systems are currently undergoing a transformation towards a decentralized paradigm by actively involving prosumers, through the utilization of distributed multi‐energy sources. This research introduces a fully decentralized multi‐community peer‐to‐peer electricity trading mechanism, which integrates iterative auction and pricing methods within local electricity markets. The mechanism classifies peers in all communities on an hourly basis depending on their electricity surplus or deficit, facilitating electricity exchange between sellers and buyers. Moreover, communities engage in energy exchange not only within and between themselves but also with the grid. The proposed mechanism adopts a fully decentralized approach known as the alternating direction method of multipliers. The key advantage of this approach is that it eliminates the need for a supervisory node or the disclosure of private information of the involved parties. Furthermore, this study incorporates the flexibility provided by residential heating systems and energy storage systems into the energy scheduling of some prosumers. Case studies illustrate that the proposed multi‐community peer‐to‐peer electricity trading mechanism effectively enhances local energy balance. Specifically, the proposed mechanism reduces average daily electricity costs for individual prosumers by 63% compared to scenarios where peer‐to‐peer electricity trading is not employed.

No abstract available

The global energy transition is fundamentally reshaping electricity markets, moving from centralized systems reliant on fossil fuels to decentralized, renewable energy solutions. Distributed energy resources such as solar panels and storage batteries are empowering consumers to become active participants in the energy market. Peer-to-peer (P2P) electricity trading, enabled by blockchain technology, presents a new model for consumers to trade surplus energy directly with one another, bypassing traditional suppliers. This article explores the potential of P2P electricity trading in Georgia, a country with significant renewable energy resources but limited experience with decentralized energy systems. As Georgia aligns its energy policies with European regulations, the development of a P2P trading market could enhance energy security, empower prosumers, and drive local economic development. Through an analysis of global trends, technological innovations, and the current Georgian energy landscape, this article assesses the opportunities and challenges in implementing P2P trading. By building the necessary infrastructure and fostering public participation, Georgia has the potential to transform its energy market and contribute to a more resilient and sustainable energy future.

Renewable Energy Communities (RECs) are emerging as a way to increase self-sufficiency of local communities at a micro-grid scale, as well as to decrease the dependence from large electricity retailers, thus providing bill cost reduction and cleaner energy sources to the prosumers. To facilitate the development of these communities, it is necessary to enable them with management solutions and market services based on real-time measurements from smart energy meters. This way, peer-to-peer (P2P) trading algorithms can be implemented to allow distributed energy exchanges inside RECs. This paper focus on the implementation of a P2P market service for RECs, thus enabling the evaluation of different energy trading algorithms. The developed service was applied to a publicly available dataset concerning a REC with 51 members, 15 of which are prosumers, being the several implemented algorithms compared in terms of average electricity price, community’s self-sufficiency and number of energy trading matches.

Community-level electric energy response is an important method for users' economic transaction settlement and satisfactory energy consumption. A community electricity economic trading method that considers dynamic electricity prices and flexible demand response is proposed in this paper. First, establish a three-tier trading system consisting of the power grid, community operators (CO), and users, where distributed PV, small-scale energy storage (ES), and flexible loads are the targets of regulation. Then, considering the price differences of electricity for solar power, storage, and load, a hybrid game model with dynamic pricing is established, where $\mathbf{C O}$ and users engage in a two-level leader-follower game to minimize economic costs. Furthermore, when the game is balanced, a cooperative game among community groups will be carried out to achieve local consumption of electric energy again. Finally, the feasibility and effectiveness of the proposed method are verified through simulation.

This Paper proposes a fully Decentralized Day ahead (DA) local electricity market (LEM) model which facilitates smooth market design in a restructured power System. LEM has local producers and consumers like PV, WT, DG and energy storage systems. The main objective of this model is to mark the challenge of integrating DERs into a grid that enables to trade energy with the operator at distribution level. As LEM involves DERs whose capacity is insufficient to incorporate a market model DER aggregator (AG) is one of the possible solutions. The proposed model involves a DA market where AGs submit their bids to DSO and the DSO clears the market at the lowest possible price by considering all technical constraints of the network. The Proposed model is evaluated through a case study of a modified 33-bus test system which shows effective energy trading at a distribution system with a considerable marginal range of violations.

This study investigates the integration of electricity and heat systems using local peer‐to‐peer energy and flexibility markets to enhance multi‐energy system efficiency. In the day‐ahead market, prosumers actively trade energy, while system operators manage operational flexibility by dividing it into upward and downward components. Through collaboration, operators and prosumers establish nodal marginal flexibility coefficients, which quantify each prosumer's contribution to system flexibility. These coefficients help operators allocate flexibility more effectively across the network. To address uncertainties and ensure reliability, an hour‐ahead flexibility market is introduced. This market allows prosumers to trade flexibility and lease it to network operators via shared multi‐energy storage systems, improving real‐time adaptability. The alternating direction method of multipliers algorithm facilitates these transactions while preserving prosumer autonomy in decision‐making. The framework is validated through three case studies: a 5‐bus power grid with a 5‐node heating system, a 33‐bus grid with a 23‐node heating system, and a 289‐bus grid linked to a 67‐node heating system involving 30 prosumers. Results show reduced operational costs for prosumers and increased profits for those offering flexibility services, demonstrating the framework's effectiveness in enabling decentralized energy exchanges and improving overall market performance.

This article analysis, through simulation, the impact that local transactions and the penetration of EVs have on the distribution network and the costs and incomes of local market participants. An auction-based competitive market and mathematical model are provided to simulate end-user transactions with EVs on the low-voltage grid. The proposed framework is validated in a scenario considering 55 end-users (some with PV generation and EVs) and six combined heat and power generators trading energy in the IEEE European Low Voltage Test Feeder system. Furthermore, a distribution system operator is considered by defining network constraints (voltage limits and lines overloading) that ensure the correct operation of the distribution grid.

In this paper, a fully decentralized local energy market based on peer-to-peer(P2P) trading is proposed for small-scale prosumers. In the proposed market, the prosumers are classified as buyers and sellers and can bilaterally engage in energy trading (P2P) with each other. The buyer prosumers are equipped with electrical storage and can participate in a demand response (DR) program while protecting their privacy. In addition to bilateral negotiating with the local sellers, these players can compensate for their energy deficiency from the upstream market as the retail market at hours without local generation. In this paper, the retail market price is assumed uncertain. Robust optimization is applied to model this uncertainty in the buyer prosumers model. The proposed decentralized robust optimization guarantees the solution’s existence for each realization of uncertainty components. Furthermore, it performs optimization to realize the hard worse case from uncertainty components. A fully decentralized approach known as the fast alternating direction method of multipliers (FADMM) is employed to solve the proposed decentralized robust problem. The proposed approach does not require third-party involvement as a supervisory node nor disclose the players’ private information. Numerical studies were carried out on a small distribution system with several prosumers. The numerical results suggested the operationality and applicability of the proposed decentralized robust framework and the decentralized solving method.

No abstract available

Incorporating Distributed Energy Resources, which comprise solar, wind energy, and batteries, in traditional energy supply represents a substantial opportunity and challenge regarding the maximization of efficient management of energy along with its proper stability at a grid level. Hence, rising penetrations call for advanced smart grids offering real-time monitoring, analytics based on forecasting capabilities, and control at local decentralised nodes due to high variation and uncertainties brought about by renewables. This study aims to optimize smart grids for seamless integration of DERs by harnessing advanced technologies such as the Internet of Things, machine learning algorithms, and blockchain-enabled energy transaction systems. The methodology includes data collection from a distributed grid network through IoT-enabled sensors, predictive modelling of energy demand and supply through machine learning, and implementation of a decentralized trading mechanism using blockchain technology. Prototyping a smart grid system and testing its efficiency, reliability, and scalability were simulated-based evaluations. Results include a 25% improvement in energy utilization efficiency, a 30% reduction in grid balancing costs, and improving system resilience to such power fluctuations. The study also opens a way to demonstrate the possibility of blockchain usage while allowing energy transactions to be transparent and secure to reduce administrative overhead. Findings on these issues highlight how intelligent technologies play an important role in the transition toward more sustainable and resilient energy systems. This research contributes to the broader effort of creating adaptive smart grids capable of accommodating the growing complexity of modern energy networks.

To address privacy, computational efficiency, and resource allocation issues in distributed energy storage P2P transactions, this paper develops an ADMM-based distributed optimization model. It minimizes user costs under power and storage constraints, using a net contribution-based settlement mechanism to encourage participation. The method decomposes the problem into parallel subproblems, improving computational efficiency while protecting privacy. Case results show the model achieves full renewable consumption with minimal cost increase and lower incremental cost under inaccurate load forecasts, balancing economy and privacy in high-renewable systems.

Distributed energy trading has become an essential part of the energy trading market and provides a useful supplement to traditional centralized energy trading, but there are still problems such as opaque trading information and asymmetric user data. The blockchain technology has the advantages of traceability, trade openness, and data transparency, which is naturally suitable for distributed energy transactions. The electricity information data transmission represented by distributed energy transaction has the characteristics of real-time, which has a high-efficiency requirement on the selected blockchain technology. The consensus algorithm is the core of blockchain technology and affects the efficiency of the blockchain system. The efficiency of the existing consensus algorithms for energy transaction-oriented blockchain still needs to be improved. In this paper, a consensus resource slicing model(CRSM) is designed to meet the requirements of consensus efficiency in energy trading scenarios. Specifically, CRSM divides consensus nodes into different consensus domains for concurrent consensus, and the storage domain only stores block information without consensus. By building an experimental platform, the efficiency of CRSM was verified, the communication pressure of the blockchain system was reduced, and the consensus speed was effectively improved.

Addressing the challenge of ensuring safe and reliable electricity consumption during typhoon conditions in distribution networks, this paper presents a distributed resource trading model. It incorporates scenario probability, considering the spatio-temporal evolution of typhoons on distribution network lines and the analysis of distributed resource failures in the affected area. Initially, a spatio-temporal evolution model of typhoon weather is constructed based on meteorological data. Quantitative analysis of distribution network component failure rates is conducted by establishing a wind field model. Subsequently, Monte Carlo simulation is employed to generate failure scenarios for lines, wind turbines, and photovoltaic units under typhoon conditions. To address uncertainty in wind and solar power output during typhoons, the k-means algorithm is utilized. A distributed resource trading model is then formulated, aiming to minimize the economic dispatch cost of the distribution network during typhoons while considering uncertainty. Finally, a system model is implemented using the Matlab 2023a simulation platform to validate the proposed model's effectiveness.

With the growing penetration of renewable generation and the ongoing restructuring of electricity markets, shared energy storage has become a key resource for enhancing the economic performance and operational flexibility of distribution networks. This paper proposes a distributed optimization strategy for planning and operating shared energy storage systems under distribution network power-flow and security constraints. A mathematical model is formulated to minimize the annualized investment and operation cost of shared storage while reducing system-level operating costs and mitigating net-load peak-valley fluctuations. By constructing the corresponding Lagrangian function and applying a distributed accelerated dual ascent algorithm, the optimal charging and discharging schedules of the shared storage and the associated network operating point are efficiently obtained in an iterative manner. Case studies on the IEEE 33-bus distribution system confirm the effectiveness of the proposed strategy. The results show that the model converges rapidly to the global optimum and, under secure operating conditions, significantly lowers the required storage capacity and unit investment cost, smooths the net-load profile, and improves overall system economic performance.

With the rapid increase of DER(distributed energy resources), it has become a global trend to establish a distributed energy trading markets to coordinate these DERs. However, the construction of distributed energy trading market faces various challenges, in which trusted transaction among market participants is crisis. Since the existing solutions need numerical calculation and communication resource that lead to large delay, this paper proposes a distributed energy trading framework with secure and effective consensus protocol. Different from the traditional PBFT(Practical Byzantine Fault Tolerance) that is the notable debasement on interaction flow between processes, a threshold signature mechanism is introduced and the consensus process of trading is reconstructed. Theoretical analysis shows that the communication complexity of the proposed framework is optimized from $O(n^{2})$ to $O(n)$, and the computation complexity of the participants is also reduced from $O(n)$ to $O(1)$. Our simulation results also show that the task delay of our consensus protocol begins to be lower than PBFT when the number of participants exceeds a certain threshold. In particular, the trusted transaction delay using our consensus protocol is reduced by 12.14% when the number of participants increases to 100.

Distributed ledger technology (DLT) and blockchain-based energy trading solutions are often labeled as resource-hungry, excessive power consumers which consequently create a significant carbon footprint. Using this approach to energy trading, without simultaneously minimizing energy consumption, defeats the purpose of using such trading solutions to encourage renewable energy production and may even cancel out any ecological benefits. This paper demonstrates that it is possible to create a DLT-based energy trading system which provides security, transparency, autonomy, scalability, and decentralization, provided by using a DLT, but with significantly lower penalties than other previously known solutions. This is best illustrated through the fact that the carbon footprint per transaction of the solution presented in this paper is 0.19 grams of $CO_{2}$ compared to 20 grams of $CO_{2}$ created by single transaction of similar complexity on the Ethereum blockchain. This makes per-transaction carbon footprint of our system approximate 100 times smaller than that of the most commonly used Ethereum blockchain. We present the architecture and features of the proposed platform, as well as a thorough analysis of its performance, including power consumption and estimated carbon footprint. All experiments are done on a dedicated Beowulf cluster comprised of general-purpose computers. The cluster mimics a microgrid environment and presents a testing ground for real-world performance and power consumption analysis of a system used for trading energy predominantly produced from prosumers and their renewable sources.

Smart energy technologies, services, and business models are being developed to reduce energy consumption and emissions of CO2 and greenhouse gases and to build a sustainable environment. Renewable energy is being actively developed throughout the world, and many intelligent service models related to renewable energy are being proposed. One of the representative service models is the energy prosumer. Through energy trading, the demand for renewable energy and distributed power is efficiently managed, and insufficient energy is covered through energy transaction. Moreover, various incentives can be provided, such as reduced electricity bills. However, despite such a smart service, the energy prosumer model is difficult to expand into a practical business model for application in real life. This is because the production price of renewable energy is higher than that of the actual grid, and it is difficult to accurately set the selling price, restricting the formation of the actual market between sellers and consumers. To solve this problem, this paper proposes a small-scale energy transaction model between a seller and a buyer on a peer-to-peer (P2P) basis. This model employs a virtual prosumer management system that utilizes the existing grid and realizes the power system in real time without using an energy storage system (ESS). Thus, the profits of sellers and consumers of energy transactions are maximized with an improved return on investment (ROI), and an intelligent demand management system can be established.

This paper presents a method to calculate parameters for aggregating distributed energy resources (DERs), especially electricity consumption of customers. In DER aggregation, energy resource aggregators reward customers complying with requests of demand response. On the other hand, the aggregators need to financially offset opportunity losses of retail electricity suppliers caused by the DER aggregation. This is the negawatt compensation. In the authors' proposal, electricity trading is expressed as a problem of social welfare maximization, allowing changes in the profits of aggregators, retailers, and customers to be measured. As a result, the unit prices of monetary rebate to customers and negawatt compensation to retailers can be calculated mathematically.

Blockchain-based approaches are increasingly being used to provide distributed trust and security in Distributed Energy Trading (DET). However, the state-of-the-art solutions lack scalability, privacy, interoperability, and often have large computational overheads hindering their mainstream adoption for sustainable development. To address these challenges, this paper proposes a multi-relationship network framework (RNF) that uses hypergraphs to organise participants in energy trading networks based on high-order relationships (rather than pairwise) for flexibility, interoperability, data privacy, and reduced resource consumption. Results indicate that the proposed framework outperforms the baseline in terms of: enabling value transfer across multiple blockchain-based DET systems; reducing computational costs and achieving energy efficiency for sustainable development.

In order to solve the influence of uncertainty on dispatch plan in integrated energy system, a multi-objective optimal scheduling method for integrated energy system under multiple uncertainties is proposed. Firstly, the interval mathematical method is adopted to describe the multiple uncertain factors such as coupling unit efficiency, user response rate and power substitution in the system. Secondly, considering the quality difference of energy in the process of production and conversion, in order to solve the influence of uncertainty on the dispatching plan in the integrated energy system, a multi-objective optimal scheduling method for the interval of the integrated energy system under multiple uncertainties is proposed. Firstly, the interval mathematical method is adopted to describe the multiple uncertain factors such as coupling unit efficiency, user response rate and power substitution in the system. Secondly, taking into account the quality difference of energy in the production conversion process, Taking into account the high-quality utilization of energy and the economy of system operation, optimize the system operation with the goal of the highest energy utilization efficiency and the lowest system operating cost; Finally, based on the interval multi-objective linear programming method, a interval multi-objective optimal scheduling model of the integrated energy system under multiple uncertainties is established. The numerical results show that the proposed multi-objective optimal scheduling model can take into account the system operation economy and energy utilization efficiency, and effectively improve the system's ability to cope with multiple uncertainties.

To address the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, this paper proposes a blockchain-based joint auction architecture for distributed energy in microgrids inside and outside industrial parks. By combining blockchain technology and auction theory, the architecture integrates the physical energy transactions within industrial parks with the distributed transactions in external microgrids to meet the centralized trading demand within industrial parks and the scattered peer-to-peer trading demand outside industrial parks, optimizing resource allocation and improving system resilience. In the microgrid auction mechanism for industrial parks, considering distributed energy providers (sellers) and distributed energy buyers, an auction mechanism with power transmission distance, average electricity price, and enterprise nature as its main attributes was constructed to maximize social welfare, realizing efficient energy flow in a multi-microgrid environment and enabling coordinated mutual benefits for producers and consumers within the region. Finally, a case study was conducted on the joint auction mechanism for microgrids inside and outside industrial parks, including the impacts of market dynamics and user preferences on electricity prices using different trading methods, the computational results using different trading matching methods (comparing single-attribute and multi-attribute methods), and multi-dimensional verification of user satisfaction with peer-to-peer transactions in a blockchain environment. The effectiveness of the joint trading between physical energy transactions within industrial parks and external microgrids was demonstrated, which could efficiently coordinate energy allocation inside and outside the parks and reduce the cost of energy configuration.

With the growing global demand for clean energy, green power trading has attracted widespread attention as an important means of optimising energy resource allocation. Due to the intermittent and uncertain nature of green power, traditional power trading methods are inadequate when dealing with complex distributed systems. This paper proposes an optimisation framework for green power trading based on reinforcement learning, using a deep Q network (DQN) and its improved version, Double DQN, to optimise power trading strategies in real time. Experimental verification shows that the proposed method significantly reduces transaction costs and transmission losses, and improves the supplydemand balance of distributed systems. The research results show the potential of reinforcement learning algorithms in dynamic electricity markets and provide new solutions for the efficient use of renewable energy. This research lays the theoretical foundation for the construction of future smart grids and the management of green energy.

This article proposes a peer-to-peer transactive multiresource trading framework for multiple multienergy microgrids. In this framework, the interconnected microgrids not only fulfil the multienergy demands of with local hybrid biogas-solar-wind renewables, but also proactively trade their available multienergy and communication resources with each other for delivering secured and high quality of services. The multimicrogrid multienergy and communication trading is an intractable optimization problem because of their inherent strong couplings of multiple resources and independent decision-makings. The original problem is thus formulated as a Nash bargaining problem and further decomposed into the subsequent social multiresource allocation subproblem and payoff allocation subproblem. Furthermore, fully-distributed alternating direction method of multipliers approaches with only limited trading information shared are developed to co-optimize the communication and energy flows while taking into account the local resource-autonomy of heterogeneous microgrids. The proposed methodology is implemented and benchmarked on a three-microgrid system over a 24-h scheduling periods. Numerical results show the superiority of the proposed scheme in system operational economy and resource utilization, and also demonstrate the effectiveness of the proposed distributed approach.

This paper proposes a distributed power trading model centered around power aggregators, which aims to optimize the trading processes and efficiency of distributed energy. Then, a blockchain-based distributed resource trading platform is designed according to the aggregator trading model. Leveraging blockchain's distributed storage, encryption, and consensus algorithms, this platform ensures transaction visualization, traceability, and security. It fills a crucial gap in blockchain-based smart contract trading mechanisms for distributed power and offers a new theoretical framework for the field.

Abstract The decentralisation of energy supply and demand can contribute decisively to protecting the environment and climate of the planet by consuming electricity in the proximity of the generation source and avoiding losses in transmission and distribution. Supporting energy transactions with emerging intelligent technologies can advance the development of energy communities and accelerate the integration of renewable sources. Distributed energy solutions play an essential role as they are explicitly designed to produce, store and deliver green energy. Profiting with these benefits is essential, especially in the context of the current debate on stopping climate change. Several technologies such as waste heat recovery with intelligent algorithms can improve the energy distribution and provide significant resource savings. On the other hand, the usage of Blockchain technology in energy markets promises to incentivise the use of renewables and provide a reliable framework to monitor real-time information of energy production and consumption. Blockchain can also enable trading between independent agents and lead to the formation of more secured energy communities. In this paper, we demonstrate how Blockchain can be utilised to support the formation and use of energy communities. We propose a Blockchain-based energy framework as a mean to support energy exchanges in a community of prosumers. We demonstrate how smart contracts can manage energy transactions and enable a more secured trading environment between consumers and producers. We utilise data and models from a real fish processing industrial site in Milford Haven Port, South Wales, based on which we validate our research hypothesis.

This paper introduces a new hierarchical optimization framework for coordinating peer-to-peer (P2P) energy trading among users in medium-voltage (MV) and low-voltage (LV) distribution networks. The framework models LV networks as energy communities (ECs) with distributed energy resources (DERs), where community members trade energy internally and with external communities via the MV-LV interface. The MV network, which also integrates DERs, establishes operational strategies for inter-EC energy transactions and efficiently allocates resources to meet the energy demand of the ECs. The optimization models are based on the second-order cone relaxation to consider network restrictions and assume an operation within a day-ahead market structure. Thus, the proposed bilevel optimization model adopts an EC-center approach, with the LV networks acting as multiple leaders at the first level, where the ECs establish the energy requirements, and the second level is represented by the MV network, which adjusts its resource dispatch to ask the energy demand efficiently. The IEEE 33-bus system and a modified version of the IEEE 906-bus systems are used to represent the MV and the LV networks, respectively, and to test the algorithm. Simulation results demonstrate the algorithm's effectiveness in optimizing energy flows, balancing community needs, and complying with network constraints.

Fast-growing distributed energy resources, prosumers, and electric vehicles risk overloading the grid and would require costly infrastructure expansion. In this respect, local energy markets seem to be a promising solution that enables the participation of prosumers and consumers in peer-to-peer energy transactions. However, most existing solutions require substantial computational resources and detailed real-time data, limiting practical deployment on edge devices and in large-scale environments. Conventional negotiation frameworks are mainly synchronous and prepaid, lacking lightweight, scalable, postpaid, and concurrent negotiation protocols to streamline transactions and minimize communication overhead. To address these gaps, we present an advanced three-stage multiagent model for peer-to-peer energy trading within the context of local energy markets, designed for simplicity and ease of integration in resource-constrained settings. This model is strategically engineered to optimize market participation and grid support by orchestrating a one-to-many concurrent composite negotiation strategy that supports postpaid transactions. Empowered by the smart Python multiagent development environment, which harnesses the instant extensible messaging and presence communication protocol, our model ensures seamless execution of peer-to-peer energy transactions with minimal computational burden. Furthermore, the methodology presented is extremely simple and generic compared to other procedures in the literature, facilitating scalable implementation on edge devices and supporting wide real-world adoption.

Free trading of distributed energy resources (DERs) is an effective way to enhance local renewable consumption and user-side economic efficiency. Yet unrestricted sharing may threaten operational security. To address this, this paper proposes a voltage-constrained, differentiated resource-sharing framework for active distribution networks (ADNs). The framework maximizes users’ economic benefits and renewable absorption while keeping system voltages within safe limits. A local energy market with prosumers and the distribution network operator (DNO) is established. Prosumers optimize trading decisions considering transaction costs, wheeling charges, and operational costs. Based on this, a generalized Nash bargaining model is developed with two sub-problems: cost optimization under voltage constraints and payment negotiation. The DNO verifies prosumer decisions to ensure system constraints are satisfied. This paper quantifies prosumer heterogeneity by integrating market participation and voltage regulation contributions, and proposes a differentiated bargaining model to improve fairness and efficiency in DER trading. Finally, an ADMM-based distributed algorithm achieves market clearing under AC power flow constraints. Case studies on modified IEEE 33-bus and 123-bus systems validate the method’s effectiveness, the allocation of benefits between producers and consumers is more equitable, and the costs for highly engaged producers and consumers can be reduced by 46.75%.

With the increasing integration of distributed renewable energy, traditional power users are evolving into prosumers capable of both generation and consumption. However, their decentralized nature poses challenges in resource coordination. This study proposes a bi‐level optimization framework for distribution networks integrating peer‐to‐peer (P2P) energy trading and shared energy storage. The upper‐level model minimizes distribution system operator (DSO) operational costs, including network losses and storage management, while ensuring voltage stability. The lower‐level model enables prosumers to maximize P2P market profits through adaptive load adjustments and shared storage utilization. To address the nonlinear, high‐dimensional optimization challenges, an improved Convex‐Soft Actor‐Critic (C‐SAC) algorithm is developed, combining deep reinforcement learning with convex optimization to achieve privacy‐preserving distributed coordination. Case studies on an IEEE 33‐node system demonstrate that the framework increases prosumer profits by 56.9%, reduces DSO costs by 23.6%, and lowers network losses by 21.5% compared to non‐cooperative scenarios. The shared storage system reduces capacity and power requirements by 20% and 14.1%, respectively. The C‐SAC algorithm outperforms traditional methods (DDPG, SAC) in convergence speed and economic metrics, showing scalability across larger systems (IEEE 69/118 nodes). This work provides a model‐free solution for renewable‐rich distribution networks, balancing efficiency and operational security.

Peer-to-peer (P2P) energy trading in directed networks often overlooks power losses and privacy risks, leading to inefficiencies and security concerns. To address these challenges, we first model the energy trading problem with power losses as a Generalized Nash Game (GNG) and establish the existence and uniqueness of its equilibrium using variational inequality. Then, we propose a distributed algorithm integrating differential privacy to solve the GNG efficiently. Simulation results validate the effectiveness of the proposed approach in preserving privacy, optimizing resource allocation, and ensuring scalability in directed communication networks.

Optimization problems arise in various domains, ranging from power systems to resource allocation and large network management. An effective solution to these problems in a distributed manner has become a crucial research area due to the increasing scale and complexity of modern systems. In this article, we propose a novel local projection global tracking (LPGT) decentralized algorithm based on the Alternating Direction Method of Multipliers for general optimization problems. Unlike existing distributed methods that require problem-specific adaptations or centralized coordination, LPGT provides tailored solutions for handling generic local equality and inequality constraints, as well as global coupled equality and inequality constraints. Moreover, a projection-based analytical scheme is designed to handle generic local equality and inequality constraints without iterative subproblem solvers, and a fully decentralized deviation tracking mechanism is constructed to enforce both global coupled equalities and inequalities constraints via agent communications, eliminating the need for a central coordinator. Case studies for a local energy trading model are proposed to verify the feasibility and applicability of the algorithm.

The increasing integration of distributed renewable energy resources among households has sparked growing interest in peer-to-peer (P2P) energy trading as a means to enhance local energy sharing and prosumer participation. Blockchain technology is often proposed to support these systems due to its ability to ensure transparency security and decentralized control. However blockchain is resource-intensive complex and not universally suitable. Therefore there is a critical need to evaluate whether a given community has sufficient trading activity to justify such infrastructure. This paper proposes a novel methodology to quantify the energy trading potential (ETP) of a P2P community. The P2P energy trading market is modeled as a bipartite graph with consumers and prosumers as nodes and edge weights capturing the potential of each trading pair based on willingness to trade (reflected through buy/sell prices) demand-supply compatibility and communication strength. The total edge weight is normalized to define the Energy Trading Potential Index (ETPI) ranging from 0 to 1 where higher values indicate stronger trading opportunities. Simulations show that the method generally favors balanced communities (with roughly equal numbers of consumers and prosumers) though deviations may occur due to other factors. ETPI serves as a decision support tool throughout the life cycle of a P2P system helping to justify or optimize energy trading infrastructure.

This work introduces a Nano/Pico smart grid energy market model where a market operator (MO) manages price determination for local peer-to-peer (P2P) energy trading among distributed energy resource (DER) sellers and buyers. The MO determines trading prices based on reserved pricing models, factoring in the minimum and maximum price ranges proposed by sellers and buyers. In this framework, the MO calculates the supply-to-demand ratio (SDR) to classify the market into buyers', sellers', or equilibrium modes. The objective is to minimize buyers' bills or maximize sellers' revenues depending on the market mode. The model's effectiveness is evaluated using a dataset from a small community with five buyers and five sellers. Comparative analysis with existing methods demonstrates that the proposed model significantly enhances sellers' profitability and provides substantial savings for buyers. Through the simulation results it is worth noting that the proposed model increases the revenues of the sellers from 17% to 7% compared with the other state-of-the-art methods. Whereas, energy bills for the buyers can also be reduced from 24% to 12%. This percentage increase in revenues and reduction in energy bills validates the proposed model's efficiency and effectiveness compared to the other state-of-the-art methods.

This paper addresses the critical challenges of renewable energy integration and regional power balance in smart grids, which have become increasingly complex with the rapid growth of distributed energy resources. It proposes a novel three-layer scheduling framework with a dynamic peer-to-peer (P2P) trading mechanism to address these challenges. The framework incorporates a preliminary local supply–demand balance considering renewable energy, followed by an inter-regional P2P trading layer and, ultimately, flexible resource deployment for final balance adjustment. The proposed dynamic continuous P2P trading mechanism enables regions to autonomously switch roles between buyer and seller based on their internal energy status and preferences, facilitating efficient trading while protecting regional privacy. The model features an innovative price update mechanism that initially leverages historical trading data and dynamically adjusts prices to maximize trading success rates. To address the heterogeneity of regional resources and varying energy demands, the framework implements a flexible trading strategy that allows for differentiated transaction volumes and prices. The effectiveness of the proposed framework is validated through simulation experiments using k-means clustered typical daily data from four regions in Northeast China. The results demonstrate that the proposed approach successfully promotes renewable energy utilization, reduces the operational costs of flexible resources, and achieves an efficient inter-regional energy balance while maintaining regional autonomy and information privacy.

Peer-to-peer (P2P) energy trading is an emerging solution for optimizing energy distribution in microgrids that requires utilization of local energy resources such as solar panels and energy storage devices. It is envisaged that energy networking will be capable of balancing energy locally and dealing with significant distributed energy resource (DER) penetration in the future. Adopting energy exchange technology in the microgrid system will develop a dynamic electricity market between peers and supplementing consumer needs with renewable energy sources. This study simulates a P2P energy trading system involving five houses, each equipped with solar panels, energy storage and loads. The objective is to identify sellers and buyers based on their current energy storage levels and calculate the amounts of energy to be traded. MATLAB software is used to simulate different patterns of consumption for every home over a 24-hour period. The results demonstrate the effectiveness of dynamically identifying sellers and buyers based on hourly energy storage levels. Houses with higher initial storage and energy generation during peak hours predominantly acted as sellers, while those with higher consumption rates were identified as buyers. The calculated amounts of energy to be sold and purchased were adjusted, ensuring accurate trading volumes that reflect the present energy supply and demand conditions. This practical simulation provides valuable insights in showcasing how energy can be efficiently distributed within a prosumer. The study contributes to the understanding of instantaneous energy trading mechanisms and highlights the potential for optimized energy management through P2P systems.

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The incorporation of peer-to-peer (P2P) energy trading mechanisms within community microgrids represents a transformative shift towards decentralized and consumer-driven energy markets. While this paradigm enhances grid flexibility and promotes renewable energy utilization, it also introduces critical cybersecurity challenges due to the reliance on advanced communication networks and distributed ledger technologies. This paper presents a comprehensive analysis of cybersecurity strategies tailored for P2P energy trading in community microgrids. We investigate potential attack vectors including data tampering, unauthorized access, and denial-of-service attacks, and examine their impact on trading integrity and grid stability. The study evaluates contemporary solutions such as blockchain-based authentication, privacy-preserving transaction protocols, and intrusion detection systems adapted to resource-constrained environments. Furthermore, a layered security framework is proposed, integrating cryptographic mechanisms, real-time monitoring, and resilient network architectures to safeguard trading operations. Simulation-based validation demonstrates the effectiveness of the proposed strategies in mitigating cyber risks while maintaining operational efficiency. The findings aim to guide future research and practical deployment of secure, scalable, and trustworthy P2P energy exchange platforms in communal microgrids.

The peer-to-peer (P2P) energy market constitutes a potent infrastructure for coordinating distributed energy resources. However, its capacity to elicit decentralized prosumer participation in real-time regulatory provision remains inadequately characterized. This study develops a novel trilevel coordination framework that explicitly links the distribution system operator), a federated power plant (FPP) manager, and individual prosumers to integrate bilateral P2P trading with real-time system regulation. We propose an incentive-aligned pricing mechanism derived from differential-cost analysis that motivates prosumers to self-organize within FPPs and commit regulation capacity while safeguarding their private economic incentives. To overcome the intrinsic nonconvexity of the resulting trilevel market-clearing problem, we formulate and employ an exact relaxation condition that convexifies the embedded equilibrium constraints, producing a computationally tractable reformulation suitable for real-time deployment. For decentralized implementation under communication and privacy limits, we design a pruning-enhanced distributed bipartite-matching algorithm based on the Hungarian method. Day-ahead shadow prices are used to preselect candidate trading pairs, substantially reducing exchange overhead and problem dimensionality. Case studies on a modified IEEE 69-bus system corroborate that the proposed methodology attains socially optimal allocations in real time with minimal computational burden and rapid empirical convergence.

We present a market-clearing mechanism for local energy markets that jointly determines physically feasible energy schedules and payment allocations among prosumers. Our approach integrates grid constraints-such as line loadability-directly into the clearing process, ensuring that trades remain within network limits. Payments are distributed via a Nash bargaining formulation that promotes cooperative behavior and reflects each agent's contribution. This physically-constrained transactive energy framework enables decentralized trading that is both equitable and grid-compliant. In a case study with three prosumers, our method achieves up to 45.36% cost savings in the prosumer with highest demand and improved fairness compared to heuristic or physically naïve baselines.

This article presents a novel two-tier peer-to-peer (P2P) market paradigm for energy sharing between multiregional proactive prosumers (producer+consumer) in transactive energy systems. In the proposed approach, interactions between prosumers can be achieved in inter-area markets as well as intra-area markets. A dual decomposition based and a consensus-based distributed market-clearing approaches are designed for the proposed double-layered hierarchical intra- and inter-area markets. The proposed approach preserves prosumers’ privacy by sharing only partial information with the fellow prosumers while settling the trade. Compared to the conventional P2P approaches, the proposed two-tier market-clearing scheme allows more prosumer participation in energy trading and significantly enhances their economic benefits in certain situations. The effectiveness of the proposed approach is validated through software simulations.

The local flexibility market models have emerged as a market-based solution to respond to the challenges that the increase in distributed energy resources caused in the power and energy systems. Using Smart Grid enabling technologies, consumers and prosumers are prepared to respond to any possible demand-side flexibility event. In this scope, this work presents an advanced bidding model for the prosumers/consumers’ participation in a local flexibility market to solve existing issues in the local grid. The proposed advanced model consists of a single-sided auction-based clearing method where prosumer offers are ranked and chosen according to the price and other characteristics, such as their location and distance to the problem to be solved. The aim is to prioritize and select the offers that have a more positive impact on the situation to solve at the lowest possible cost.

In the existing literature, P2P energy trading market can be cleared in a centralized, distributed or decentralized manner. The decentralized market structure is in line with the energy prosumer dominance essence. However, the iteration process in the current decentralized market-clearing method poses significant challenges to the computation and communication of the power system. Therefore, we develop a non-iterative decentralized market-clearing method in this article. We first model the energy trading behaviors inside microgrids as Stackelberg games. Since the microgrids and prosumers have heterogeneous market roles, we employ a continuous double auction mechanism to simulate the bidding processes of microgrids after the intra-microgrid markets are cleared. Then, we analyze the energy buyers' best responses to the sellers' prices and formulate the traded power as piecewise constant functions concerning prices. We linearize the distribution network security constraints as piecewise constant constraints. We propose the concepts of active trading segments and critical security constraints based on these piecewise constant functions. According to numerical simulation results, our proposed method can effectively identify the substitution model within a few steps. Considering the heterogeneous market roles and detailed distribution network constraints, the identified substitution model could accurately portray the market members' behaviors. The non-iterative market-clearing time does not increase with the problem scale, which verifies the computational efficiency.

A distributed data-driven algorithm is proposed for market clearing within a community-based energy market. The objective is to maximise social welfare in energy trading. Each participant in the market, known as a prosumer, employs an extremum-seeking control (ESC) algorithm within a consensus-based architecture based on simple third-order dynamics. The proposed method circumvents the need for a central coordinator in the market clearing process. Furthermore, prosumers do not require analytical and exact formulations of cost or utility functions. They are required to transmit only a single local decision variable to the neighbours through an undirected connected graph. Under the assumption that prosumers have strongly convex local objective functions, the proposed algorithm demonstrates semi-global practical asymptotic (SPA) convergence to the optimal solution. This convergence is established using averaging theory. Simulation results validate the effectiveness, scalability, and robustness of the proposed distributed strategy.

Photovoltaic energy storage system (PV-ESS) prosumer aggregators are characterized by a large number but small scale in the distribution system and are not competitive enough to participate in market transactions. For this reason, a prosumer aggregator alliance is proposed to participate in the distribution market bidding strategy. Firstly, based on the framework for prosumer aggregator alliances participating in distribution market trading, a bilevel bidding model is constructed. The upper level represents the optimal decision-making model for the prosumer aggregators, while the lower level constitutes the distribution market-clearing model. Secondly, the additional benefits obtained by the alliance are distributed more fairly using the improved Shapley value based on the PV self-consumption rate. Given the problem that the traditional diagonalization algorithm (DA) has an excessive number of iterations when solving the game equilibrium problem of multiple subjects, the DA is improved by optimizing the initial value of the inputs. Finally, case studies are conducted based on the improved IEEE-33 bus distribution system to validate the feasibility and economic viability of the proposed strategy. The case study results show that forming cooperative alliances to participate in market bidding can significantly increase overall profits. The improved DA reduces the number of bids and computation time by 75% and 80%, respectively. Additionally, the improved Shapley value facilitates compensation for some of the aggregators.

This paper proposes a market mechanism that enables the advanced distribution management system (ADMS) for energy trading in the local energy market. Two primary functions of the ADMS are discussed: reducing operational costs and coordinating the energy community (EC). The integration of these two functionalities considers all the constraints for maintaining distribution system reliability. Two energy transaction actions are allowed for the ECs: 1) each prosumer in the EC trades energy locally to maximize the global social welfare and pay network usage fees to the distributed system operator (DSO); 2) the EC trades energy with the DSO to sell their exceed energy or purchase energy to meet their load demand. The payment among the ECs is subject to a clearing price. In the market, the Newton distributed algorithm has been used to achieve the equilibrium point, and the ADMS actively optimizes voltage, and reactive power (Volt-VAR) via controlling distributed energy resources (DER) and managing the iterative process. To confirm the functionality of the model, 33-bus networks have been used to model the ECs.

Flexible resources such as renewable energy and electric vehicles are expanding. In order to efficiently utilize the energy from various types of prosumers, an energy sharing market mechanism between prosumer aggregator and charging stations is proposed. Based on the PJ-ADMM algorithm, the proposed mechanism is capable of parallel iterative computation to realize market clearing. We validate the effectiveness of the proposed mechanism through a case study. After participating in energy sharing, the social welfare is improved and the resources can be optimally allocated.

Peer-to-peer (P2P) energy trading and energy communities have garnered much attention over in recent years due to increasing investments in local energy generation and storage assets. However, the efficiency to be gained from P2P trading, and the structure of local energy markets raise many important challenges. To analyse the efficiency of P2P energy markets, in this work, we consider two different popular approaches to peer-to-peer trading: centralised (through a central market maker/clearing entity) vs. fully decentralised (P2P), and explore the comparative economic benefits of these models. We focus on the metric of Gains from Trade (GT), given optimal P2P trading schedule computed by a schedule optimiser. In both local market models, benefits from trading are realised mainly due to the diversity in consumption behaviour and renewable energy generation between prosumers in an energy community. Both market models will lead to the most promising P2P contracts (the ones with the highest Gains from Trade) to be established first. Yet, we find diversity decreases quickly as more peer-to-peer energy contracts are established and more prosumers join the market, leading to significantly diminishing returns. In this work, we aim to quantify this effect using real-world data from two large-scale smart energy trials in the UK, i.e. the Low Carbon London project and the Thames Valley Vision project. Our experimental study shows that, for both market models, only a small number of P2P contracts, and only a fraction of total prosumers in the community are required to achieve the majority of the maximal potential Gains from Trade. We also study the effect that diversity in consumption profiles has on overall trading potential and dynamics in an energy community.

The disruptive innovation of renewable energy generation affects energy consumers' ability to self-generating electricity in their area. The inequality of surplus and demanded energy in the neighborhood area takes place in a local energy market. Regarding the market model, an aggregated model is considered, and the action between participants is set as non-cooperative. The request from market participants to the market operator is to maximize the profit as much as possible. To handle the critical feature is on the market clearing algorithm. The paper has selected the Nikaido-Isoda function and Relaxation Algorithm as solving tool. Furthermore, the function of the seller’s revenue and buyer’s cost saving, and market clearing price are added to determine the maximum profit. To present the market clearing process, the case study containing five prosumers is implemented. The discussion is on the difference between the local price and the retail price, and the allocated energy quantity according to prosumer bidding strategy.

With the integration of massive distributed energy resources (DER) into the distribution network, establishing a sound electricity market trading system that attracts prosumer participation to attain local supply-demand balance is an urgent issue. This paper proposes a hierarchical architecture in which virtual power plants (VPPs) act as the primary participants to tackle this challenge. At the upper tier, to achieve the dayahead market (DAM) between VPPs with complex relationships, a cloud-edge collaboration based market clearing scheme and a strategy formulation method for prosumers incorporating multiple game scenarios and time advancement is constructed, which can be solved by different game theories. At the lower tier, to mitigate the issues arising from discrepancies between actual and forecasted new energy generation for the delivery of power traded in the DAM, a distributed intraday market (IM) is organized for specific VPPs without relying on the grid corporation (GC). In general, compared with the benchmark methods, the proposed DAM system can effectively improve the revenues of sellers, while the profits of buyers and the GC are reduced to a certain extent but within an acceptable range. And the electricity interactions between VPPs and the GC in the DAM are significantly reduced. Compared with the proposed DAM combined with the GC–controlled IM structure, the proposed DAM integrated with the distributed IM structure shows decent improvements in autonomous power trading completion, participants revenue affected by new energy uncertainty and the local consumption of new energy.

With the increasing penetration of distributed energy resources (DERs), traditional grid-dependent consumers are evolving into active prosumers, who can control their generation and demand while interacting with peer neighbors to earn profits. This article presents a novel encryption-based coordinated peer-to-peer (P2P) trading framework for kilowatt (kW) and negawatt (nW) transactions, incorporating the utilization of second-life batteries (SLBs) from retired electric vehicles (EVs). In this framework, two different types of P2P transactions are coordinated, allowing prosumers to switch their market roles freely between kW and nW markets based on optimization results and market-clearing information. The optimal market bidding strategy is determined by the household energy management system (HEMS) optimization, and a double-sided auction method is employed for market clearing. To protect prosumer privacy and prevent potential data integrity attacks (DIAs) in the P2P markets, Rivest–Shamir–Adleman (RSA) digital signatures and Goldreich–Goldwasser–Halevi (GGH) encryption algorithms are implemented. Unlike the traditional battery energy storage systems (BESSs) that assume constant charge/discharge power and efficiency, the non-ideal battery models are embedded to capture the impacts of BESS operation on the real-life P2P trading markets. The battery cycling degradation issue is also incorporated. The effectiveness of the proposed framework is demonstrated on the modified distribution system.

This paper introduces a model for coordinating prosumers with heterogeneous distributed energy resources (DERs), participating in the local energy market (LEM) that interacts with the market-clearing entity. The proposed LEM scheme utilizes a data-driven, model-free reinforcement learning approach based on the multi-agent deep deterministic policy gradient (MADDPG) framework, enabling prosumers to make real-time decisions on whether to buy, sell, or refrain from any action while facilitating efficient coordination for optimal energy trading in a dynamic market. In addition, we investigate a price manipulation strategy using a variational auto encoder-generative adversarial network (VAE-GAN) model, which allows utilities to adjust price signals in a way that induces financial losses for the prosumers. Our results show that under adversarial pricing, heterogeneous prosumer groups, particularly those lacking generation capabilities, incur financial losses. The same outcome holds across LEMs of different sizes. As the market size increases, trading stabilizes and fairness improves through emergent cooperation among agents.

A framework for the cooperation of renewable sources and demand response entities is presented in both the day-ahead and real-time markets, set against the backdrop of a microgrid. A robust dynamic energy trading algorithm is presented that facilitates energy exchange between consumers, prosumers, energy storage, and responsive loads, the key players in the microgrid. The proposed algorithm enables day-ahead energy trading in a microgrid using a genetic algorithm-based optimizer that determines the economic dispatch and market clearing price (MCP) based on the bids placed by the market players. The energy traded is based on the MCP and any energy deficit is procured from the legacy grid. An ancillary service provision from batteries, flexible loads, and reserve is included to handle uncertainties.This approach enhances market participation for the prosumer, ensuring profitability for users of renewable energy while addressing uncertainties in energy supply. The proposed framework provides a practical way to enable and integrate modern microgrid services into an energy trading model with dynamic pricing ensuring efficient interaction among these entities.

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Currently, the distribution systems are moving towards decentralized operation due to the high penetration of distributed energy resources (DERs). Peer-to-peer (P2P) energy trading has been an emerging concept that promotes autonomous DER participation in energy markets while preserving their privacy concerns. In this work, a novel P2P energy trading enabled decentralized market framework is proposed for the optimal operation of distribution grids. Nodal agents and P2P agents are established as market participants, and market equilibrium is iteratively achieved via alternating direction method of multipliers based algorithms. The proposed market framework guarantees grid constraint satisfaction, market equilibrium, and global optimality for all market participants without violating their privacy concerns. The agent coordination and local optimization are designed such that fairness of the market clearing mechanism, prosumer autonomy, and prosumer anonymity is preserved without compromising the market efficiency. Further, costs/rewards of ancillary services associated with the P2P energy transactions are considered as trade-offs within the market mechanism, and those are accurately allocated to the respective trading pairs. The case studies illustrate the effectiveness and scalability of the proposed market framework.

No abstract available

With increasing local energy generation consumers are converting into prosumers by selling their excess generation with their peers. This transaction of locally generated energy is called Peer-to-Peer (P2P) energy trading. For P2P energy trading, a community system is widely endorsed but causes an increase in overall system cost as both prosumer and consumer need to pay charges to the community manager. In this context, a hybrid P2P energy trading market is implemented through a decentralized blockchain-based mechanism. It permits market participants to trade energy directly without involving an additional entity. Contracts play a crucial role in blockchain-based energy trading mechanisms. Thus, smart contracts are designed for the efficient implementation of this trading mechanism. These contracts are designed using the Solidity platform on REMIX IDE. Prosumer and consumer interact among themselves through the main smart contract to execute P2P energy trading till market-clearing time. Afterwards, they interact with the grid through Peer-to-Grid (P2G) smart contracts for additional sell/purchase of energy. Results show that the proposed approach leads to a smart, secure and economical mechanism of energy trading.

Peer-to-peer (P2P) energy trading has recently emerged as a promising paradigm for integrating renewable and distributed energy resources into local energy grids with the presence of active prosumers. However, prosumers often have different preferences on energy trading price and amount. Therefore, in decentralized P2P energy markets, a negotiation between prosumers is needed to obtain a commonly satisfactory set of preferences, i.e., a market-clearing solution. To achieve that, this paper proposes a novel approach in which a decentralized inverse optimization problem is solved by prosumers to cooperatively learn to set their objective function parameters, given their preferential intervals of energy prices and amounts. As such, prosumers’ parameters can be determined in specific intervals computed analytically from the lower and upper bounds of their preferential intervals, if a certain learning condition is satisfied. Next, the structural robustness of prosumer’s cooperative learning against the malicious and Byzantine models of cyberattacks is studied with the weighted-mean-subsequence-reduced (WMSR) resilient consensus algorithm. A novel sufficient robustness condition is then derived. Finally, case studies are conducted on the IEEE European Low Voltage Test Feeder system to validate the effectiveness of the proposed theoretical results.

Peer-to-peer (P2P) energy trading allows surplus energy to be traded between distributed energy resources (DER) and prosumers in the community microgrid. In Malaysia, P2P energy trading is still under development, where it is expected to be exclusively participated by commercial and industrial prosumers. This paper proposes how a motivational psychology framework can be used effectively to design P2P energy trading to increase user participation for residential prosumer. All the data such as power consumption and solar energy value are adjusted and modelled in such a way to facilitate the calculation of P2P energy trading in Malaysia. An auction-based P2P market clearing model is then proposed and solved by using the Linear Programming optimization approach. The numerical results show the sustainability and the potential of the proposed P2P energy trading model to attract residential customers to participate in energy trading.

This paper investigates prosumers' flexibility provision for the optimal operation of active distribution networks in a transactive energy (TE) market. From a prosumer point of view, flexibility can be provided to operators using renewable energy resources (RES) and demand response (DR) through home appliances with the ability to modify their consumption profiles. In the TE market model, the distribution system operator (DSO) is responsible for market-clearing mechanisms and controlling the net power exchange between the distribution network and the upstream grid. The contribution of this work is the enhancement of a strategy to reduce operational costs of an active distribution network by using prosumers' flexibility provision through an aggregator or a smart building coordinator. To this end, a TE market for both energy and flexibility trading at distribution networks is presented, demonstrating the possibility to fulfill DSO requirements through the flexibility contributions in the day-ahead (DA) and real-time (RT) markets.

In order to promote the reform of retail power market and realize the "friendly" consumption of distributed energy access on the resident side, this paper proposes a quantitative model of decentralized power retail transaction (DPRT) using blockchain technology. This paper models the structure, mechanism, mode and benefit evaluation of power retail transaction between prosumers, and gives the equilibrium clearing model of DPRT. Finally, an example is given to verify that the proposed DPRT can reduce the buyer's cost, improve the seller's profit, improve the prosumer's satisfaction with DPRT. This paper provides a way for the intelligent DPRT in the open distribution network power market.

The diversity of prosumers’ resources in energy communities can provide significant technical and economic benefits to both prosumers and the distribution system operator (DSO). To maximize these benefits, a coordination framework is required to address all techno-economic constraints as well as the objectives of all agents. This paper presents a fully distributed market-clearing scheme to coordinate the strategies of agents within a local energy community. In the proposed framework, prosumers, the DSO, and the local market operator (LMO) are the participating agents. The framework addresses the preferences and techno-economic constraints of all actors while preserving their privacy. The proposed model is based on a modified alternating direction method of multipliers (ADMM) method with two outer and inner loops; the outer loop models the interactions between the LMO and prosumers, while the inner loop addresses the interactions between the LMO and the DSO. The model is demonstrated on IEEE-69bus test network, showcasing its effectiveness from various perspectives.

This paper aims to develop an optimisation-based price bid generation mechanism for the sellers and buyers in a double-auction-aided peer-to-peer (P2P) energy trading market. With consumers being prosumers through the continuous adoption of distributed energy resources, P2P energy trading models offer a paradigm shift in energy market operation. Thus, it is essential to develop market models and mechanisms that can maximise the incentives for participation in the P2P energy market. In this sense, the proposed approach focuses on maximising profit at the sellers, as well as maximising cost savings at the buyers. The bids generated from the proposed approach are integrated with three different market clearing mechanisms, and the corresponding market clearing prices are compared. A numerical analysis is performed on a real-life dataset from Ausgrid to demonstrate the bids generated from sellers/buyers, as well as the associated market clearing prices throughout different months of the year. It can be observed that the market clearing prices are lower when the solar generation is higher. The statistical analysis demonstrates that all three market clearing mechanisms can achieve a consistent market clearing price within a range of 5 cents/kWh for 50% of the time when trading takes place.

No abstract available

No abstract available

This paper presents a comparative analysis of market-clearing methods in a community-driven transactive energy micro-grid aimed at enhancing community self-sufficiency, boosting self-consumption, and reducing costs for prosumers. The study explores various market-clearing approaches, including bill sharing, supply-to-demand ratio, and mid-market rate, to establish internal market prices for peer-to-peer energy transactions within the community microgrid. A deep learning algorithm is applied to forecast the participants' day-ahead demand and generation profiles. Simulation results demonstrate a marked improvement in self-sufficiency, higher self-consumption, and lower energy costs for participants across the community.

Peer-To-peer (P2P) energy trading has emerged as an innovative alternative to centralized energy system. However, the variability in participants’ bidding patterns introduces uncertainty in the Market Clearing Prices (MCPs) within P2P markets. To address this issue, the study investigates various market clearing mechanisms, including Average, Vickrey-Clarke-Groves (VCG), Trade Reduction (TR), and McAfee, in a double auction based P2P market using real-world data from the Ausgrid Dataset. The impact of the bidding and asking price distributions on the uncertainty in MCP are analyzed mathematically and the relative performances across different mechanisms are investigated. A numerical simulation is performed to evaluate the MCPs for two sets of users- 30 prosumers and 127 prosumers over an entire year. Simulation results indicate that VCG mechanism exhibit the highest variance in MCP for buyers (38.47 and 36.78 for L = 30 and L = 127, respectively) whereas average mechanism has the least variance in MCP (21.06 and 20.98 for L = 30 and L = 127, respectively). The insights obtained from this analysis will help design new algorithms that can manage the impact of uncertainties more effectively.

The energy sector is undergoing a transformative shift, driven by advancements in Distributed Energy Resources (DERs), the digitization of the energy supply chain and decarbonization policy objectives across the world. This paradigm shift has led to the emergence of Local Energy Markets (LEMs), which enable small-scale prosumers to actively participate in the energy market, trade power, and leverage their flexible resources. To ensure the success and acceptance of LEMs, this paper proposes a cooperative game-theoretic approach that fosters prosumer engagement and fair profit allocation. We utilize prospect theory from behavioural economics to examine the decision-making process of prosumers and incorporate their preferences for changes in wealth status. By adopting a cooperative game structure, prosumers can pool their resources, reduce transaction costs, and enhance data utilization. The paper introduces a novel pricing algorithm inspired by prospect theory that incentivizes prosumer participation and accounts for the uncertainty involved in LEM operations. Additionally, a computationally efficient method for profit allocation based on the variation of the Shapley value is proposed to ensure scheme stability. A use case evaluation is conducted on a real-world low-voltage network, demonstrating the effectiveness of the proposed approach in terms of economic efficiency and market characteristics. The results highlight the benefits of the consumer-centric LEM, including improved local trading dynamics, fair profit distribution, and enhanced grid stability. Overall, this research contributes to the design and development of LEMs that prioritize prosumer engagement, community cooperation, financial inclusion and democratization of the energy market.

No abstract available

Power networks witness a high penetration of distributed energy resources (DERs). These DERs produce a new generation of end-users known as prosumers, who can both consume and generate energy. The increasing number of prosumers creates opportunities for much more flexible energy markets. Peer-to-peer (P2P) energy trading has arisen as a novel trading paradigm to facilitate the exchange of surplus energy between prosumers and consumers in local markets. In this context, this paper proposes an optimization model for P2P energy trading among energy prosumers connected to the utility grid. Each prosumer is equipped with renewable energy resources. Demand side response (DSR) strategies are incorporated to reduce costs effectively. The optimization problem is solved using the genetic algorithm considering three different scenarios. The simulation results demonstrate that incorporation of DSR strategies and P2P trading mechanisms reduces significantly the overall prosumers’ operating costs compared to conventional grid-only operations.

In the context of a high proportion of new energy grid connections, demand-side resources have become an inevitable choice for constructing new power systems due to their high flexibility and fast response speed. However, the response capability of demand-side resources is decentralized and fluctuating, which makes it difficult for them to effectively participate in power market trading. Therefore, this paper proposes a robust transaction decision model for demand-side resource aggregators considering multi-objective clustering aggregation optimization. First, a demand-side resource aggregation operation model is designed to aggregate dispersed demand-side resources into a coordinated aggregated response entity through an aggregator. Second, the demand-side resource aggregation evaluation indexes are established from three dimensions of response capacity, response reliability, and response flexibility, and the multi-objective aggregation optimization model of demand-side resources is constructed with the objective function of the larger potential market revenue and the smallest risk of deviation penalty. Finally, robust optimization theory is adopted to cope with the uncertainty of demand-side resource responsiveness, the robust transaction decision model of demand-side resource aggregator is constructed, and a community in Henan Province is selected for simulation analysis to verify the validity and applicability of the proposed model. The findings reveal that the proposed cluster aggregation optimization method reduces the bias penalty risk of the demand-side resource aggregators by about 33.12%, improves the comprehensive optimization objective by about 18.10%, and realizes the optimal aggregation of demand-side resources that takes into account both economy and risk. Moreover, the robust trading decision model can increase the expected net revenue by about 3.1% under the ‘worst’ scenario of fluctuating uncertainties, which enhances the resilience of demand-side resource aggregators to risks and effectively fosters the involvement of demand-side resources in the electricity market dynamics.

In the smart grid environment, the participation of electric vehicles in demand side response will affect the operation of the grid.How to effectively use electric vehicles to make power grid operation more stable will be the future development of smart grid to face. In the light of the uncertainty created by the participation electricity users in the demand side response, this paper establishes electricity users' demand side response model based .By studying the load characteristics of electric vehicles and developing an effective trading mechanism, both power grid companies and users can reach a deal.this paper establishes electricity users' demand side response model based on two kinds of price mechanism (i.e. real-time pricing and time-of-use pricing) to analyse changes in consumer consumption patterns and load transfers. Testing the improved the participation of users in the demand side response model, this paper makes use of numerical stipulations under different restrictions through MATLAB.

With high penetration of renewable energy in power system, the demand side response is gradually considered to be an important flexible resource, leading to active participation in electricity market. This article provides a design of demand response trading platform for load aggregators, with integration of end-edge-cloud collaboration and blockchain. Firstly, the end-edge-cloud collaborative architecture enables distributed resources entering electricity market efficiently and realizes the privacy protection of users’ production and operation data, by hierarchical decomposition and coordination of computing and data storage tasks. Meanwhile, certifying and accounting the transaction results through Consortium Blockchain technology can improve security and credibility of transaction on the Platform. Secondly, credit evaluation is taken into account when distributed resources aggregation and instructions decomposition, which encourages the user to strictly execute the contract, thus, mitigate the negative impact for aggregators. Credit evaluation is only carried out with the help of consortium blockchain, providing a secure, efficient and mutually trusted trading ecosystem.

With the development of the electricity market and the increasing importance of demand-side management, next-day demand-side resource transaction size measurement has become one of the key challenges to improving the operational efficiency of the market. To address this problem, this study proposes a prediction model combining LassoNet and Stacked Multilayer Perceptron (S-MLP). LassoNet is used as a feature selector to achieve a sparse representation of the input features using L1 regularisation to filter out key predictor variables. The selected features are passed to the S-MLP, which cascades multiple MLP hierarchies to capture complex nonlinear relationships and patterns, thereby enhancing the accuracy of the size measurement. In this paper, the validity and performance of the model are validated on a real demand-side resource trading dataset, and compared to other models, the performance of this paper's method is reduced by at least 0.0003 on the MSE, at least 0.001 on the RMSE metric, and at least 0.009 on the MAE metric.

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multi-energy systems (MES) have emerged as a vital solution for integrating and optimizing various energy sources, such as electricity, heating, and cooling, within energy markets. This paper proposes a method that leverages advanced energy management techniques to assess the performance of energy hubs (EHs) on the demand side. The EH, which integrates key components such as wind turbines (WTs), energy storage systems (ESS), and combined heat and power (CHP) units, is designed to meet the demand for electricity, heating, and cooling. A critical aspect of the optimization involves implementing demand response programs (DRPs) specifically tailored to electrical demand, which help in adjusting consumption patterns to reduce costs. The results of the study demonstrate the substantial flexibility offered by effective EH management, enabling a more dynamic response to fluctuating energy demands. Through detailed simulations, the study shows that incorporating renewable energy sources (RES) and demand response strategies can achieve 2226.2 dollars reduction in the operating costs of EHs.

Community energy storage systems must balance equitable energy sharing among prosumers with long-term battery health. While forecast-driven allocation strategies improve fairness and operational efficiency of such systems, their impact on battery degradation remains underexplored. This study integrates supply-demand forecasting with a comprehensive battery aging model to examine the trade-offs between system performance and asset longevity in community storage applications. An extended power-law degradation model is used to capture the combined effects of state-of-charge variability, C-rate fluctuations, and thermal conditions on capacity fade mechanisms. To address these dynamics, a multi-objective optimization framework with special ordered sets linearization is proposed, balancing degradation minimization with renewable self-consumption maximization under adaptive power constraints. Validation using a 10-year dataset of five residential prosumers sharing a 20 kWh system shows that forecast-driven control enhances utilization while reducing capacity retention from 93.88% to 85.45% due to intensified cycling. The proposed degradation-aware optimization mitigates this penalty, retaining 91.01% capacity—representing a 6.5% improvement over the base forecast approach—while preserving efficiency gains. Results highlight that intelligent state-of-charge management with adaptive power limiting can reduce stress-induced aging while maintaining predictive scheduling advantages, particularly during periods of renewable energy surpluses when aggressive charging strategies become acceptable from a degradation perspective. The proposed framework demonstrates that sustainability and equity in community energy systems need not be mutually exclusive objectives.

Planned Communities (PCs) present a unique opportunity for deployment of intelligent control of demand-side distributed energy resources (DER) and storage, which may be organized in Microgrids (MGs). MGs require balancing for maintaining safe and resilient operation. This paper discusses the implications of using MG concepts for planning and control of energy systems within PCs. A novel tool is presented, based on decision trees (DTs), with two potential applications: (i) planning of energy storage systems within such MGs and (ii) controlling energy resources for energy balancing within a PC MG. The energy storage planning and energy balancing methodology is validated through sensitivity case studies, demonstrating its effectiveness. A test implementation is presented, utilizing distributed controller hardware to execute the energy balancing algorithm in real-time.

The integration of distributed energy resources (DERs) and digital technologies has accelerated the transition to decentralized energy systems. Among these technologies, blockchain stands out for its ability to facilitate peer-to-peer (P2P) energy trading efficiently and securely. This paper explores the concept of P2P energy trading within community microgrid systems, leveraging blockchain-based smart contracts. The proposed system integrates an incentive-driven demand response program directly into the smart contract framework, offering real-time rewards for load-balancing contributions. By incorporating the microgrid’s Energy Management System (EMS) and transparently recording all transactions on the blockchain, the proposed platform provides detailed data and immediate reward distribution. At the core of our system lies the Supply to Demand Ratio (SDR), ensuring fair energy exchange within the community. Dynamic pricing, enabled by blockchain and Tether (USDT) cryptocurrency, adjusts to real-time market conditions, enhancing transparency and responsiveness in energy trading. This adaptive pricing model fosters a more equitable and efficient trading environment compared to static approaches. Moreover, this system is tailored for community microgrids, emphasizing a community-centric approach. Local prosumers serve as validators in the blockchain network, aligning energy management decisions with community needs and dynamics. This localized engagement promotes efficiency and participation, fostering resilient, sustainable, and user-centric energy landscapes. Through rigorous analysis, we demonstrate the system’s effectiveness in optimizing economic efficiency, reducing operational costs, and increasing compliance rates. By combining blockchain technology with community-focused design principles, the proposed platform represents a significant advancement towards self-sufficiency and resilience in local energy systems.

In grids with high penetration of photovoltaic (PV) systems and electric vehicle (EV) charging points, the phase imbalance can happen mainly due to uneven distributions of PV systems and EV charging points across different phases. This study presents a multi-objective optimization for a three-phase smart grid with 55 single-phase connected nodes to balance the grid, minimize electricity costs, and maximize operational efficiency. The research primarily focuses on individual node optimization, where each node autonomously adjusts its devices, aiming to reduce electricity expenses. It considers integrating community battery storage, operated based on multiple objectives, including phase balance and maximization of self-consumption within the community. Our approach involves a series of scenarios exploring various configurations of PV panel sizes and EV penetration levels. A critical aspect of this investigation is assessing how community battery storage can increase the PV hosting capacity of the grid. This enhancement is required to increase PV integration while ensuring grid stability. The study utilized Pyomo for modelling the optimization problems, coupled with the use of generic solvers. This combination provides a robust and free framework for tackling the complex multi-objective optimization tasks inherent in modern smart grids. The findings of this study can be used as a guideline for utility providers and grid operators, offering insights into optimal battery storage operation, phase balancing strategies, and facilitating higher PV integration.

No abstract available

To address the limitation of traditional microgrid operator-led optimization models that compromise user-side benefits, this paper proposes a novel method for the collaborative optimal operation strategy of community-based integrated energy microgrids and diversified flexible resources. The method deeply integrates user-side flexibility resources into the decision-making process. Unlike existing research that only considers electro-heat coupling, our model integrates shared energy storage services into an integrated energy system, reflecting a more realistic future application. A Stackelberg game framework is then established with the microgrid operator (MGO) as the leader and the user aggregator as the follower. The user aggregator optimizes its energy strategy by coordinating user demand response, thereby increasing the profits of both itself and the shared energy storage operator. Meanwhile, this model guides the MGO’s pricing decisions for electricity and heat, balancing interests of all stakeholders. To solve the model, a hierarchical approach that merges the Harris Hawks Optimization algorithm with the CPLEX solver is employed. Finally, simulation results demonstrate that the proposed model and strategy significantly enhance user-side revenue and flexibility, achieve a win-win outcome for the user aggregator and MGO, and lay the foundation for future shared energy storage service providers to participate in market pricing as key game entities.

The rapid deployment of Electric Vehicles (EVs) and the integration of renewable energy sources have ameliorated the existing power systems and contributed to the development of greener smart communities. However, load balancing problems, security threats, privacy leakage issues, etc., remain unresolved. Many blockchain-based approaches have been used in literature to solve the aforementioned challenges. However, they are not sufficient to obtain satisfactory results because of the inefficient energy management methods and time-intensiveness of the primitive cryptographic executions on the network devices. In this paper, an efficient and secure blockchain-based Energy Trading (ET) model is proposed. It leverages the contract theory, incentive mechanism, and a reputation system for information asymmetry scenario. In order to motivate the ET entities to trade energy locally and EVs to participate in smart energy management, the proposed incentive provisioning mechanism plays a vital role. Besides, a reputation system improves the reliability and efficiency of the system and discourages the blockchain nodes from acting maliciously. A novel consensus algorithm, i.e., Proof of Work based on Reputation (PoWR), is proposed to reduce transaction confirmation latency and block creation time. Moreover, a shortest route algorithm, i.e., the Dijkstra algorithm, is implemented in order to reduce the traveling distance and energy consumption of the EVs during ET. The performance of the proposed model is evaluated using peak to average ratio, social welfare, utility of local aggregator, etc., as performance metrics. Moreover, privacy and security analyses of the system are also presented.

In order to solve the problems of unclear service scope of shared energy storage and redundant allocation of energy storage on the new energy side, an optimal allocation method of shared energy storage considering regional power self-balancing is proposed. Firstly, a shared energy storage area division indicator including modularity indicator and power balance indicator is proposed. Secondly, the shared energy storage area division problem is abstracted into a graph theoretic model, and the community discovery Girvan-Newman algorithm is used to realize the shared area division of the transmission network. And then, a shared energy storage siting and capacity determination model based on the shared area division is set up to realize the optimal allocation of shared energy storage across the whole network. Finally, the effectiveness of the methodology in this paper is verified by comparing and analysing the shared allocation scheme with the source-side fixed-ratio allocation scheme, using the actual power grid of a coastal province in the eastern part of China as an arithmetic example.

Moving from smart homes to smart cities is a complex but essential task to consider. Setting up a modern smart city has many problems, such as unstable power generation systems, a lack of integration of demand-side loads, low profits, more pollution, and agents that cannot communicate quickly and smartly. Our model has the following steps to deal with all of these problems. It performs consumption forecasting through GNN and the Smart Hybrid model (LSTM + GNN) to get optimal forecasting results. The smart hybrid model outperformed the LSTM model by 1.14% in MAPE. The LSTM and Smart Hybrid models have MAPE values of 0.0787 and 0.0776, respectively. This infrastructure uses GNN, Smart Hybrid Model, MOPnP, MOPPnP, and knapsack algorithms to maximize personal comfort, lower costs, profit the community through transaction agents, reduce unwanted peaks by shifting loads, and use an intelligent and robust structure for agent communication. The integration of renewables, optimum consumption forecasts, adjusting consumer comfort, decentralized energy market structure and formulated algorithms have combined to reduce monthly electricity expenses by up to 94%. For the four trade setups that were examined, the unrealized PnL rates were +116%, +78%, +71.631%, and +250%, in that order.

In a smart community infrastructure that consists of multiple smart homes, smart controllers schedule various home appliances to balance energy consumption and reduce electricity bills of customers. In this paper, the impact of the smart home scheduling to the electricity market is analyzed with a new smart-home-aware bi-level market model. In this model, the customers schedule home appliances for bill reduction at the community level, whereas aggregators minimize the energy purchasing expense from utilities at the market level, both of which consider the smart home scheduling impacts. A game-theoretic algorithm is proposed to solve this formulation that handles the bidirectional influence between both levels. Comparing with the electricity market without smart home scheduling, our proposed infrastructure balances the energy load through reducing the peak-to-average ratio by up to 35.9%, whereas the average customer bill is reduced by up to 34.3%.

Community-driven energy initiatives have recently emerged as key enablers in the realization of efficient energy management systems, focusing in particular on trading and management of energy. In this article, we aim at introducing a novel community-based load balancing and prosumers incentivization framework in smart grid systems, based on the theory of hedonic community formation games. Such an approach enables the prosumers to autonomously select the most beneficial partition (community) they should join, in terms of optimizing their achieved payoff, while accounting for both the discounts offered by the provider and the load balancing characteristics of each community. Following such modeling, the existence of a Nash-stable and individually-stable partition is mathematically proven and a distributed hedonic community formation algorithm is designed, that converges to the stable solution. The performance of the proposed approach is achieved via modeling and simulation, and detailed numerical results demonstrate its operational characteristics and its benefits when compared to alternative strategies.

No abstract available

To mitigate the impact of source-load uncertainty on the stable operation of multi-energy systems in energy communities, this paper proposes a two-stage distributionally robust optimization scheduling approach based on Wasserstein distance. The model first incorporates flexible resources, such as distributed energy storage and demand response loads, using historical scenario probability distributions. A min-max-min nested structure is then linearized and reformulated, with the Column and Constraint Generation algorithm employed for efficient solution, ensuring minimal operating costs under the most adverse scenario. Finally, the method’s efficacy is validated through a case study of the Inner Mongolia energy community demonstration project. Results demonstrate that the proposed approach allows for flexible control of conservatism in the optimal solution, balancing economic performance and robustness through adjustable confidence levels and sample sizes, providing an effective strategy for energy community operators.

The national energy sectors are undergoing a significant transformation due to the increased interest in renewable energy. This revolution has a significant impact on electrical distribution networks, which are increasingly challenged by the complexities of balancing demand, managing production fluctuations, and adapting to regulatory updates. Through Load Flow (LF) simulations carried out over a one-year period, this paper investigates the impact of Distributed Generation (DG) on a Low-Voltage (LV) network composed of 25 nodes and connecting 23 end-users, both prosumers and consumers, 10 of which are members of a Renewable Energy Community (REC) located in the north of Italy. Additionally, calculations of the economic incentives recognized to the shared energy (i.e., virtual energy self-consumption) within the REC are carried out, comparing two different methods. The presence of both prosumers and consumers leads to a reduction in the lines’ loading, improved network efficiency and lower active and reactive power losses (up to 27% and 42%, respectively) with respect to a network consisting of only consumers.

This paper presents the development of an energy management system (EMS) for a renewable energy community (REC) with the load-generation balancing objective. In this regard, rule-based and optimization mechanisms are proposed for the REC management in line with the scope of a field trial and considering the scarcity of the measurement and historical data. This typical data scarcity along with the intermittent behaviour of renewable energy resources introduce an unavoidable level of uncertainty— not being adequately addressed in the EMS literature— that might ultimately affect the proper REC management. Hence, a comprehensive performance analysis of the proposed EMS has been conducted via global sensitivity analysis (GSA). Particularly, variance-based sensitivity analysis has been employed to investigate how the variability of a set of selected indicators of the REC performance is apportioned to the different sources of uncertainty specifically related to the forecast and flexibility availability. Results show that the EMS performance is consistent with the EMS objective. The application of GSA reveals though interesting findings that contradict antecedent misconceptions about how different uncertainty sources affect the EMS performance. Although being related to the specific REC under study, the present work specializes GSA method in novel ways that pave the path for its reusability in the context of other EMS applications with different boundary conditions. By highlighting the necessity of GSA and showcasing its suitability to study the EMS performance under an uncertainty framework, the present work offers a precious tool to support system operators in their decision making process.

A Local energy community (LEC) is a vertically nested system in energy supply and an ecosystem with joint values and objectives. On-site integrated hydrogen-based systems connected to the grid, and consisting of electrolyser, hydrogen storage and fuel cell system - hydrogen prosumers - provide efficient balancing of local energy consumption and local production of renewable energy that can be extended over annual cycles. There is no transport of hydrogen needed. Thus, the H2LEC – Local energy community with integrated hydrogen systems - represents the carrier of dispersed energy and hydrogen production as a complement of concentrated energy and hydrogen production: on average, H2LEC will predictably achieve at least 75% self-supply. Additionally, with Combined Heat-and-Power systems, coupling to the thermal system adds to energy efficiency. H2LEC represents a virtual socio-economic system based on community values and thus engages initiative, innovation and capital of local actors, new technology start-ups and local industry; and represents opportunities for new disruptive business models. It brings into the energy supply system new players – prosumer who actively trade their flexibilities among themselves and on the external markets, and stimulates new enabling technologies - notably automated close-to-real time trading - thereby boosting end-to-end automated solutions. The H2LEC create the need and the market for smaller integrated hydrogen-based systems ranging from a few kWe for residential homes to a few MWe units for larger industrial companies or local districts with a complete range of capacities in between, for public or tertiary buildings and smaller enterprises. Thus, they provide an opening for participation of SMEs in local and international value chains and will create a strong complementary energy bottom-up pillar and hydrogen supply system locally and in Europe.

Focusing on remote, isolated, and underserved communities, a multi-energy system is designed in this research which is capable of utilizing different energy sources in a more coordinated and energy-efficient way to support various demands, such as fresh water, electricity, hydrogen, thermal demand, etc. The energy sources considered are renewables (wind, solar, marine) and natural gas. The energy conversion process includes water desalination, gas combustion, water electrolyzation, and different types of storage (hydrogen tank, electricity, thermal, etc.) are designed to serve as buffers in supply-demand balancing. Sets of experiments are designed to demonstrate the effectiveness of the proposed operating model and investigate the impact of uncertainties from renewable generations and demands.

This article proposes an energy trading model basedon blockchain to manage and supervise the trading process. In the model, proof-of-energy reputation generation and proof-of-energy reputation consumption consensus mechanisms are proposed to solve the high computational cost and huge monetary investment issues created by the existing consensus mechanisms. Similarly, a mutual verifiable fairness mechanism based on time commitment is presented, which is introduced to prevent cheating attacks in the model. The proposed model’s performance is assessed using energy cost, peak-to-average ratio, and trust. The simulation results show that the energy cost of the proposed model decreases by 40%. The results for the load balancing depict that the values of peak-to-average ratio of the proposed model with 20% and 50% peak demand reduction are 6.88 and 3.50, which are lower than 9.17 of the benchmark model. Moreover, the proposed model’s results show satisfactory performance for privacy and security of the system.

Having emerged as a new paradigm to re-structure energy systems around citizens, energy communities have quickly gained the attention of researchers worldwide. Nevertheless, a significant challenge is the lack of datasets and tools to support research developments at scale. As such, this paper presents the PRO-social energy Community SIMulator, an open-source project to support the development and evaluation of pro-social energy management schemes in energy communities.

In order to unlock the maximum flexibility potential of all levels in the power system, distribution-network-located flexible energy resources (FERs) should play an important role in providing system-wide ancillary services. Frequency reserves are an example of system-wide ancillary services. In this regard, this article deals with the optimal operation of a local energy community (LEC) located in the distribution network. The LEC is proposed to participate in providing manual frequency restoration reserves (mFRR) or tertiary reserves. In addition, the community is supposed to have a number of electric vehicles (EVs) and a battery energy storage system (BESS) as FERs. The scheduling of the community, which is fully compliant with the existing balancing market structure, comprises two stages. The first stage is performed in day-ahead, in which the energy community management center (ECMC) estimates the amount of available flexible capacities for mFRR provision. In this stage, control parameters are deployed by the ECMC in order to control the offered flexibility of the BESS. In the second stage, the real-time scheduling of the community is performed for each hour, taking into account the assigned and activated amount of reserve power. The target of the real-time stage is to maximize the community’s profit. Finally, the model is implemented utilizing a case study considering different day-ahead control parameters of the BESS. The results demonstrate that the proposed control parameters adopted in the day-ahead stage considerably affect the real-time profitability of the LEC. Moreover, according to the simulation results, participating in the mFRR market can bring additional profits for the LEC.

Energy storage is substantially admitted as an immense potential for distributed energy sources in the smart grid and load balancing. It is an enabling aid to the adaptation of renewable energy resources by small-scale residential users. However, the generated power from these sources is irregular/intermittent in nature. This affects the stability of smart grids. Therefore, energy storage is a new technology for coordinating and managing their fusing. The acceptance of distributed energy storage (DES) is still a challenge, including control effectiveness, installation cost, integration of information communication technology (ICT), and enactment. A Multiagent System (MAS) is a new solution of meeting the demands of distributed computing, communication, and data integration for smart grid applications. This study recommends a new distributed multi-agent-based architecture of storage in the community, i.e., cloud energy storage (CES), providing energy storage service to users at a significantly lower cost. To study autonomous energy management in residential communities, the proposed MAS platform and architecture are designed on the Stateflow platform. The platform provides an environment to model the decision logic and controls. In the suggested platform, Simulink's advantages are combined with Stateflow's decision-making and reactive features.

This paper proposes the network-aware operational strategies to provide (flexibility) services from Local Energy Community (LEC) by maximizing their profit based on the optimized operation of battery energy storage system (BESS), considering the uncertainty between forecasted and actual load net in LEC. The proposed method takes into account the capacity limitation constrain in the contract between LEC and distribution system operator in order to avoid penalties, and provides insightful data to support LEC in making the decision to join the balancing reserve. Three different case studies, which investigate the hypothetical LEC including 01 BESS (100kW/200kWh) and 55 houses with each installed photovoltaic system, are performed using a mixed-integer linear programming to validate the effectiveness of the proposed method. The results show that, by considering the registered limitation, the benefit is increased up to 30%, compared to the case without the capacity limitation constrain.

This study examines integrating a hydrogen-based reversible fuel cell system within a Santa Barbara County, California residential community to alleviate grid congestion caused by excess photovoltaic (PV) energy generation. By converting surplus PV energy into hydrogen for storage and later reconversion to electricity, the community enhances renewable energy utilization and reduces reliance on grid imports. This approach increases the renewable energy consumption of pro-sumers—households who both produce and consume energy, such as those with rooftop PV systems—and consumers and mitigates the strain on the grid from energy exports. The findings demonstrate the economic viability and effectiveness of using hydrogen storage systems to manage surplus renewable energy in communities with high PV penetration.

Renewable Energy Communities (RECs) play a vital role in driving the transition to sustainable energy systems by facilitating inclusive and cost-effective renewable energy production. They empower citizens to actively participate in the energy sector, promote local energy resource sharing, and improve local energy balancing efforts. This study presents a model for investment and operational decision-making within an REC framework, enabling multiple members to invest in renewable energy generation and battery energy storage systems. The model determines optimal capacities for each technology, facilitates energy sharing among members, and evaluates both individual and collective economic benefits through an internal electricity sharing price. By examining various scenarios within an established three-member REC, the research identifies key factors influencing the acceptance of a new member into the community. The findings indicate that the economic advantages of expanding the REC are significantly dependent on the characteristics of the prospective new member.

No abstract available

The main objective of this paper is to review the centralized, decentralized, and hybrid control approaches based on key performance metrics such as efficiency, reliability, and scalability. By improving sustainability, dependability, and efficiency, the combination of artificial intelligence (AI) and the Internet of Things (IoT) in community micro-grids has completely changed energy management. The Internet of Things (IoT) and artificial intelligence (AI) have been used more and more in microgrid control to improve autonomy, dependability, and efficiency. Sensors, smart meters, distributed energy resources (DERs), and energy storage systems are just a few of the microgrid's components that can communicate and monitor in real time thanks to the Internet of Things.AI uses this data to make smart decisions on activities like fault detection, load forecasting, renewable energy prediction, and optimal power dispatch.  To optimize power distribution, load balancing, and fault detection in micro-grids, this article compares several control systems that make use of IoT and AI. The study looks at decentralized, hybrid, and centralized control strategies, emphasizing their benefits, drawbacks, and applicability in various operational scenarios. Important performance indicators are assessed, including cost-effectiveness, responsiveness, energy efficiency, and flexibility about renewable energy sources. The results contribute to the development of smart energy systems by shedding light on the best control schemes for enhancing microgrid performance.

The effective management of shared resources within energy communities poses a significant challenge, particularly when balancing renewable energy generation and fluctuating demand. This paper introduces a novel optimization framework that integrates people flow data, modeled using the Social Force Model (SFM), with energy management strategies to enhance the efficiency and sustainability of energy communities. By combining SFM with the Non-dominated Sorting Genetic Algorithm III (NSGA-III), the framework addresses multi-objective optimization problems, including minimizing energy costs, reducing user waiting times, and maximizing renewable energy utilization. The study employs synthesized data to simulate an energy community with shared facilities such as electric vehicle (EV) charging stations, communal kitchens, and laundry rooms. Results demonstrate the framework’s ability to align energy generation with resource demand, reducing peak loads and improving user satisfaction. The optimization model effectively incorporates real-time behavioral dynamics, showcasing significant improvements in renewable energy utilization–reaching up to 88% for EV charging stations–and cost reductions across various scenarios. This research pioneers the integration of people flow modeling into energy optimization, providing a robust tool for managing the complexities of energy communities.

The integration of renewable energy sources and distributed generation highlights the need to leverage consumer flexibility for grid management. An effective way to incentivize it is to foster their participation in the balancing and flexibility markets, where aggregated consumers' commit to change their consumption in real-time according to their bids before real-time. Therefore, it is relevant to prevent congestions and account for grid losses when forecasting the available flexibility to form the market bid. This paper evaluates the impact of grid conditions on aggregated flexibility from an energy community, analyzing how incentive-driven scenarios influence flexibility under constrained and unconstrained grid conditions. Using grid simulations and data-driven profiles of consumer flexibility, the analysis explores the effects of technical constraints such as line capacity, voltage regulation, and transformer loading. Results highlight how grid conditions can reduce or, in some cases, enhance flexibility by improving voltage profiles, managing reactive power, and redistributing loads. The findings quantify realistic flexibility considering grid constraints and emphasize the role of incentives in extracting said flexibility effectively. This paper aims to guide market participants in optimizing demand response strategies for a resilient power system.

The article presents the developed Mixed Integer Linear Programming (MILP) mathematical model for scheduling and storage capacity estimation for achievement of autonomy (balancing) of an energy community, comprising producers, consumers and prosumers operating connected through the electricity distribution network. If in particular the community contains all the users of the distribution network, it could be regarded as an autonomous (internally balanced) distribution network. Having in mind such a network we describe how the necessary installed production and storage capacities could be estimated on the basis of the characteristic seasonal daily load/production curves of the community and respective daily available energy resource intensities. The model is built using the General Algebraic Modeling System (GAMS). A numerical example is presented and the solution is discussed. Some possible extensions are provided.

The integration of renewable energy sources, particularly solar photovoltaics, into household power supply has become increasingly popular due to its potential to reduce energy costs and environmental impact. However, solar power variability and new regulative changes concerning excess solar energy compensation schemes call for effective energy storage management and sizing to ensure a stable and profitable electricity supply. This paper focuses on optimizing residential battery storage systems under different electricity pricing schemes such as time-of-use tariffs, dynamic pricing, and different excess solar energy compensation schemes. The central question addressed is how different pricing mechanisms and compensation strategies for excess solar energy, as well as varying battery storage investment costs, determine the optimal sizing of battery storage systems. A comprehensive mixed-integer linear programming model is developed to analyze these factors, incorporating various financial and operational parameters. The model is applied to a residential case study in Croatia, examining the impact of monthly net metering/billing, 15 min net billing, and dynamic pricing on optimal battery storage sizing and economic viability.

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With the rapid growth of electric vehicle ownership in China, electric vehicles are demonstrating immense potential as mobile energy storage units. In the process of enabling electric vehicles to supply electricity to the grid, the feed-in tariff becomes a critical foundation for project implementation. Based on an in-depth analysis of the peak-shaving of electric vehicles and user satisfaction, this paper studies the feed-in tariff for vehicle-to-grid (V2G). An optimal pricing objective function is established, incorporating constraints such as electric vehicles' charging and discharging energy limits, battery depth-of-discharge limits, and regional grid load constraints, and user charging and discharging strategies are analyzed. Taking a city's electricity tariff as an example, the charging and discharging strategies of 100 electric vehicles in a residential community were simulated. For different peak-shaving scenarios, the corresponding optimal feed-in tariffs were derived, verifying the effectiveness and applicability of the proposed method.

Focusing on thermal demands including space heating and domestic hot water (DHW), this study develops a hierarchical optimization approach for residential community energy systems (RCES) operating under a multi-energy pricing mechanism. In the upper-level model, the RCES operator utilizes various energy conversion devices to jointly schedule electricity/heat supply and set retail multi-energy prices, aiming to achieve optimal economic returns. Conversely, the lower-level problem explicitly models controllable assets like radiators and electric water heaters, ensuring consumers can satisfy their thermal comfort and water usage needs. Guided by the operator’s price signals, residents act as followers to adjust their load profiles, thereby minimizing expenses and realizing integrated demand response (IDR). To solve this bi-level problem, the lower-level constraints are transformed using Karush-Kuhn-Tucker (KKT) conditions and strong duality theory, converting the original model into a tractable single-level mixed-integer linear programming (MILP) formulation. Case studies on a typical RCES indicate that accurately modeling controllable loads facilitates effective IDR. Furthermore, the framework successfully coordinates flexibility on both the supply and demand sides. The results confirm that this strategy yields a win-win outcome, simultaneously boosting the operator’s profits and reducing the energy expenditures of residential consumers.

The disorderly charging of large-scale electric vehicles (EVs) in residential areas will have a negative impact on peak load, so it is very important to guide the charging behavior of electric vehicles reasonably. Firstly, a pricing mechanism of a residential district agent based on time-of-use (TOU) price is proposed, and the market structure of EV charging and the pricing strategy of the agent are analyzed. Secondly, a multi-objective optimization model is established to minimize the peak-valley difference, maximize the profit of the agent and minimize the charging cost of EV users. The multi-objective model is transformed into a game model between the agent and the users considering the incentives from the power grid company. By using KKT conditions and duality theorem, the game problem is transformed into mixed integer linear programming problem, which is solved by YALMIP. Finally, taking a residential district as an example, electric vehicle users can charge the EVs orderly by responding to the electricity price to provide valley filling load for the grid. Considering the incentive of grid company for the agent, it has a certain effect on the realization of peak load shifting and valley filling, the improvement of agent’s income and the reduction of charging cost of EV users.

Solar prosumers, residential electricity consumers equipped with photovoltaic (PV) systems and battery storage, are transforming electricity markets. Their interactions with the transmission grid under varying tariff designs are not yet fully understood. We explore the influence of different pricing regimes on prosumer investment and dispatch decisions and their subsequent impact on the transmission grid. Using an integrated modeling approach that combines two open-source dispatch, investment and grid models, we simulate prosumage behavior in Germany's electricity market under real-time pricing or time-invariant pricing, as well as under zonal or nodal pricing. Our findings show that zonal pricing favors prosumer investments, while time-invariant pricing rather hinders it. In comparison, regional solar availability emerges as a larger driver for rooftop PV investments. The impact of prosumer strategies on grid congestion remains limited within the scope of our model-setup, in which home batteries cannot be used for energy arbitrage.

In Lebanon, the publicly organized electricity grid has struggled for decades to provide a reliable electricity supply and has nearly collapsed. In response, a network of distributed diesel generators has flourished as an alternative to the unreliable central grid. However, electricity prices within these microgrids remain largely unregulated. This study has two main objectives: first, to estimate the electricity demand elasticity of residential consumers using a unique dataset from a Lebanese microgrid in Deir Kanoun al Naher; second, to analyze how the microgrid owner’s knowledge of this elasticity affects electricity pricing to maximize profits. We apply an ordinary least squares estimator to determine electricity demand elasticity and a game-theory-based optimization model to derive the microgrid owner’s profit-maximizing pricing strategy. The estimated selling price elasticity is <inline-formula><tex-math notation="LaTeX">$-0.48$</tex-math></inline-formula>, indicating relatively inelastic demand, in line with values reported for developing countries. Considering the estimated elasticity in the pricing strategy resulting in an increase of <inline-formula><tex-math notation="LaTeX">$54 \%$</tex-math></inline-formula>, revenues increase by up to <inline-formula><tex-math notation="LaTeX">$15 \%$</tex-math></inline-formula>, while the share of suppressed demand reaches approximately <inline-formula><tex-math notation="LaTeX">$25 \%$</tex-math></inline-formula>. Sensitivity analysis suggests that higher price elasticity mitigates excessive pricing strategies. The role of the public grid and solar PV penetration presents a promising direction for future research.

This study presents an optimization method for scheduling electric vehicle (EV) charging in residential areas, aimed at minimizing costs associated with peak demand periods. As the adoption of EVs increases, effective management of their charging demands becomes crucial for both utilities and EV owners. This research develops a mathematical model for a combined EV aggregator that coordinates charging and discharging activities among residential, commercial, and industrial users. Using a Multi-Agent Charging and Discharging (MACD) algorithm, the study shifts EV charging from peak to off-peak hours, leveraging time-of-use (TOU) pricing to reduce overall electricity costs. Case studies demonstrate that coordinated charging can decrease costs by up to 15% compared to uncoordinated methods, highlighting the algorithm's efficiency in managing energy demand and enhancing grid stability. The findings underscore the potential for optimized EV scheduling to contribute significantly to smart grid operations and suggest avenues for future research into the algorithm's broader impacts on network performance.

Peer-to-peer (P2P) energy trading is becoming central to modern distribution systems as rooftop PV and home energy management systems become pervasive, yet most existing market and reinforcement learning designs emphasize efficiency or private profit and offer little real-time guidance to ensure equitable outcomes under uncertainty. To address this gap, a fairness-aware multiagent reinforcement learning framework, FairMarket-RL, is proposed in which a large language model (LLM) critic shapes bidding policies within a continuous double auction under partial observability and discrete price-quantity actions. After each trading slot, the LLM returns normalized fairness scores Fairness-to-Grid (FTG), Fairness-Between-Sellers (FBS), and Fairness-of-Pricing (FPP) that are integrated into the reward via ramped coefficients and tunable scaling, so that fairness guidance complements, rather than overwhelms, economic incentives. The environment models realistic residential load and PV profiles and enforce hard constraints on prices, physical feasibility, and policy-update stability. Across a progression of experiments from a small pilot to a larger simulated community and a mixed-asset real-world dataset, the framework shifts exchanges toward local P2P trades, lowers consumer costs relative to grid-only procurement, sustains strong fairness across participants, and preserves utility viability. Sensitivity analyses over solar availability and aggregate demand further indicate robust performance, suggesting a scalable, LLM-guided pathway to decentralized electricity markets that are economically efficient, socially equitable, and technically sound.