电力交易在社区的应用分析

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.

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Local energy communities establish a platform for prosumers and consumers to share surplus generation via peer-to-peer (P2P) energy trading. Traditional optimization techniques for energy management problems necessitate precise models and accurate predictions. In contrast, reinforcement learning has gained widespread attention as it can handle system uncertainties without relying on models. However, multi-agent reinforcement learning (MARL) approaches for P2P energy trading involve a large volume of communications and computations, hindering the practical deployment of decision-making algorithms on edge devices whose communication and computation resources are very limited. To this end, this paper proposes an efficient and privacy-preserving MARL approach for edge computing-enabled P2P energy trading. Specifically, we design an information interaction framework that shares representations rather than sensitive observations to protect the privacy of various agents. In addition, we develop an event-triggered communication approach that controls the frequency of interactions through a gating mechanism to reduce communication overhead among agents. To save the memory footprint of each agent, we investigate an evolutionary training method that updates the networks using perturbation without backward gradient computation. Case studies on a real-world dataset demonstrate that our proposed method yields significant efficiency improvements while maintaining high decision-making performance.

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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 Internet of Energy (IoE) is a topic that industry and academics find intriguing and promising, since it can aid in developing technology for smart cities. This study suggests an innovative energy system with peer-to-peer trading and more sophisticated residential energy storage system management. It proposes a smart residential community strategy that includes household customers and nearby energy storage installations. Without constructing new energy-producing facilities, users can consume affordable renewable energy by exchanging energy with the community energy pool. The community energy pool can purchase any excess energy from consumers and renewable energy sources and sell it for a price higher than the feed-in tariff but lower than the going rate. The energy pricing of the power pool is based on a real-time link between supply and demand to stimulate local energy trade. Under this pricing structure, the cost of electricity may vary depending on the retail price, the number of consumers, and the amount of renewable energy. This maximizes the advantages for customers and the utilization of renewable energy. A Markov decision process (MDP) depicts the recommended power to maximize consumer advantages, increase renewable energy utilization, and provide the optimum option for the energy trading process. The reinforcement learning technique determined the best option in the renewable energy MDP and the energy exchange process. The fuzzy inference system, which takes into account infinite opportunities for the energy exchange process, enables Q-learning to be used in continuous state space problems (fuzzy Q-learning). The analysis of the suggested demand-side management system is successful. The efficacy of the advanced demand-side management system is assessed quantitatively by comparing the cost of power before and after the deployment of the proposed energy management system.

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The rapid growth of distributed solar energy, such as rooftop photovoltaics (PVs), has revolutionized conventional power systems into more distributed networks, enabling end-users to engage in and trade within the energy market. Maximizing the benefits of rooftop PV panels for residential end-users, including increased renewable energy use and reduced reliance on the utility grid, remains an essential challenge in conventional centralized markets. Moreover, reducing energy consumption may lead to increased peak demand, decreased self-consumption, reduced system flexibility, and reduced grid stability. Therefore, this study presents a transactive energy market framework that integrates home energy management systems (HEMSs) with multi-objective optimization and an aggregator-based, distributed peer-to-peer (P2P) trading strategy to increase rooftop PV utilization and reduce grid dependency within an intra-residential community. The HEMS is structured to integrate rooftop PV production, battery energy storage systems, and smart appliances to offer flexibility through demand response programs in balancing supply and demand by scheduling appliances during periods of rooftop PV production and lower grid prices. Multi-objective (i.e., minimizing energy consumption cost and peak load) optimization problems are solved using the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) by achieving a Pareto-optimal solution. To validate the reliability and optimality of the NSGA-II results, the same problem formulation is solved using a mixed-integer linear programming approach. Moreover, a Strategic Double Auction with Dynamic Pricing (SDA-DP) strategy is proposed to support P2P trading among consumers and prosumers and thereafter compared with a rule-based zero-intelligence strategy with market-matching rules to analyze the trading performance of the proposed SDA-DP. The results of this comparative analysis (for 10 households, year-long simulation with 15 min time resolution) demonstrate that compared to the baseline case, integrating NSGA-II optimization with SDA-DP trading significantly enhances rooftop PV utilization by 35.11%, reduces grid dependency by 34.04%, and reduces electricity consumption costs by 30.53%, with savings of €1.93 to €6.67 for a single day after participating in the proposed P2P market.

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Peer-to-peer energy trading enhances intracommunity energy utilization and economic benefits in prosumer microgrids. This paper optimizes the operation of a microgrid community with profit-driven community hybrid energy storage using the Nash bargaining framework and dynamic bidding price-based peer-to-peer energy transactions. The decentralized, privacy-preserving optimization is validated on a microgrid community having three microgrids and a community hybrid energy storage system. Results show that dynamic bidding increases social welfare by $\sim 18.16 \%$ and self-consumption by $\sim 27.31 \%$ compared to the absence of peer-to-peer trading.

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.

In many areas, microgrids and other decentralized systems face challenges like inefficient energy transport, pricey transactions, and a lack of openness in peer energy trading. Typical centralized models can block efficient energy trades and are at risk of fraud and manipulation. In community-based microgrids, where both creating and using energy are local, these problems are widespread, so a dependable, safe, and affordable system is essential. The proposed framework uses blockchain, smart contracts, and DApps to ensure community microgrids have secure and streamlined P2P energy trading. Because of blockchain, both the safety and the process of energy transactions are maintained. The present system supports matching energy supply with demand, flexible pricing, and computerized energy transactions from producers to users. We experiment by running the framework in different ways, calculating how efficiently it trades, how much it cuts costs, and how quickly transactions complete. Compared to centralized systems, we found that energy trading now takes less time, has lower transaction fees, and is safer. In addition, the system’s capability to handle a lot of energy and adjust prices suggests it could benefit from being in large community microgrids. Blockchain makes it easier for P2P energy trading systems to be secure, efficient, and transparent for everyone, supporting the development and durability of decentralized energy globally.

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Microgrids have been developing nowadays as an initiative aiming to operate modern power distribution systems more reliably and efficiently. With the decreasing price of battery energy storage systems (BESSs), BESSs are highly recommended to be exploited in the operation of microgrids in the distribution network system. The advancements in BESS, increasing trends of distributed generation, and proliferation of the prosumer community require effective energy utilization in the microgrid paradigm. The Peer-to-Peer (P2P) energy trading mechanism establishes a marketplace where prosumers can engage in energy transactions, reduce energy consumption costs and increase resilience. However, it leads to a complicated problem particularly when multi-energy systems participate the energy trading. In previous studies, P2P trading models have been primarily considered with only prosumers possessing renewable energy resources. However, it is believed that dynamic components such as BESS and the Distribution System Operator (DSO) will continue to have a significant role in the Local Energy Market (LEM). This paper therefore proposes a trading model in a prosumers-based P2P in the LEM that includes several local energy providers and pure energy consumers, in addition to a market community coordinator which is namely a P2P market manager (P2PM). In the proposed market model, each prosumer is considered a market participant, who tends to negotiate with each other in a way that follows their benefits. Each prosumer has a different distributed generator photovoltaic (PV), wind turbine, and storage unit to satisfy its load demand. To handle the market clearing and energy balance problem, the P2PM is responsible for the implementation of functions for the centralized problem of the P2P model. The results of two different cases for the proposed LEM scheme are compared to verify the effectiveness of the solution.

With the rapidly growing energy consumption and the rising number of prosumers, next-generation energy management systems are facing significant impacts by peer-to-peer (P2P) energy trading, which will enable prosumers to sell and purchase energy locally. Until now, the large-scale deployment of P2P energy trading has still posed many technical challenges for both physical and virtual layers. Although the communication infrastructure represents the cornerstone to enabling real-time monitoring and control, less attention has been given to the performance of different communication technologies to support P2P implementations. This work investigates the scalability and performance of the communication infrastructure that supports P2P energy trading on a community microgrid. Five levels make up the developed P2P architecture: the power grid, communication network, cloud management, blockchain, and application. Based on the IEC 61850 standard, we developed a communication network model for a smart consumer that comprised renewable energy sources and energy storage devices. Two different scenarios were investigated: a home area network for a smart prosumer and a neighborhood area network for a community-based P2P architecture. Through simulations, the suggested network models were assessed for their channel bandwidth and end-to-end latency utilizing different communication technologies.

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Peer-to-peer (P2P) energy trading has emerged as a novel approach to enhancing the coordination and utilization of distributed energy resources (DERs) within modern power distribution networks. This study presents a techno-economic analysis of different DER characteristics, focusing on the integration of photovoltaic (PV) systems and energy storage systems (ESS) within a community-based P2P energy trading framework in Aswan, Egypt, under a time-of-use (ToU) electricity tariff. Eight distinct cases are evaluated to assess the impact of different DER characteristics on P2P energy trading performance and an unbalanced low-voltage (LV) distribution network by varying the PV capacity, ESS capacity, and ESS charging power. To the best of the authors’ knowledge, this is the first study to comprehensively examine the effects of different DER characteristics on P2P energy trading and the associated impacts on an unbalanced distribution network. The findings demonstrate that integrating PV and ESS can substantially reduce operational costs—by 37.19% to 68.22% across the analyzed cases—while enabling more effective energy exchanges among peers and with the distribution system operator (DSO). Moreover, DER integration reduced grid energy imports by 30.09% to 63.21% and improved self-sufficiency, with 30.10% to 63.21% of energy demand covered by community DERs. However, the analysis also reveals that specific DER characteristics—particularly those with low PV capacity (1.5 kWp) and high ESS charging rates (e.g., ESS 13.5 kWh with 2.5 kW inverter)—can significantly increase transformer and line loading, reaching up to 19.90% and 58.91%, respectively, in Case 2. These setups also lead to voltage quality issues, such as increased voltage unbalance factors (VUFs), peaking at 1.261%, and notable phase voltage deviations, with the minimum Vb dropping to 0.972 pu and maximum Vb reaching 1.083 pu. These findings highlight the importance of optimal DER sizing and characteristics to balance economic benefits with technical constraints in P2P energy trading frameworks.

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.

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.

With the massive access to distributed energy resources, an increasing number of users have transformed into prosumers with the functions of producing, storing, and consuming electric energy. Peer-to-peer (P2P) energy trading, as a new way to allow direct energy transactions between prosumers, is becoming increasingly widespread. How to determine the trading strategy of prosumers participating in P2P energy trading while the strategy can satisfy multiple optimization objectives simultaneously is a crucial problem to be solved. To this end, this paper introduces the demand response mechanism and applies the dissatisfaction function to represent the electricity consumption of prosumers. The mid-market rate price is adopted to attract more prosumers to participate in P2P energy trading. The P2P energy trading process among multiple prosumers in the community is constructed as a Markov decision process. We design the method of deep reinforcement learning (DRL) to solve the optimal trading policy of prosumers. DRL, by engaging in continual interactions with the environment, autonomously learns the optimal strategies. Additionally, the deep deterministic policy gradient algorithm is well-suited for handling the continuous and intricate decision problems that arise in the P2P energy trading market. Through the judicious construction of a reinforcement learning environment, this paper achieves multi-objective collaborative optimization. Simulation results show that our proposed algorithm and model reduce costs by 16.5%, compared to the transaction between prosumers and grid, and can effectively decrease the dependence of prosumers on the main grid.

In smart grids, the large-scale integration of distributed renewable energy resources has enabled the provisioning of alternative sources of supply. Peer-to-peer (P2P) energy trading among local households is becoming an emerging technique that benefits both energy prosumers and operators. Since conventional energy supply is still needed to help fill the gap between local demand and supply when the local solar generation is not sufficient, demand–response management will keep playing an important role in the future P2P energy market. Blockchain and smart contract technology has gained increasing attention in P2P trading for its secure operation. The performance of blockchain-based P2P energy trading still remains to be improved, in terms of latency and cost of computation resources. This article studies the challenges of demand–response management in P2P energy trading and proposes a blockchain-empowered energy trading system for a community-based P2P market. The proposed demand–response mechanism is developed using two noncooperative games, in which dynamic pricing is applied for suppliers. The proposed energy trading system is prototyped on a cluster network, with a coordinator running as a smart contract in a Hyperledger blockchain. We implemented both on-chain and off-chain processing modes to study the system performance. The results from experiments with our prototype indicate that our proposed demand–response games have a great effect on reducing the net peak load, and at the same time, the off-chain processing mode provides lower latency and overhead compared to the on-chain mode while still keeping the same system integrity as the on-chain mode.

The integration of renewable energy resources in rural areas, such as dairy farming communities, enables decentralized energy management through Peer-to-Peer (P2P) energy trading. This research highlights the role of P2P trading in efficient energy distribution and its synergy with advanced optimization techniques. While traditional rule-based methods perform well under stable conditions, they struggle in dynamic environments. To address this, Multi-Agent Reinforcement Learning (MARL), specifically Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), is combined with community/distributed P2P trading mechanisms. By incorporating auction-based market clearing, a price advisor agent, and load and battery management, the approach achieves significant improvements. Results show that, compared to baseline models, DQN reduces electricity costs by 14.2% in Ireland and 5.16% in Finland, while increasing electricity revenue by 7.24% and 12.73%, respectively. PPO achieves the lowest peak hour demand, reducing it by 55.5% in Ireland, while DQN reduces peak hour demand by 50.0% in Ireland and 27.02% in Finland. These improvements are attributed to both MARL algorithms and P2P energy trading, which together results in electricity cost and peak hour demand reduction, and increase electricity selling revenue. This study highlights the complementary strengths of DQN, PPO, and P2P trading in achieving efficient, adaptable, and sustainable energy management in rural communities.

Peer-to-peer (P2P) energy trading requires prosumers to determine bidding prices, yet existing approaches rely on either static rules or computationally intensive algorithms. This paper compares two contrasting pricing strategies: status-aware pricing (SAP) adjusts bids based on internal resource status, while market-responsive pricing (MRP) adapts to observed clearing prices through adaptive learning. Using an efficiency-equity framework, this study evaluates both strategies against a fixed-margin pricing (FMP) baseline through simulations on a multi-microgrid system. MRP achieves 15 % higher transaction volume and 12 % greater system profit than SAP, while SAP exhibits 33 % lower profit variance compared to MRP. Both strategies improve upon the baseline, with SAP and MRP representing distinct Pareto-optimal points trading off efficiency and equity. Community-oriented markets should favor SAP's implicit equity mechanism, while profit-driven markets may prefer MRP's efficiency gains. These findings inform the design of P2P pricing mechanisms that balance economic performance with fair benefit distribution.

Recent years have witnessed a significant transformation in the sources of energy and its distribution among consumers and prosumers. Renewable energy sources (RES) such as photovoltaic have become particularly popular. However, RES requires a new approach to manage energy demand and supply by leveraging distributed energy generation. In this context, a useful paradigm is a peer-to-peer (P2P) energy marketplace, which is a trading mechanism that actively involves consumers and prosumers in trading electricity using state-of-the-art information and communication technologies. Accordingly, we focus on optimizing total energy trading costs by using quantum reinforcement learning (QRL) to ensure that local generation is first consumed by energy consumers within a local community. This is followed by a proposal of blockchain-based energy trading systems to facilitate decentralized, secured, and privacy-protected trading platforms for the trading participants. To this end, we use three optimization techniques - linear optimization techniques, reinforcement learning, and QRL - and analyze their performance. Simulation results show that the agents learning using quantum computing converge faster than those of the other two optimization techniques.

Peer-to-peer (P2P) energy trading presents a promising way for consumers to share excess energy and enhance community-driven energy systems. This paper introduces an elastic energy block (EB)-based P2P trading model with an automatic switch Internet of Things (IoT) terminal to address the flexibility of user-initiated transactions and ensure consistency between online transactions and offline dispatching. The EB model enhances user flexibility by allowing dynamic adjustments of transaction parameters through a rolling aggregation process. It also features an optimized power loss allocation mechanism that accurately reflects network conditions, improving trade alignment with actual energy flows. Additionally, the designed automatic switch IoT terminal facilitates seamless bidirectional data exchange between online transactions and offline energy dispatch, ensuring precise control over energy transmission and addressing issues such as latency and inconsistencies in smart meter readings. The case study on an IEEE 15-bus system shows that the EB model effectively increases user transaction volumes, boosting total social welfare by over 8%. The effectiveness and accuracy of the systems are also validated.

Peer-to-peer (P2P) energy trading is gaining momentum as a decentralized alternative to conventional energy systems. Blockchain offers transparency integrity and trust for such markets. However executing blockchain logic on embedded platforms such as Raspberry Pi (RPi) raises concerns about gas cost communication overhead and long-term storage. Despite growing interest in blockchain-based energy trading there is limited performance evaluation on resource-constrained devices. We present a comprehensive analysis of two double auction (DA) implementations using full Quorum nodes running on RPi devices. One performs sorting and order matching fully on-chain while the other shifts these operations off-chain with verifiable results. We analyze gas cost, communication overhead and blockchain growth across different community sizes. Our results show that off-chain implementation offers improved gas efficiency as the number of participants increases. On-chain execution also remains a viable alternative especially for smaller and moderate communities. Both implementations maintain manageable communication and storage requirements under realistic trading activity. These findings demonstrate that full blockchain nodes can operate effectively on RPi devices, enabling the practical and scalable deployment of P2P energy trading systems.

Peer-to-peer (P2P) energy trading frameworks are mostly implemented for decentralized energy trading among participants in a residential community. While economically effective, these platforms are susceptible to cyber-physical threats such as false data injection attacks (FDIAs). This paper proposes a novel class of zero-sum FDIAs especially designed to disrupt supply-demand ratio-based P2P energy trading markets without modifying the daily net energy consumption of individual prosumers. The objective of these attacks is to economically alter the local electricity market, decreasing the profitability of legitimate energy trading and undermining trust in the system. By injecting tactically crafted false demand data during the P2P trading period, the attacker exploits the supply-demand balance within the community, thereby impacting internal selling and buying prices to shift market outcomes. FDIAs are simulated at various attack percentages of targeted prosumers, analyzing both operational and economic effects at both the community and prosumer levels. Results indicate that even low-intensity FDIAs affecting a large portion of prosumers can significantly degrade prosumer utility and alter market prices while remaining stealthy. The results highlight the vulnerability of P2P energy trading frameworks to undetectable injections, reinforcing the need for robust anomaly detection and attack mitigation techniques in P2P energy trading systems.

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.

Peer-to-peer (P2P) energy trading has emerged as an innovative approach for selling electricity from prosumer to consumer at the distribution level. This paper is the first to conduct a techno-economic assessment of P2P energy trading in Aswan, Egypt. Different scenarios under different electricity tariffs, which consider photovoltaic systems, energy storage systems, and electric vehicles deployment, are analyzed to assess the performance of P2P trading considering different distributed energy resources (DERs) installations. The variety of these scenarios enables a thorough analysis of P2P trading and a clear comprehension of how P2P trading impacts distribution networks. The study offers new perspectives on the impacts of implementing P2P trading on the distribution network since it uses a real demand profiles. Results show that P2P energy trading can reduce community electricity costs, improve self-consumption by reducing exports to distribution system operator, and rise self-sufficiency compared to home energy management system (HEMS). The distribution network operation limits are not violated in any of the studied scenarios and electricity tariffs. The impacts on the distribution network for P2P trading scenarios and equivalent HEMS are very similar for flat tariff. However, for time of use tariff, P2P trading scenarios with flexible devices result in higher impacts on the distribution network than the equivalent HEMS.

This work presents a novel two-layer distributed control system for energy management in an energy community, integrating stochastic and distributed model predictive control with a fault-tolerant mechanism. Unlike conventional approaches, the proposed method allows agents to autonomously detect, isolate, and adapt to faults in a fully distributed manner, without relying on a central coordinator. This is achieved through a smart contract deployed on a blockchain network, which facilitates decentralized coordination and fault management. The lower layer implements a fault-tolerant mechanism that detects, isolates, and adapts the control parameters of affected agents. It minimizes false positives through residual signal analysis and dynamically adjusts operational constraints to maintain system stability and performance. When multiple agents are affected, fault-related information is exchanged among them, enabling a coordinated response. This allows proactive actions such as disabling power lines linked to faulty agents or adjusting power transactions in response to reduced output capacity from a neighboring agent. The upper layer employs chance-constrained model predictive control to handle uncertainties in energy demand and renewable generation, ensuring robust decision-making under variability. Energy scheduling is managed through blockchain-based smart contracts, providing a secure and decentralized coordination framework. The effectiveness of the proposed scheme is validated through simulations, demonstrating its ability to optimize energy distribution while enhancing fault resilience in energy communities.

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.

The rapid increase in integration of Electric Vehicles (EVs) and Renewable Energy Sources (RESs) at the consumption level poses many challenges for network operators. Recently, Peer-to-Peer (P2P) energy trading has been considered as an effective approach for managing RESs, EVs, and providing market solutions. This paper investigates the effect of EVs and shiftable loads on P2P energy trading with enhanced Vehicle to Home (V2H) mode, and proposes an optimized Energy Management Systems aimed to reduce the net energy exchange with the grid. Mixed-integer linear programming (MILP) is used to find optimal energy scheduling for smart houses in a community. Results show that the V2H mode reduces the overall energy costs of each prosumer by up to 23% compared to operating without V2H mode (i.e., EVs act as a load only). It also reduces the overall energy costs of the community by 15% compared to the houses operating without the V2H mode. Moreover, it reduces the absolute net energy exchanged between the community and the grid by 3%, which enhances the energy independence of the community.

The growing integration of distributed energy resources and advancements in communication and information technology have necessitated the development of smart grids with advanced demand response capabilities. This paper proposes a hierarchical reinforcement learning framework integrating intelligent home energy management and a peer-to-peer energy trading community. The low-level policy optimizes home energy management by leveraging a fuzzy actor-critic reinforcement learning algorithm with a centralized-training-decentralized-execution structure. It regulates energy consumption based on Time-of-Use tariffs while considering user dissatisfaction levels. Then, the low-level policy communicates surplus or deficit energy to the high-level policy. The high-level policy employs the fuzzy actor-critic reinforcement learning algorithm under the decentralized-training-decentralized-execution structure, with value decomposition networks to generate a privacy-preserving cooperative-competitive strategy for pricing in a dynamic continuous double auction market. While optimizing individual agent benefits, the high-level policy also fosters cooperation to enhance energy trade within the community. The simulations using the real-world data demonstrate the effectiveness of the low-level policy in managing the energy consumption and determining the required/excessive energy in the market. The comparisons with the purely cooperative and purely competitive markets represent the superiority of the proposed approach in terms of increased load transactions, buyers’ cost savings, and sellers’ revenue.

This paper studies the real-time energy trading problem in a smart community consisting of a group of grid-connected prosumers with controllable loads, renewable generations and energy storage systems. We propose a peer-to-peer (P2P) energy trading system, which integrates energy trading with energy management, enabling each prosumer to jointly manage its energy consumption, storage scheduling and energy trading in a dynamic manner for smart communities consisting of a group of grid-connected prosumers with controllable loads, renewable generations and energy storage systems. The proposed community-based P2P energy trading system combines an online energy control and trading algorithm with a double auction mechanism. The energy control and trading algorithm is designed based on the Lyapunov theory, allowing each prosumer to independently determine its bid in each time slot only based on its current energy supply condition, while the trading price, which is determined via the double auction mechanism, reflects the collective energy supply conditions of all prosumers participating in energy trading. The integration of the Lyapunov-based energy control and trading algorithm and the double auction mechanism yields a dynamic energy trading pricing mechanism that induces the prosumers to participate in energy trading in a coordinated manner by influencing the energy consumption, energy charging/discharging and energy trading decisions of the prosumers. Numerical simulation results demonstrate that energy exchange in the proposed scalable energy trading system yields significant improvements in terms of energy cost savings and renewable energy utilization efficiency, while ensuring the fair sharing of the benefits reaped from energy trading among the prosumers.

Developing countries are faced with constant power shortages, which hinders economic growth. The use of fossil fuels has also contributed to this challenge, but now governments have been trying to encourage people to adopt renewable energy at an individual level. This is seen as a long-term solution to the current power challenges. The adoption and use of renewable energy, however, has its own challenges, such as intermittent availability and the high start-up costs. This paper proposes a communitybased virtual power plant (CVPP) with a peer-to-peer energy trading platform as a solution to some of these challenges. CVPPs are decentralized energy systems within a small geographic area that connect small-scale energy producers. The resources of these producers are then pooled together to create a single energy ecosystem. The addition of peer-to-peer energy trading to this setup adds an economic incentive to the members of the CVPP as it allows them to sell excess energy to their neighbours. Blockchain technology acts as a medium of trust in the absence of a trusted third-party. A small-scale prototype for this system was built and tested. Various technologies were used for this prototype such as smart contracts and smart meters.

With the increase in the use of renewable energy sources (RESs) and advances in technologies, peer-to-peer (P2P) energy trading is emerging as a promising approach to the local energy market. This work proposes a virtual community (VC) based P2P energy trading for buildings equipped with RES and flexible loads. A modified Nash bargaining solution is used for fair incentive distribution in the cooperative game. In this work, the equilibrium strategies of all players are obtained using a privacy-preserving decentralised approach. Shareable battery energy storage is considered in each VC, and Hong's 2m point estimation method is used to cope with the uncertainties associated with RESs.

A local energy community is a network of energy consumers and producers who engage in peer-to-peer (P2P) energy trading, sharing locally generated power among members to optimize resource utilization and enhance grid resilience. In this paper, an operational framework focusing on such a community integrated with renewable energy sources, specifically integrating electric vehicles (EVs) within charging stations equipped with solar generation is proposed. The proposed framework leverages dynamic pricing mechanisms for EVs based on the stored energy cost, boosting owners' profits, and fostering the participation of charging stations in P2P markets. Our study, conducted across three scenarios, demonstrates significant reductions in energy costs and grid dependency, with savings of about 31% in Case 2 and 55% in Case 3 compared to conventional energy supply models. These results highlight the effectiveness of the integrated P2P trading system in enhancing economic and environmental sustainability within energy communities.

No abstract available

The growth of renewable energy sources usage at the local level contributes to decentralizing the power and energy systems. Nowadays, there is an increment of residential consumers becoming prosumers able to consume their generation or sell it to the public grid to reduce the electricity bill. This great penetration of electricity compromises the proper functioning of the system. Local electricity markets (LEM) are market platforms aimed at electricity end-users to be able to negotiate and transact it between them, thus becoming active players in the system, being a possible solution to balance local systems. Different approaches for LEM design and implementation are proposed in the literature, usually based on community markets and peer-to-peer. Despite their value, these solutions’ scalability is compromised as these are centralized solutions, and processing can become very heavy. In this sense, this work proposes a blockchain-based distributed and decentralized optimal solution for implementing LEM.

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.

Local electricity markets offer new trading opportunities for existing and emerging actors in the energy sector. In this study, a mathematical model is created for the hourly scheduling of a day-ahead local electricity market. The local electricity market has local producers and consumers and a connection with a retailer. The local resources considered are solar PV production, and an energy storage system. Therefore, the local electricity market has local resources, the inflexible and flexible residential loads, and the connection to the distribution network. This connection is used for buying energy from the wholesale market through a retailer. For evaluating the feasibility of the scheduling in a local energy market, an optimization model is proposed in this study to maximize the operational profits from the local electricity market, which is managed by an energy service company (ESCO). There are revenues from selling the energy to all residential loads, while the costs come from buying the electricity and managing all the local resources. A case study illustrates the energy profiles of all the resources managed, which have an impact on revenues and costs of the local market. From the case study, we draw some practical conclusions about the impact of local resources on the local electricity market.

The local electricity market is a new mechanism to engage the participation of end-users as well as to ensure greater transparency of electricity markets. In order to simulate the workings of a local market, this paper introduces two mathematical models, geared at: i) minimizing expected costs or ii) maximizing expected profits. This model produces the quarterly energy profiles of local players for a whole day, i.e., 96 periods. Four types of players are considered in the local electricity market: PV producers, residential loads, flexible residential loads, and the Local System Operator (LSO). The two models provide energy profiles for these four players, which may differ depending on whether the LSO minimizes costs or maximizes profits of managing local resources. A case study addresses the analysis of the resulting profiles for residential loads, flexible residential loads, and local PV producers. The work concludes with a short discussion of the future role of the LSO and the implications of its operating philosophy on consumption levels, costs and revenues for local prosumers.

The energy transition from a formerly centralized, fossil-fuel based system towards a sustainable system based on a large share of renewable generation calls for a decentralization and regionalization of the electricity system. Local electricity markets (LEMs), on which prosumers and consumers can trade locally produced electricity, meet these requirements and simultaneously enable formerly excluded residential customers to actively take part in the electricity market. However, trading can be complex and time intensive. Therefore, it should be automated. We provide an analysis of intelligent learning strategies for agents of residential electricity customers in LEMs. To this end, we conduct a multi-agent-based simulation of a LEM with a merit order market design based on the current German electricity spot market. LEM agents maximize their individual utility via reinforcement learning. We expand existing approaches of reinforcement learning with generation and storage states as well as time-dependent learning. The evaluation of the strategies is based on the agents' and community electricity storage's revenues, costs, and consumption of local electricity. The results show that for fixed sell prices, time-dependent reinforcement learning of buy bids is the best strategy. It facilitates a market self-consumption of 54 %. For learning buy and sell prices, traditional reinforcement learning with generation states is the dominant strategy.

This paper presents a study on a resource trading process aimed at assisting the grid operator (GO) in managing system imbalances during the intra-day market. The research incorporates two types of incentives, taking into account the perspectives of service providers (SPs) and the GO. The in-teractions among the entities involved are modelled using the Stackelberg game, while Stackelberg equilibrium (SE) is achieved through a distributed algorithm utilizing local information only. The numerical analysis indicates that demand response (DR) programs play a crucial role in enabling the GO to operate the power system reliably and economically. Moreover, a comparison between the two case studies reveals that implementing shorter timeframes for DR programs can yield enhanced demand reduction by residential consumers (RCs), resulting in potential cost savings and increased revenue for service providers facilitated by effective incentives. These results highlight the value of DR programs as a valuable tool for the GO in optimizing power system operations.

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.

: Increased interest is demonstrated recently in the emergence of prosumer schemes for the residential sector on the basis of combined RES and storage configurations. Primarily, such schemes aim to increase energy autonomy for end users. Despite providing an alternative supply solution that may secure end users from volatile energy prices, RES–battery configurations also suggest costly and, in most cases, capital-intensive solutions. As such, exploring the generation of additional revenue through market participation is an exercise worth undertaking, noting at the same time that decongestion management services may also be provided to the local grid. In this context, the current study introduces an operational framework for the market participation of RES–battery prosumer schemes, seeking to determine the optimal balance between self-consumption and market integration. For that purpose, we use typical demand patterns and perform an extensive parametrical analysis concerning system size, spot price levels and degree of market integration in the context of the Greek electricity market, with our results indicating areas of optimum balance for the minimization of similar schemes’ levelized cost of electricity.

Currently, blockchain technology is a widely discussed hype in the energy community. It may have the potential to revolutionize the energy system and support the energy transition towards distributed renewable generation. In particular, local electricity markets (LEMs) seem to provide applications for the usage of blockchain technology. Through its innovative design as a distributed and decentralized information system, blockchain supporters see its potential in organizing residential households and prosumers in LEMs. We assess the current maturity of blockchain-based applications for LEMs. To this end, we develop a blockchain maturity model that constitutes a framework for analyzing the current and future maturity of blockchain-based LEMs in a comprehensive structured approach. In a second step, we apply the new blockchain maturity model to an use case of a blockchain-based LEM. Our assessment shows that, in the current status, the project is in an early stage of maturity due to missing regulatory rules and standardization.

Local electricity markets (LEMs) are investigated as a solution to provide the residential and small commercial consumers, and prosumers the opportunity to have control over their electricity-related choices and make more profit from the electricity trading. This work analysis the market design factors such as update intervals in a time step, production to consumption (PtC) ratio and pricing scenarios, influencing the performance of an LEM run on the Decentralized Autonomous Area Agent (D3A) simulation framework. Comparing the results using performance indicators such as self sufficiency, share of market savings (SMS), and average buying rate (ABR) reveals that the performance of LEMs is highly dependent on the market design factors. The level of savings or profits made by participants also changes significantly with these market design factors. Furthermore, the results imply that LEM can provide better incentives for prosumers by providing them with the opportunity to trade their PV generated electricity at a price higher than the feed-in tariff. With only 20% reduction in average buying rate, it is also evident that LEMs provide a great opportunity for keeping the small scale PV systems active after their 20 years of fixed remuneration under Renewable Energy Source Act (EEG) in Germany.

This paper presents the Norwegian Energy Community Dataset, developed to support modelling and optimization of integrated electricity-hydrogen energy systems. The dataset represents a local energy community in Porsgrunn, Norway, consisting of 400 electricity end users, including residential, commercial, and industrial consumers and prosumers. It includes hourly smart meter data for electricity consumption and generation over a nine-month period (January to September 2024). The dataset features distributed energy resources such as rooftop solar PV (installed by 300 users), a 1.3 MW wind power plant and battery energy storage systems for 250 users. Electricity market data, including hourly buy and sell prices for 2024, are also included. On the hydrogen side, the dataset incorporates transport demand from (a) a real hydrogen-powered ferry operating in a northern Norwegian island and (b) 15 synthetic hydrogen buses modelled using Norwegian specifications and realistic operational patterns. Green hydrogen production is modelled using two electrolyzers (1.2 MW and 2.5 MW) along with associated hydrogen storage. The dataset would enable researchers to explore sector coupling, local energy market design, flexibility strategies and cost optimization of community scale integrated energy system.

The ever-increasing uptake of distributed energy resources necessitates the introduction of local electricity markets at the residential level. Electric retailers, who are adversely affected by these changes, can make a profit by operating local trading platforms and offering services through community-level battery storage. In this work, we propose a Stackelberg game-based approach for sizing the centralized battery unit under the operation of a multi-interval local market. The optimization is formulated as a bilevel program, where the leader is the market aggregator responsible for determining the local prices and battery charging/discharging schedules. Also, the followers in the bilevel program are prosumers, who can vary electricity consumption with respect to their comfort and cost of electricity. Upon obtaining the optimal capacity of the community storage, we modify the algorithm to efficiently operate the battery on a daily basis. The applicability of the proposed model is evaluated using real-world data of residential prosumers with rooftop photovoltaic systems for two different pricing schemes, which represents the profit trade-off between the aggregator and prosumers. The results show the profitability of the proposed model for community storage installation, where a relatively short payback period can be achieved via either pricing scheme.

This paper describes a peer-to-peer (P2P) energy trading market framework based on game theory and agent-based modeling (ABM) that enables owners of photovoltaic (PV) systems to sell the electricity they produce to neighbors and the grid. Energy is traded at a rate determined by local energy producers and consumers. The energy price is dynamic and depends on changes in the generation-to-demand ratio throughout the day. The amount of excess energy produced is listed in the market by all available sellers, along with the generation type, price, and location. The market framework was tested using multiple scenarios, which were determined by including battery storage, reduction of subsidies for non-renewable energy, introduction of a carbon tax, and increasing PV capacity to study outcomes of solar PV trading. The results suggest that when greater PV capacity is assumed, the benefits of trading increase, and a larger proportion of household demand is met locally without the need to buy energy from the grid. Adding battery storage to the system enhances trading, provides more energy for sale, and reduces the amount of energy purchased from the grid. The model presented can be scaled up to model several neighborhoods or an entire city.

No abstract available

The expected increase in distributed energy resource (DER) penetration at residential levels is promoting new local market frameworks to manage the use of these resources efficiently and improve users' and energy communities' welfare. In this regard, Peer-to-peer (P2P) energy trading and the flexibility market emerge as tentative solutions to address this purpose, empowering the users and energy communities' role in the electricity markets and in the energy transition. This article introduces a deterministic three-stage optimization model to self-manage congestion arising from the high penetration of DERs within an energy community through a local P2P and flexibility market framework. Thus, under a day-ahead time framework, the initial stage considers that each user minimizes their own objective function, which is the energy bought from the grid. Then, in the second stage, the DSO receives the scheduling dispatch from each user through the local market operator and solves an optimal power flow problem to identify possible congestion line issues. If any congestion appears in the second stage, it is managed in the third stage using the flexibility provided by the DERs and the P2P energy trading, considering the user's preferences derived from the first stage problem. The model has been tested in a modified version of the IEEE 33 bus system and shows the capability to mitigate congestion line issues using the flexibility from the storage and PV systems under a P2P energy trading scheme.

The electricity market has increasingly played a significant role in ensuring the smooth operation of the power grid. The latest incarnation of the electricity market follows a bottom-up paradigm, rather than a top-down one, and aims to provide flexibility services to the power grid. The blockchain-based local energy market (LEM) is one such bottom-up market paradigm. It essentially enables consumers and prosumers (those who can generate power locally) within a defined power network topology to trade renewable energy amongst each other in a peer-to-peer (P2P) fashion using blockchain technology. This paper presents the development of such a P2P trading-facilitated LEM and the analysis of the proposed blockchain-based LEM by means of a case study using actual German residential customer data. The performance of the proposed LEM is also compared with that of BAU, in which power is traded via time-of-use (ToU) and feed-in-tariff (FiT) rates. The comparative results demonstrate: (1) the participants’ bill savings; (2) mitigation of the power grid’s export and import; (3) no/minimal variations in the margins of energy suppliers and system operators; and (4) cost comparison of Ethereum versus Polygon blockchain, thus emphasising the domineering performance of the developed P2P trading-based LEM mechanism.

The integration of renewable energy resources and electric vehicle (EV) fleets with community microgrids (CMG) has increased fluctuations in net load. To address this and ensure safe operation, tapping into demand-side flexibility capacities in local electricity markets (LEM) is essential. Hence, this article presents a multilevel methodology for settling energy and flexibility markets among CMGs, utilizing the potential of Internet-of-Things-enabled appliances (IoT-EA), thermostatically-controlled loads (TCLs), and EVs in smart residential buildings (SRB) to enhance system performance. At level 1, SRBs are modeled using the virtual energy storage system (VESS) concept. Level 2 involves CMG scheduling, and at level 3, the distribution system operator settles the energy and flexibility markets using an adaptive alternating direction method of multipliers (ADMM) algorithm. Strong duality theory (SDT) and Karush-Kuhn-Tucker (KKT) conditions form a mathematical program with equilibrium constraints (MPEC) where market prices are variable for all participants. By unlocking the potential of SRBs, the proposed framework reduces flexibility market costs by 49.67%, network losses by 24.1%, and improves the voltage profile. The results confirm that the proposed market clearing mechanism ensures market efficiency and protects CMGs' privacy.

The progress of ICT technologies, day-ahead forecast, home energy management systems, implementation of smart meters, and Distributed Energy Sources (DER) enables new business opportunities for prosumers to locally trade the surplus via blockchain platforms leading to considerable advantages at the community level. The current research handles settlement similar to a centralized market that it is not necessarily the best solution for blockchain. Nonetheless, the settlement is essential as sellers and buyers perceive the attractiveness of the local trading through the market results. In this paper, we propose two novel and efficient settlement mechanisms (Global Balancing Settlement GBS and Splitting Settlement SS) for Peer-to-Peer (P2P) electricity exchange enhancing the performance of the classic Pairwise Settlement PS. These will be written as stored procedures embedded into the smart contracts along with auctioning procedures. The simulations are performed using a small residential community with 30% of the electricity that can be locally traded to lower the bills and unstress the public grid. The performance of the two proposed settlement methods is proved by the 14 scenarios that thoroughly indicate that GBS and SS provide better results for both sellers and buyers than PS. In the reference scenario, with GBS, sellers have the highest encashments with almost 4% more, whereas buyers encounter the lowest payments with almost 5% less than in case of the classic settlement. Starting from reference scenario, alternative scenarios are envisioned to extend the analyses and assess the performance of the settlement mechanisms. The highest gain is recorded with GBS mechanism: almost 8.8% for sellers and 6.5% for buyers. Another interesting outcome is that GBS is providing better results than SS. When deviations are small, SS provides almost 6% gain for both sellers and buyers, but when they increase, the gain is exceedingly small or none.

The proliferation of distributed renewable energy resources and plug-in electric vehicles (EVs) have helped residential electricity consumers evolve into prosumers as they participate in the local energy market (LEM) by engaging in transactions of surplus electricity. In this system, the budget-balance problem is a frequent issue, particularly when Vickrey-Clarke-Groves (VCG)-based mechanisms are applied to managing the two-sided nature of LEM. Although this issue could be partially addressed by manually modifying the LEM, the variance in the LEM environment needs to be better understood. This paper proposes a deep learning-based automatic mechanism design (AMD) method to improve VCG for tackling the budget-balanced two-sided LEM, as a way to avoid tedious manual adjustments. A convolutional neural network (CNN) with self-attention mechanism is constructed to extract features from biddings and to provide robust generalization capabilities for participating prosumers. The gated recurrent units (GRUs) are utilized to extend the proposed approach to the non-stationary bidding environment. This improved mechanism is targeted as efficient and incentive compatible, with the ability to keep the balance between the budget-balance and individual rationality. Case studies are conducted to demonstrate effectiveness of the proposed automatically improved mechanism and adaptive ability to various bidding environments.

Local energy markets (LEMs) are well suited to address the challenges of the European energy transition movement. They incite investments in renewable energy sources (RES), can improve the integration of RES into the energy system, and empower local communities. However, as electricity is a low involvement good, residential households have neither the expertise nor do they want to put in the time and effort to trade themselves on their own on short-term LEMs. Thus, machine learning algorithms are proposed to take over the bidding for households under realistic market information. We simulate a LEM on a 15 min merit-order market mechanism and deploy reinforcement learning as strategic learning for the agents. In a multi-agent simulation of 100 households including PV, micro-cogeneration, and demand shifting appliances, we show how participants in a LEM can achieve a self-sufficiency of up to 30% with trading and 41,4% with trading and demand response (DR) through an installation of only 5kWp PV panels in 45% of the households under affordable energy prices. A sensitivity analysis shows how the results differ according to the share of renewable generation and degree of demand flexibility.

In this paper, the coordinated operation of agents representing residential household energy management systems in the electricity market is considered. Each agent has its local decision vector that may contain both continuous and discrete variables. The problem of minimizing the energy procurement cost of the community of agents is represented by a Mixed Integer Linear Program with local and global constraints. A dual decomposition method with a tightening of the global constraint is applied to solve the problem with guarantees for the feasibility of the obtained solutions. The problem is decomposed and solved in a distributed way with coordination by a community coordinator. The method is applied to a realistic case study of 15 households.

Transactive energy is a two-way exchange of energy between the electric power grid and a community’s distributed energy resources, offering opportunities for efficiency improvements through market-based economic and control techniques. A community’s distributed energy resources include electricity-producing resources and controllable loads. Increased usage of unsynchronized generation of non-dispatchable solar photovoltaic energy and household demand at the community level can adversely affect the power quality, reliability and network balancing of the electricity grid. A solution was developed in this paper in the form of energy storage and demand side management on a solar residential community. An agent-based transactive energy management system was developed and simulated using multiple prosumer houses with roof-top PV systems and local energy storage. Experimental work conducted on an archetype house, near Toronto, Ontario, Canada, was used to model an all-electric residential house and clusters were created with varying orientations and building properties to mimic different efficiency levels of the houses within the virtual community. A machine learning algorithm using historical data and weather forecasts from Natural Resources Canada (NRCan) was used to forecast the community’s energy generation as well as building’s thermal loads. In this community, consumers can curtail their loads based on price signals sent to smart devices in homes. Open-loop mixed integer linear programming technique (MILP) and model predicted control (MPC) were compared and evaluated. The simulation shows promising results with a 9% energy savings during the summer solstice day and 5% during the winter solstice day when compared to the normal operation of houses’ mechanical equipment.

Electric vehicle (EV) coordination can provide significant benefits through vehicle-to-everything (V2X) by interacting with the grid, buildings, and other EVs. This work aims to develop a V2X value-stacking framework, including vehicle-to-building (V2B), vehicle-to-grid (V2G), and energy trading, to maximize economic benefits for residential communities while maintaining distribution voltage. This work also seeks to quantify the impact of prediction errors related to building load, renewable energy, and EV arrivals. A dynamic rolling-horizon optimization (RHO) method is employed to leverage multiple revenue streams and maximize the potential of EV coordination. To address energy uncertainties, including hourly local building load, local photovoltaic (PV) generation, and EV arrivals, this work develops a Transformer-based forecasting model named Gated Recurrent Units-Encoder-Temporal Fusion Decoder (GRU-EN-TFD). The simulation results, using real data from Australia's National Electricity Market, and the Independent System Operators in New England and New York in the US, reveal that V2X value stacking can significantly reduce energy costs. The proposed GRU-EN-TFD model outperforms the benchmark forecast model. Uncertainties in EV arrivals have a more substantial impact on value-stacking performance, highlighting the significance of its accurate forecast. This work provides new insights into the dynamic interactions among residential communities, unlocking the full potential of EV batteries.

This article proposes a network hosting capacity-aware local energy trading (LET) among residential customers to reduce their electricity costs, while the margins of the distribution stakeholders, including a retailer and a network operator, are maximized. A maximized customer export and import approach is proposed using dynamic operating envelopes. With maximized export and import allocation, a cooperative local energy market paradigm is proposed, in which customers, a retailer, and a network operator engage in peer-to-peer energy trading to reap the maximum possible financial benefits. The mathematical properties of the proposed LET paradigm, which include cooperation benefit, cooperation stability, pricing stability, and allocation fairness, are also proven to articulate the framework generality. A network hosting capacity-aware LET algorithm is also provided and deployed on a representative low-voltage distribution network. The performances of the proposed algorithm in terms of the integrity of the distribution network, the reduction in electricity cost reduction for customers, and the increase in margins of stakeholders are validated by extensive simulation results.

The idea of community energy network is being advocated to enhance the elasticity of diverse energy systems required for efficiently integrating a substantial volume of distributed energy resources. On the other hand, the interest in renewables-based desalination systems has received significant interest recently to consider freshwater as an additional end-use product in the community energy network system. Within this context, this paper introduces a multifaceted method for community energy networks with a focus on desalination-capable systems. The central goals involve diminishing the cumulative long-term expenses of the configuration, all while concurrently augmenting the system's capacity to store electrothermal energy on a daily basis that varies – all aimed at enhancing the reliability and security of resource provisioning. Importantly, the model co-optimizes the community energy network expenditure and reserve capacities, whilst integrating electrical, thermal, and natural gas vectors, as well as providing a platform for supplying freshwater needs. The overall freshwater provisioning infrastructure incorporates a water storage system, a desalination unit, a water well component, and a water pumping system. Furthermore, for the purpose of enhancing the adaptability, the community energy network concept put forth here utilizes coordinated electrothermal responsive load initiatives. These are coupled with meticulously planned electrothermal reservoir setups to curtail the wastage of surplus renewable production amidst diverse origins of unpredictability. The normalized weighted sum method is employed to convert the proposed formulation to a single-objective problem that is amenable to commercially available solvers in GAMS software. Then, the modelling framework is adapted to a system populated for a hypothetical site. The results verify the validity of the model in yielding globally optimum results for complex community energy networks with intertwined vectors of energy and end-use products. They also indicate that relatively small raises in the size of the electric and thermal reservoirs – and insubstantial raises in the expenditure of the system – can have potentially significant impacts on the ability of the system in serving loads during contingency conditions. In particular, by implementing demand response programs a cost reduction of 2.07% is shown, which is significant in the day-ahead operational planning phase.

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.

“Sharing economy” refers to a transformative socio-economic phenomenon where individuals or institution with idle resources transfer the right to use resources for economic compensation. With the widespread adoption of distributed photovoltaic generation and energy storage (ES) device in residential communities, there is a growing interest in establishing a suitable platform for residential users to share their ES capacity with community shared equipment controllers (CSECs). This paper proposes a local ES capacity sharing market, and presents the market trading process, pricing and allocation rules using an iterative uniform-price bidding mechanism Acknowledging the selfish-interest of both RUs and CSECs, we introduce the resource management organization (RMO) as a regulated third-party organization responsible for administering the market. To evaluate the proposed scheme, we conduct case studies based on real-life data from Pecan Street. The numerical experiment results demonstrate the effectiveness and applicability of our approach.

With the rapid growth in clean distributed energy resources involving micro-generation and flexible loads, users can actively manage their own energy and have the capability to enter in a market of energy services as prosumers while reducing their carbon footprint. The coordination between these distributed energy resources is essential in order to ensure fair trading and equality in resource sharing among a community of prosumers. Peer-to-Peer (P2P) networks can provide the underlying mechanisms for supporting such coordination and offer incentives to prosumers to participate in the energy market. In particular, the federation of energy clusters with P2P networks has the potential to unlock access to energy resources and lead to the development of new energy services in a fast-growing sharing energy economy. In this paper, we present the formation and federation of smart energy clusters using P2P networks with a view to decentralise energy markets and enable access and use of clean energy resources. We implement a P2P framework to support the federation of energy clusters and study the interaction of consumers and producers in a market of energy resources and services. We demonstrate how energy exchanges and energy costs in a federation are influenced by the energy demand, the size of energy clusters and energy types. We conduct our modelling and analysis based on a real fish industry case study in Milford Haven, South Wales, as part of the EU H2020 INTERREG piSCES project.

The rapid development of distributed energy resources has brought in challenges to the real-time balance between electricity supply and demand. In this context, a suitable market design not only solves the problem of resource allocation, but also maximizes social welfare. This paper compares two typical community market designs: centralized manager-based energy market and the peer-to-peer energy trading. Both the model of the manager-based energy market and a continuous double auction based peer-to-peer trading are presented. In case studies, we quantitatively compare these two market designs in terms of social welfare, total payment, and energy trading volume. The results indicate that the manager based energy market facilitates trading energy within the community and, if necessary, with external communities, but it requires collecting information of every market participants and has to solve a centralized optimization problem. In contrast, for an energy self-sustaining community, peer-to-peer energy trading might be a superb design because it is decentralized, flexible, and privacy-preserving.

With a spectacular boom in blockchain technologies at various verticals and with the increase in renewable energy penetration, these could solve one of the major issues in Power Systems - Energy Demand Management by the utilities. Blockchain being a distributed architecture, it could fit into the deregulated energy market to enable energy trading in a self sustainable energy community and manage the power demands among the community members. As proof of concept, the blockchain technology is introduced in a smart community where consumers take part in energy trading among themselves as well as with the utility. In this paper, three use cases for P2P energy trading in a private blockchain network is discussed. The blockchain architecture is developed using the Hyperledger framework and the smart contracts are defined in the chaincode. The performance of the blockchain network on resource constrained IoT system based on Raspberry Pi is also evaluated.

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.

The development and popularization of distributed renewable energy generation have granted traditional small-scale electricity consumers the qualification to participate in the electricity trading market. The corresponding trading mechanisms adapted for this purpose can guide the participation of small-scale users in the market, promote the integration of renewable energy, and alleviate the operating pressure on the distribution grid. Therefore, a day-ahead distributed energy sharing model for community photovoltaic(PV) users is designed by considering the power consumption characteristics of small users and introducing the cloud energy storage(CES) mechanism. Firstly, the user identities are classified, and models for various types of trading behaviors in the market are constructed based on multi-game theoretical approaches. Secondly, parallel distributed algorithms are employed to solve the equilibrium solutions of each game and generate day-ahead trading orders. Furthermore, based on the day-ahead trading results, considering the energy consumption characteristics of community PV users, a user-side CES day-ahead operation optimization model is constructed and solved. Finally, the feasibility and effectiveness of the proposed trading method are verified through case study simulations.

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.

Smart buildings in the integrated community energy system (ICES) are normally equipped with distributed energy resources (DERs), thereby creating building prosumers with both energy production and consumption. Peer-to-peer (P2P) energy trading among building prosumers can bring higher economic benefits for them. Therefore, a P2P multi-energy trading scheme among building prosumers is proposed, which fully explores the flexibility of buildings’ heating loads based on the thermal dynamics of buildings with different thermal insulation properties. Each building prosumer is heterogeneous in terms of its computation and communication infrastructures. This results in a heavy computation burden with the traditional centralized method. To improve the computational efficiency for P2P trading among heterogeneous building prosumers, an asynchronous distributed algorithm based on alternating direction method of multipliers (ADMM) is developed to enable each prosumer to trade energy asynchronously instead of waiting for the trading information from others with poor infrastructure. This asynchronous procedure integrated with the prediction and anomaly detection steps can further accelerate the convergence speed of P2P trading. Simulation results verify the effectiveness of the proposed trading scheme and the feasibility and solution optimality of the proposed algorithm.

The integration of distributed energy resources (DERs) into renewable grids faces challenges from volatile generation, dynamic loads, and non-independent and identically distributed (non-IID) data across decentralized nodes. Existing centralized optimization methods lack scalability and privacy safeguards, while conventional federated reinforcement learning (FRL) struggles with unstable convergence under asynchronous updates. This study proposes DA-FRL (Distributed Asynchronous Federated Reinforcement Learning), fusing Asynchronous Advantage Actor-Critic (A3C) with Adaptive Proximal Policy Optimization (APPO). Innovations include: (1) an asynchronous federated aggregation mechanism combining A3C’s distributed training with APPO’s adaptive KL-divergence regularization for robust non-IID convergence; (2) a LSTM-AEPPO probabilistic forecasting model using long short-term memory (LSTM) for temporal dependencies and adaptive exploration (AEPPO) to balance exploration-exploitation trade-offs; (3) a differential privacy-preserving gradient sparsification protocol reducing communication costs via Top-k gradient compression while securing sensitive client data. Experiments on NREL wind and CDGS photovoltaic datasets show DA-FRL reduces Winkler scores by 18.7% and 21.3%, respectively, advancing secure, scalable DER coordination under dynamic grid conditions.

Smart Grid 2.0 (SG 2.0) implementation constitutes an additional challenge in the industry and research fields. Energy consumption decreases when producers exchange excess energy consumers, including intelligent consumers, Distributed Generation (DG), such as wind and solar, and Electric Vehicles (EVs). By utilizing Demand Response (DR) based on Real-Time Pricing (RTP), the operation of every device in a smart home can be scheduled. Allowing users to trade energy directly with other energy producers (prosumers) rather than exclusively relying on the grid, peer-to-peer (P2P) energy trading in smart homes lowers energy prices for users. This article focuses on how the DR P2P energy trading affects consumers. The study conducted utilizes a two-stage scheduling technique to reduce consumers' electricity expenses. The initial stage involves arranging each device in the smart home based on RTP employing a deep learning method. The P2P energy trading between consumers in the second phase is made more accessible by the DR and the simulation results exhibit that energy trading decreases electricity bills in smart homes. Utility companies can reduce load during peak hours using DR-based P2P energy trading.

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.

Cloud energy storage (CES) is a cost-effective solution for residential energy sharing, transforming consumers into self-sufficient ones. This paper uses a multiround seller–buyer matching strategy to introduce an optimized energy management model for end-to-end (E2E) energy trading. The seller–buyer offers the bid multiple times in a time slot. The model considers factors, such as agent load profile, distributed energy resources, user grid cost, energy trading cost investment for individual batteries, and CES. The efficacy of the proposed model is substantiated through simulation. The main highlights are introducing a single-round seller–buyer matching strategy and a multiround seller–buyer matching strategy to determine the market clearing price for E2E energy trading between agents. Simulations show that CES user agents reduce costs, reduce grid energy demand, and increase profit for users, with overall community costs reduced by 36.05% and profit increased by 17.10% with a single-round seller–buyer matching strategy. The proposed trading strategy has also been validated using market data from India and British Columbia, Canada.

To efficiently use the ubiquitous behind-the-meter distributed energy resources (DERs) in distribution systems for providing grid services, this paper presents a hierarchical control framework for DER optimal aggregation and control. We first develop a convex optimization model to evaluate the DER flexibility, and then use a convex model-predictive-control based approach to dispatch those DERs. The hierarchical control framework consists of a utility controller, community aggregators and multiple home energy management systems. The flexibility of the DERs is evaluated by each controller in the hierarchy such that the resultant flexibility is feasible given its operational domain. Based on the determined flexibility, the hierarchical controllers then compute optimal setpoints for the DERs to help the distribution system regulate node voltages and provide other distribution grid services. Numerical simulations performed on a model of a real distribution feeder in Colorado, using actual DER data in a residential community demonstrate that the proposed approach can effectively alleviate voltage issues and support resilient operation.

There are numerous strategies to coordinate distributed energy resources (DERs) to provide a variety of services to the power grid. DER coordination can affect resources and participants unequally, for example, by excessively degrading or curtailing particular DERs more than others. However, few DER coordination strategies explicitly take into account fairness, equity, or justice. In this paper, we explore fairness metrics and their applicability to the DER coordination problem. In particular, we investigate metrics from machine learning to identify metrics that could be incorporated into DER coordination problems and we summarize fairness metrics that have been used in the power systems literature. A key challenge is that most existing fairness metrics are static – ensuring fairness at a point in time. DER coordination problems are inherently dynamic and we often care about fairness over time, not at each time. The machine learning literature offers some ways to think about fairness over time and, more generally, how to incorporate fairness into dynamic power systems problems. We use a specific DER coordination problem – the problem of computing dynamic operating envelopes – to demonstrate how incorporation of a fair-over-time metric changes DER coordination solutions, and highlight the trade-offs that arise.

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.

This study examines the feasibility and impact of a blockchain-enabled distributed energy trading platform designed to support rural electrification in Ethiopia’s East Shewa Zone. A simulation model was developed to evaluate how effectively blockchain technology can optimize solar photovoltaic (PV) energy use and facilitate efficient energy transactions within an off-grid rural community—including residential, agricultural, and public service users. Results show that the platform met approximately 94% of the community’s electricity demand while minimizing renewable energy curtailment. The integrated dynamic pricing mechanism successfully managed consumption during shortages by signaling scarcity through real-time price adjustments. The platform also demonstrated strong resilience under various stress scenarios, including seasonal variation, equipment failures, and network disruptions. These findings suggest that blockchain technology is a viable and efficient solution for improving energy access in rural areas and highlight opportunities to further enhance energy equity among diverse user groups.

An increase in the deployment of Distributed Energy Resources (DERs) and Renewable Energy (RE) resources is a promising paradigm in the decentralized energy era. It has motivated multi-Microgrids (MGs) to trade energy directly with others in the Local Energy Market (LEM), as well as with the main grid. The LEM has become a popular platform that covers several shortcomings of surplus/deficient energy, which can also manage the increasing connection of multi-microgrids, meet internal balance, and maximize the social welfare of the community Microgrid (MG). Moreover, in the LEM, the MGs would like to provide some payoff to encourage each other to exchange their energy locally. However, designing an appropriate market framework, privacy protection, and the community’s unbalanced energy supply and demand is challenging. To cope with these challenges, in this study, an LEM for a multi-microgrid system is designed to maximize the social welfare of the community, and a decentralized clearing algorithm based on the Alternating Direction Method of Multipliers (ADMM) is proposed for local market clearing and privacy protection. The Community Manager (CM) is used as an intermediate coordinator between the interconnected MGs. This way, the computation process will be completely distributed, and the privacy of each MG will be protected. Moreover, considering the utility function for the consumers and energy providers, an equivalent cost model based on internal pricing is proposed to state the willingness of the utility and motivate the participants to join LEM. Finally, an illustrative example and a case study are used to demonstrate the efficiency and effectiveness of the proposed design of LEM and algorithm in terms of social welfare and power balance. In our study, we found that by using dynamic pricing in conjunction with our proposed model, the social welfare of the energy community can be increased by 14.25%. This demonstrates the significant economic benefits and effectiveness of our approach in the Local Energy Market (LEM).

Due to increase in use of rooftop solar PV system and increase of electricity price, the power sharing among the prosumers of community microgrid become an interesting research area. This study proposes a power sharing mechanism that captures the interaction within a community microgrid. The efficient management of energy sharing is crucial for the efficient operation of community microgrids. To design rooftop solar PV systems, Helioscope software is employed. One of the key features of Helioscope is its ability to efficiently arrange arrays and blocks of solar panels based on the designated location within the software. The power sharing is obligated to coordinate the sharing of PV energy with maximization of the own profit, while the prosumers are autonomous to maximize their utilities with demand response availability. Finally, a load demand and PV generation mechanism is designed to deal with the uncertainty of PV energy and load consumption.

Several microgrids (MG) operating in the neighborhood can be integrated to form a community of MGs to harness several benefits of a larger networked system. The present research is an investigation of the coordination between the several AC and DC MGs of such community MG. Individual MGs may function as self-contained entities, but they might also collaborate with other nearby MGs to provide backup operations in the community MG. In this paper a coordination technique is suggested for transmitting excess power of a MG to the community bus under the control of the community MG controller so that nearby MGs can use it as needed, while maintaining stable operation of the participating MGs. A centralized controller called the community MG controller maintains the coordination between the neighboring MGs. The controller receives power shortage report from the MGs facing power shortage problem and decides as to which MG can draw this excess power. The controller uses a priority-based distribution algorithm to decide the allocation of power. The algorithm takes into consideration the presence of critical loads like hospital, industry, academic institution, etc. An infrastructure of smart metering (SM) is used to check the amount of power flow into any MG. The MGs will be penalized if they draw power more than that allocated to them. Thus, each MG is able to maintain stable operation without having to resort to spinning reserve for excess load and dump load to remove the excess power from the system. The obtained simulation results show that the suggested approach is a practical and efficient means of coordinating power flow in an islanded community MGs.

In recent years the development of LVDC distribution networks is under consideration. DC electrical distributions offer several advantages compared to AC ones in many applications, in particular in the presence of energy storage systems and distributed generation like high efficacy, flexibility and simple integration of renewables. The DC distribution allows to integrate in a more efficient “microgrid” different sources with DC/DC converters. The article proposes an innovative model of microgrid configuration for aggregations of end-users able to share the power produced by common generators and energy services named by the authors Power Sharing Model (PSM) using a DC bus that connects in a one way approach, the common generators to the end-users. The article investigates on the different suggested configurations of the PSM, with the converter characteristics and controls. A simplified case study is analyzed to test the performance of the sharing model and the stability of the control in different scenarios. The article compares the PSM based on a LVDC grid with existing approaches of virtual aggregations, and it highlights the main differences between the currently existing methods and our new LVDC microgrid approach. The suggested PSM appears more efficient, convenient and flexible than the existing virtual models, because users physically self-consume and share the energy locally generated.

In recent years, because of the energy crisis and fossil fuels depletion, Renewable Energy Sources (RES) are more and more deployed to meet the energy demand and their strategic importance is rising more than ever. Most of them are natively sources in DC, moreover many loads, such as electric vehicles and most electronic devices, are natively loads in DC too. The latest European directives introduced the concepts of Renewable Energy Communities (REC) and collective self-consumption. These communities are increasingly attractive and, considering that the collective self-consumption (as power sharing) can be easily applied when the community is based on a DC microgrid, this network topology is more and more developed and studied. In this paper, the authors want to investigate the possible control approaches suitable for the management of a DC microgrid designed to realize collective self-consumption through a power sharing model. A possible architecture for the DC microgrid is presented, highlighting the main elements which can be actively included in control algorithms and the main tasks to be pursued. Different control approaches are analysed and compared, highlighting the main benefits and limits of specific applications and configurations.

In recent years the development of the LVDC distribution networks is under consideration. DC electrical distribution offers several advantages compared to AC in many applications, in particular in the presence of distributed generation and energy storage systems like high efficacy, flexibility and simple integrated to renewable sources. The DC distribution allows to integrate in a more efficient “microgrid” different sources with DC/DC converters. The paper proposes an innovative model of microgrid architecture for aggregations of users able to share the power produced by common generators and energy services called power sharing model (PSM) using a DC bus, called power sharing link (PSL), that connects the common generators to the users. Each user has also an independent AC connection point with the distributor and a power electronic converter as Open Unified Power Quality Conditioner (Open UPQC), so PSM can be easily implemented in special power sharing contracts compliant with national regulatory systems. The produced energy is shared by PSL to the users through unidirectional power electronic device. In this way the common generator is like a multiple generator without power exchange between users and each user remain passive towards the distributor. Only the balance node of the DC bus assumes a role of active user.

This paper presents an in-depth evaluation of peer-to-peer (P2P) energy sharing in community microgrids using PSS Sincal for power flow simulations. The study focuses on an IEEE bus system configured as community microgrids, where each bus functions as an independent microgrid equipped with loads, Solar PV systems, batteries, and converters. These microgrids are connected to the main grid and leverage a dynamic power flow control mechanism through strategically placed switches managed by a custom algorithm by ensuring the power flow converges. Four distinct scenarios were rigorously tested: ”Isolated Operation,” where each community grid meets its power requirements solely through PV generation; ”Total Dependence on the Main Grid,” where PV systems generate no power, and communities rely entirely on the main grid; ”Partial PV Generation,” where PV systems partially meet power needs, supplemented by the main grid; and ”Surplus PV Generation,” where PV systems generate excess power for sharing. The PSS Sincal simulations demonstrated the algorithm's effectiveness in enhancing energy distribution efficiency, stability, and the utilization of renewable resources. This study underscores the practical viability of P2P energy sharing in community mi- crogrids, contributing valuable insights to the development of resilient and intelligent power networks.

In recent years, investment in renewable energy sources (RES) has become a global priority, driven by the objectives of the Paris Agreement to achieve a carbon-neutral future. Integrating these sources not only supports emission reduction and power balance but also contributes to reducing the cost of energy for communities. Organizing distributed energy resources (DERs) and energy storage systems (ESSs) into energy communities, particularly in developing countries, empowers the local economy, reduces energy poverty, and lowers emissions. This paper investigates a case study in which the campus of Ss. Cyril and Methodius University is organized into a microgrid. Three scenarios are analyzed: (i) no operation optimization, (ii) operation optimization of individual campus buildings without considering energy sharing among them, and (iii) optimization with peer-to-peer (P2P) energy sharing among the campus buildings forming an energy community. The results demonstrate that enabling energy sharing leads to a significant reduction in the overall system cost. The findings highlight the benefits of organizing a university campus into a microgrid system to enhance both economic and operational performance.

With the diffusion of the prosumer figure as a participant in the distribution network, new forms of aggregations are emerging with the aim of sharing energy among prosumers and consumers. The energy community is therefore a new possibility for increasing both final user and prosumer economic benefits. In this context, final users could find profitable to modify their demand profile in order to increase the shared energy and community advantages. In this paper, the impact of demand response mechanism in energy community operation planning is investigated through a multiobjective energy management strategy. To this purpose, the adopted optimization procedure accounts for energy community economic goals, considering the peculiarities of prosumers as DC microgrids, along with technical targets of distribution grid. Suitable evaluation of user flexibility in the community is provided by means of specific indicators.

Distributed Energy Resources (DER) provides a unique opportunity to support communities in powering electric loads in case of wide spread power outages for a prolonged period. We propose an energy sharing approach among customers to supply load after a wide-spread and sustained power outage, leveraging rooftop solar photovoltaic (PV) generation. An optimization-based algorithm is proposed to facilitate the energy sharing approach, with an objective function designed to ensure rationality of individual participation. By means of a case study, we show that community sharing of DER resources enables more electricity to be supplied to each customer than what could be achieved if customers operated in isolation from one another.

The increase in the number of installed renewable energy in a residential network needs an efficient energy management system (EMS) to store or sell the surplus energy back to the grid during the surplus generation. Selling of power back to the grid can affect the reliability of the traditional grid system, to avoid this energy sharing is proposed to share the surplus energy with close neighbours to avoid the transmission loss and increase the utilization rate of renewable energy. In this paper, we use the cyber-physical system (CPS) to coordinate the neighbours and to collect data from all the homes using the smart meters. The system’s objective is to reduce the grid cost and to benefit each home which shares the energy. This system reduces the need for a huge energy storage system (ESS) which is a major capital cost during the installation. We adopt a scheduling algorithm to manage the loads during the shortage of supply and flexibility in scheduling the loads helps to improve energy management without disturbing the user comfort. The whole model is validated with an experimental setup in the university as a part of the MNRE Funded Project.

With the development of intelligent power systems, the ecological community of microgrid community power autonomous organizations has become increasingly active. However, the uncertainty of renewable energy within the microgrid has led to energy coordination issues within the community, posing a threat to the sustainable development of microgrid community energy. And blockchain technology, with its characteristics of decentralization, tamper resistance, and distributed storage, is highly compatible with decentralized and autonomous microelectronic networks. This article utilizes intelligent contract automation to achieve optimal allocation and utilization of power resources. Proposed a blockchain-based energy storage time-sharing trading model Blockchain time-of-use energy storage (BLES-TOU). After processing the data through smart contracts, we provide feedback to the subject information. We adjust the electricity consumption strategy by constructing a Stackelberg game model. Finally, we design a smart contract testing plan based on the caliper and obtain the throughput and performance analysis of the smart contract.

The minimization of distribution losses is one of the main necessities in the community based DC microgird (MG) with energy sharing between different DERs with surplus energy and/or with load diversity. Energy sharing can take place within a community MG or with other nearby community MGs. Distribution losses are normally calculated based on the distance (or distribution conductor length). However, they can also be dependant on voltage difference between sharing nodes, amount of energy sharing, and conductor size, etc. Therefore, peer-to-peer (P2P) energy sharing can't be optimally managed with only distance-based decision techniques, and must be decided based on appropriate analysis of distribution losses in an optimal power flow framework. In this paper, we proposed a nonlinear P2P energy sharing framework to ensure optimal power flow with minimal distribution losses and compare our proposed framework with nearby energy sharing model. We present the detailed analysis of distribution losses in a DC MG with several distributed generators (DG) and energy storage systems (ESSs) at distributed locations. The simulation results validate the proposed optimal P2P sharing method in reducing distribution losses upto 60 % and voltage profile of each prosumer remains in the stable limit of operation.

The new concept of renewable energy communities introduced by the Revised European Directive on the promotion of renewable sources (2018/2001) has opened new possibilities for microgrids. In fact, it permits to enhance the value of the energy produced by renewable sources sharing it inside an “energy community” and to increase the social welfare. In the present paper, the authors investigated about the actual legislation framework on energy communities at European and Italian level, highlighting regulatory problems and barriers that are delaying their constitutions. The authors propose a “power sharing model” (PSM) useful for energy communities and based on the sharing of renewables and other energy services. PSM is suitable both at building level and for larger communities. PSM has been analyzed through a case study that regards the preliminary study of a smart microgrid that should be realized in Campobasso, Italy, to connect buildings of the public administration. This scenario was investigated through a simulation conducted in Simulink environment were the control strategy was implemented, and the results were compared to a traditional configuration for renewable sources integration.

This study presents a comprehensive comparative analysis of the operational strategies for multi-microgrid systems that integrate battery energy storage systems and electric vehicles. The analyzed strategies include individual operation, community-based operation, a cooperative game-theoretic method, and the alternating direction method of multipliers for multi-microgrid systems. The operation of multi-microgrid systems that incorporate electric vehicles presents challenges related to coordination, privacy, and fairness. Mathematical models for each strategy are developed and evaluated using annual simulations with real-world data. Individual operation offers simplicity but incurs higher costs due to the absence of power sharing among microgrids and limited optimization of battery usage. However, individual optimization reduces the multi-microgrid system cost by 47.5% when compared to the base case with no solar PV or BESS and without optimization. Community-based operation enables power sharing, reducing the net cost of the multi-microgrid system by approximately 7%, as compared to individual operation, but requires full data transparency, raising privacy concerns. Game theory ensures fair benefit allocation, allowing some microgrids to achieve cost reductions of up to 13% through enhanced cooperation and shared use of energy storage assets. The alternating direction method of multipliers achieves a reduction in the electricity costs of each microgrid by 6–7%. It balances privacy and performance without extensive data sharing while effectively utilizing energy storage. The findings highlight the trade-offs between cost efficiency, fairness, privacy, and computational efficiency, offering insights into optimizing multi-microgrid operations that incorporate advanced energy storage solutions.

The isolated microgrids adjacent to each other form an isolated microgrid community, which can realize optimal resource allocation among microgrids with different source-load characteristics through energy sharing, thereby improving the economical, reliability and renewable energy utilization of regional power system operation. Aiming at the energy management problem of the isolated microgrid community, based on the multi-agent system, a hierarchical energy management architecture of the isolated microgrid community was established. On this basis, the flexibility index is introduced to evaluate the impact of renewable energy output uncertainty on system operation. A multi-objective optimization energy management strategy as well as a community layer energy allocation strategy is constructed. The linearized model is solved by the tolerant lexicographic method. Finally, an isolated microgrid community consisting of four microgrids is taken as an example to verify the effectiveness of the proposed energy management strategy.

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In recent years, the need for electricity for each home is rapidly increasing due to the number of inventions in electric appliances and electric vehicles being so high. The fast-growing gated community apartments and villas want their own microgrids to meet the needs of their own residents. In Electric Vehicles, Bi-directional power flow is made feasible by the power electronics used in the rechargeable batteries. Therefore, electric vehicles are considered as grid’s power bank Storing excess power in electric vehicle (EV) batteries through Grid-To-Vehicle (G2V) technology and returning the energy back to the grid through Vehicle-To-Grid (V2G) technology while demand is at its maximum can aid in the management of micro-grid energy. This research review analysis the architectural framework and control mechanisms to develop hierarchical energy management for energy sharing among V2G and G2V in terms of simulation. It uses fast EV charging in a microgrid. A dc speedy charging point is designed as part of a trial micro-grid system for connecting EVs. V2G-G2V power transfer, DC Bus voltage, Battery SOC and Battery Voltage and Current is demonstrated through simulation.

With the development of small and medium-sized microgrids, more community power grid systems use blockchain technology to achieve distributed energy exchange and sharing. However, with the introduction of blockchain decentralization technology, there are bribery attacks on the information security between users, microgrid service providers, and regulatory departments, which affect the balance of microgrids. To solve the problem of information exchange security among users, microgrid service providers, and regulatory departments, A micro grid bribery attack evolutionary game model based on small-scale blockchain information exchange was proposed, and the evolutionary stability of the strategy choices of each participant was analysed. The impact of different scenarios on the three-party strategy choices was explored, and the stability of the equilibrium point in the three-party game system was further analysed. Finally, simulation analysis was conducted using MATLAB, and the results showed that in a small-scale blockchain based microgrid, in response to the rent-seeking behaviour of microgrid users and service providers, which leads to grid load overload, power surplus, and other issues, the impact of bribery attacks can be controlled to a certain extent through appropriate policy intervention and participant games, achieving grid load power balance.

In this article, we develop novel mathematical models to optimize utilization of community energy storage (CES) by clustering prosumers and consumers into energy sharing communities/microgrids in the context of a smart city. Three different microgrid configurations are modeled using a unifying mixed‐integer linear programming formulation. These configurations represent three different business models, namely: the island model, the interconnected model, and the Energy Service Companies model. The proposed mathematical formulations determine the optimal households' aggregation as well as the location and sizing of CES. To overcome the computational challenges of treating operational decisions within a multi‐period decision making framework, we also propose a decomposition approach to accelerate the computational time needed to solve larger instances. We conduct a case study based on real power consumption, power generation, and location network data from Cambridge, MA. Our mathematical models and the underlying algorithmic framework can be used in operational and strategic planning studies on smart grids to incentivize the communitarian distributed renewable energy generation and to improve the self‐consumption and self‐sufficiency of the energy sharing community. The models are also targeted to policymakers of smart cities, utility companies, and Energy Service Companies as the proposed models support decision making on renewable energy related projects investments.

This study addresses strategies for managing energy in hybrid renewable energy systems, with an emphasis on photovoltaic systems, battery storage, and hydrogen storage. Two scenarios were analyzed: a conventional microgrid and another with transactions between participants in a Renewable Energy Community (REC) to verify more sustainable and self-sufficient approaches. The results show that it is possible to reduce dependence on conventional electricity grids, emphasizing the importance of sharing energy and resources within a community. The study offers insights into system efficiency and grid independence, as well as significant contributions to the implementation and management of renewable energies.

The advancements in Renewable Energy Sources (RES), increasing trends of distributed generation and proliferation of prosumer community requires an affective utilization of energy in Microgrid (MG) paradigm. Peer-to-Peer (P2P) energy trading mechanism provides a platform where prosumers can participate in energy sharing which is beneficial for all users of MG. Meanwhile, due to the high penetration of RES and distributed generations in distribution system used in P2P energy trading concept, the control and operation of existing grid is lacking the required functionalities. In P2P energy trading, power congestion occurs on distribution system which can limit effective utilization of RES. As RES and power demands are increasing, congestion problem also tends to increase. Smart Metering (SM) infrastructure is used to handle Energy Congestion Problem (ECP) by more effective management of prosumer community in P2P energy trading mechanism. SM monitors power transactions, maintains status of the network and obtains power profiles of prosumers. In this paper, Normalized P2P (NP2P) energy trading scheme is proposed with objective to decrease load on main grid and cost minimization for all users. NP2P is centralized contracting scheme treats all users in Microgrid equal for energy flow and cost implementation. Three different cases are considered to validate efficiency of proposed P2P energy trading scheme. In this research work, ECP due to NP2P model is analyzed, modeled as Knapsack Problem (KP) and solved using Greedy and Simulated Annealing (SA) algorithms. In proposed research, Central Energy Management Unit (CEMU) is responsible for implementation of functions for NP2P model and energy congestion control. To improve the reliability of grid, some prosumers with surplus energy should be disconnected. The results of various considered cases for proposed NP2P scheme and algorithms for ECP are compared to verify effectiveness of the solution.

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.

There is a need for an efficient methodology for modelling digital replicas for microgrid systems that can be used for multiple applications and ensure the reliable operation of the microgrid. In this paper, we present a whole system model-based design of peer-to-peer energy trading (P2P-ET) among prosumers in a grid-connected community microgrid. The model consists of a power system component with distributed resources; an energy storage system (ESS) with heuristic and optimised control policy; and two P2P market paradigms: bill sharing and mid-market rate, which estimates the financial benefit to the community. Simulated transaction facilitated through a case study of P2P-E $T$ between prosumers illustrates the impact of transaction on grid voltage, ESS and individual household bills. Results show that the energy bought from grid by the community is reduced by 15.36% when optimised ESS policy is used over the heuristic policy. Also, the P2P-ET reduces individual household bill in the community by at least 11.3%.

Access to electricity in the remote areas is challenging, and investing the bottom up electrification concept for DC micro grid (DC-MG). In this paper, the surplus electricity from the SHS (Solar Home System) within the DC micro grid in Bangladesh demonstrates the hidden potential to share amongst households within the same DC-MG. A single SHS is the initialization process, next the neighboring households including SHS and household without PV are the continuation of the cluster of bottom-up electrification. The surplus electricity from all SHSs’ and sharing among the community is called bottom-up electrification. Hence, the SHS generated surplus electricity shared along within the neighbor households. The tropical regions including South Asia and sub-Saharan countries mainly based on PV system have proven to be appropriate and suitable for rural electrification. Sharing the surplus electricity would be the opportunities that not only gives reliability in system but also utilizing the excessive power improve the life standard of the community. The eminent tool for energy modelling and optimization Homer Pro microgrid simulation tool used to design the optimal size of the micro grid.

With the development of internet of things (IoT) technology, massive distributed energy resources (DERs) can be aggregated to improve the economy and reliability of power grids by sharing the surplus energy among a community. Energy sharing in smart cities requires the support of advanced information and communication technology (ICT) to ensure communication reliability. However, existing literature has ignored the impact of communication reliability (CR) on energy sharing. Therefore, an energy sharing model is proposed considering CR in smart cities, which aims to investigate the impacts of CR on achieving energy sharing benefits. A multi-microgrid connected by a distribution network is conceived in this paper. Each microgrid (MG) includes photovoltaic, energy storage, controllable load, and a communication base station (BS). The BS is responsible for the communication interaction between the local MG and the distribution system operator (DSO). Due to the interference and noise among the MGs, CR is modeled as a function of transmit power of different BSs. The proposed energy sharing scheme is to achieve DER sharing under the premise that each MG meets its physical and CR constraints. Case studies based on the IEEE 33-bus feeder system validate the effectiveness of the proposed framework and method. Traditional energy sharing without considering CR may lead to the violation of CR requirements. Additionally, BSs can be used as a kind of controllable load in the distribution network to reduce operation costs.

Hierarchical energy management systems (EMSs) are becoming popular for operation of a multi-microgrid (MMG) systems. In hierarchical EMS, a community EMS is required for cooperative operation of all the microgrids (MGs), which requires an extensive communication infrastructure. It results in several challenges in operation of MMG system, such as higher cost, lower reliability, and higher communication burden. In order to overcome these problems, this paper proposes a diffusion strategy-based distributed optimization for operation of an MMG system. The proposed strategy is divided into two main steps. In the first step, the total amount of shortage power is determined in the entire MMG system. In the second step, the amount of sharing power among MGs is determined using the information of total shortage power in the MMG system. By applying the proposed strategy, the direct trading with the utility grid can be reduced due to the sharing of surplus power among the MGs of the network. The numerical results show that the MMG system can optimally operate by using the proposed method without a community EMS.

This paper investigates some issues associated with the power sharing in the Hybrid Energy Storage System (HESS) in the Multi Micro- Grid System (MMGS) to meet the load demands. To address this problem in isolated microgrids, which often arises in emergency situations, the present study proposes an efficient energy management system (EMS) which operates the battery and supercapacitor (SC) based on their State of Charge (SOC) level using fuzzy logic based control algorithm. The optimal amount of charging/discharging of the battery and SC is decided by the fuzzy inference system (FIS) . Simulations are carried out by creating a microgrid test bench for fuzzy logic system (FLS) in MATLAB/Simulink environment. The performance of the proposed approach is validated considering different modes of operation and loading conditions and found to be satisfactory.

This study extends the existing secondary droop control models for frequency synchronization and power-sharing problem to enhance the efficiency of converter-interfaced generators integration into community microgrids. We address this problem by developing models of hierarchical community EMS, which provide synchronization conditions by frequency for an autonomous microgrids community by means of graph theory and Kuramoto-based consensus algorithms for multi-agent reinforcement systems (MARLs). Moreover, we consider the optimal consensus of MARLs in the problem of design controllers for each agent in such a way that all agents will be consensual with the leader agent. The proposed control approach validates via a simulation example of a community microgrid based on the modified CIGRE distribution network.

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.

Renewable energy microgeneration is rising leading to creation of prosumer communities making it possible to extract value from surplus energy and usage flexibility. Such a peer-to-peer energy trading community requires a decentralized, immutable and access-controlled transaction system for tokenized energy assets. In this study we present a unified blockchain-based system for energy asset transactions among prosumers, electric vehicles, power companies and storage providers. Two versions of the system were implemented on Hyperledger Fabric. Assets encapsulating an identifier or unique information along with value are modelled as non-fungible tokens (NFT), while those representing value only are modelled as fungible tokens (FT). We developed the associated algorithms for token lifecycle management, analyzed their complexities and encoded them in smart contracts for performance testing. The results show that performance of both implementations are comparable for most major operations. Further, we presented a detailed comparison of FT and NFT implementations based on use-case, design, performance, advantages and disadvantages. Our implementation achieved a throughput of 448.3 transactions per second for the slowest operation (transfer) with a reasonably low infrastructure.

This study presents a novel system for simulating an energy community utilizing advanced tools such as Simulink and the Ethereum Sepolia blockchain. Simulink is employed to simulate energy transactions within the community, while the Ethereum Sepolia blockchain is used to securely record these transactions. The primary objective of this study is to assess the feasibility of establishing an energy community that utilizes a public blockchain, such as Ethereum Sepolia, within a simulated environment using the Simulink tool. This research is distinctive because no previous study has utilized a public blockchain for this type of community model through a simulation tool like Simulink. Public blockchains, while offering numerous advantages, often suffer from high transaction costs. This study aims to address this challenge by proposing a solution that can reduce these costs without compromising security or decentralization.

Through a digital platform, distributed generations can be managed intelligently to increase the overall efficacy of the distribution system. It was made possible by the growing integration of distributed generation with smart meters, Internet of Things, smart sensors, etc. Decentralized peer-to-peer (P2P) energy trading is a new concept and is encouraged by blockchain technology (BT) due to its transparency, security, and speedy transaction handling. This article expands on the P2P concept by creating a decentralized energy trading system to demonstrate the benefits of BT in providing a secure and efficient transaction platform for a community microgrid system containing consumers, prosumers, and renewable energy source (RES) owners. The supply–demand ratio method is used to determine the P2P selling and buying prices within the network based on the optimized allocations of the prosumers/RESs owners and consumers. This article highlights the participation of miners (validators) in the microgrid ecosystem, specifically local prosumers and RES owners. By actively participating in the energy trading, miners can enhance energy security, increase system resilience, and enjoy financial incentives. The suggested model designed on the Ethereum platform showcases effective energy management of microgrid system operation and increased security level through a step-by-step implementation process.

The frequent electricity transactions of multienergy complementary Energy Internet Cluster result in higher operating costs and increased risks of information security by traditional transaction mode. Therefore, based on the blockchain technology, this paper proposes an electricity trading architecture suitable for Community Energy Internet Cluster. Firstly, the article elaborates the basic structure of Energy Internet and blockchain, and analyzes the adaptability of blockchain applied to Energy Internet Cluster electricity transaction. Secondly, the process of establishing electricity trading platform and deploying smart contract based on Ethereum network is described in detail. Thirdly, the power transaction framework of Community Energy Internet Cluster is constructed, and the double auction mechanism is applied to complete matchmaking tradeoff, and the smart contract is designed. Finally, a practical energy trading platform is built through the Ganache client of Ethereum network. Case studies demonstrate the feasibility and effectiveness of the trading operation framework.

Considering the distributed characteristics of energy transactions in most microgrid systems, our study proposes a community microgrid energy transaction mechanism based on the main-side consortium blockchain, which aims to encourage honest transactions among diverse transaction entities within the microgrid. To achieve this, we first introduce an aggregation agent to partition the energy transaction scenario into two layers and construct a two-layer energy transaction framework for the microgrid using the main-side chain management approach, which enhances the system's scalability. In addition, we employ electric vehicles (EVs) for energy balancing services, which can reduce the dependence on the main grid in a more economical way and mitigate the negative impact of disorderly charging of electric vehicles on the grid. Furthermore, we design a contribution evaluation mechanism to quantify user contributions within the microgrid system and integrate the contribution evaluation into both the transaction process and the consensus process, which ultimately enhances the quality of distributed trading and ensures the stability of system operations. Through case analysis and numerical simulation, we demonstrate the effectiveness of the proposed transaction mechanism, which shows that it can improve the overall utility of the system. This research has the potential to contribute to the development of sustainable and efficient energy systems.

The integration of aggregators into the market and peer-to-peer energy trading of electric vehicles achieved through blockchain technology represents one of the potential ways to ensure peer privacy preservation, transaction trackability, and greater energy system flexibility. This paper presents a decentralized blockchain-powered smart contract-based peer-to-peer model to enable optimal energy trading coordination of electric vehicles with aggregators in the smart community, where revenue from feed-in tariffs, ancillary services, and peer-to-peer sales are optimized, and the cost is minimized using a mixed-integer linear programming formulation. The goal of the real-time bilateral trading scheme is to balance supply and demand within the constraints of the power network through direct peer-to-peer negotiations without the need for a middleman. The performance evaluation of the proposed scheme demonstrates how well the framework synchronizes power supply and demand by coordinating electric vehicles’ charging and discharging through an appropriate aggregator by consumption patterns. The development of smart contracts for initiation, enrollment, stay-alive negotiation, trading, and settlement of transactions is presented in the subsequent demonstration of the blockchain model’s distinctive establishment process. Furthermore, the transactional efficiency depicting the execution time, latency, and throughput performance for viability and scalability evaluations in practical applications is presented to illustrate the effectiveness of the proposed framework.

A digital platform can be used to intelligently manage the distributed generations to improve the overall performance of the distribution system. It became possible by the growing integration of distributed generation with smart meters, IoTs, smart sensors, etc. The adoption of blockchain technology (BT) for decentralized Peer-to-Peer (P2P) energy trading is encouraged because of its transparency, security, and fast transaction processing. This paper expands on the P2P concept by creating a decentralized energy trading system to demonstrate the benefits of BT in offering a secure and efficient transaction platform for a community microgrid system having consumers, prosumers and RES owners. The supply-demand ratio (SDR) approach is used to estimate the P2P purchasing and selling pricing within the microgrid. In order to meet the community's energy demands in the most effective way, the proposed P2P design framework mentioned in this paper has targeted the development of a community trading market using Blockchain Smart Contract. Additionally, ELEC (Electrify. Asia) cryptocurrency is considered in P2P energy trading simulation that is successfully mined and published on the blockchain network.

Buildings can become a significant contributor to an energy system’s resilience if they are operated in a coordinated manner to exploit their flexibility in multi-carrier energy networks. However, research and innovation activities are focused on single-carrier optimization (i.e., electricity), aiming to achieve Zero Energy Buildings, and miss the significant flexibility that buildings may offer through multi-energy coupling. In this paper, we propose to use blockchain technology and ERC-1155 tokens to digitize the heat and electrical energy flexibility of buildings, transforming them into active flexibility assets within integrated multi-energy grids, allowing them to trade both heat and electricity within community-level marketplaces. The solution increases the level of interoperability and integration of the buildings with community multi-energy grids and brings advantages from a transactive perspective. It permits digitizing multi-carrier energy using the same token and a single transaction to transfer both types of energy, processing transaction batches between the sender and receiver addresses, and holding both fungible and non-fungible tokens in smart contracts to support energy markets’ financial payments and energy transactions’ settlement. The results show the potential of our solution to support buildings in trading heat and electricity flexibility in the same market session, increasing their interoperability with energy markets while decreasing the transactional overhead and gas consumption.

With the high interest in the peer to peer (P2P) energy trading system, blockchain technology has increased attention as a solution to alleviate the current challenges in the centralised energy system. The study set out to discover how blockchain could be used for microgrid while controlling the scalability to encourage peer-to-peer energy trading with the assumption of the average dwelling density at around 20-30 households. The results showed that there is a scalability challenge in the blockchain as the network grows; however, the blockchain technology can be applied in the community which has a similar size with Hobart as there is no significant difference in the execution time up to 100 nodes while accumulating the transactions. Additionally, the study also found mining time, transaction fee and transactions number in the block are also related to the execution time. Therefore, these parameters should be considered to build a scalable blockchain for a microgrid.

The concept of peer-to-peer local energy trading holds the promise of fostering a more sustainable and efficient energy ecosystem, aligning with the global goals of environmental responsibility, energy conservation, and the advancement of renewable energy sources. As such, it has attracted lots of attention from researchers seeking to revolutionize the energy sector and address the challenges associated with traditional centralized energy models. However, to effectively set up a local energy market, two vital components are needed – a robust transaction infrastructure and a welldefined set of market and auction rules. In this paper, our proposed solution relies on the Ethereum blockchain and smart contracts, thereby exploiting the benefits of blockchain technology to meet the requirements for secure and decentralized energy trading within a community, all without the need for a trusted third party. We propose an Ethereumbased framework to facilitate secure and decentralized peer-topeer electricity trading among the members of a smart community. The framework implements smart contract auction with filters at different stages throughout the process to ensure the integrity of data, maintain data confidentiality, and safeguard user identity privacy on the network. We also performed a thorough security assessment of our smart contract using MythX by Consensys - a security analysis tool for Ethereum smart contracts to identify and rectify any potential security threat and vulnerabilities in our proposed framework and auction process.

The demand for Electric vehicles (EV) are growing day by day. But the power grid is operating in bottleneck to meet customer power requirements. Blockchain technology is being used in applications that involves transaction between untrusted entities. We propose blockchain enabled energy pooling platform with smart buildings and EVs. This platform enables peer to peer energy transactions between buildings and EVs. Rooftop photovoltaic (PV) systems generate power during daytime and utilised by the loads. We propose, EVs to store the excess energy generated from the rooftop PV system. EVs will discharge the stored energy during night or when there is an energy demand. We have demonstrated this concept with two use cases using Ethereum Platform on Google Cloud Platform. We also developed a Fuzzy Logic based smart contract to decide incentives for energy storage and discharge. We envision that this platform will help EV owners, OEMs and grid operators to maximise their system efficiency and help recoup their investment.

The Internet of Electric Vehicle (IoEV) energy trading is where the Electric Vehicles (EVs) provide energy to vehicles, grids, community, and buildings. A scalable, efficient, secure, and best price selection scheme is needed that supports the IoEV energy trading transaction. The traditional blockchain is used in the existing researches to achieve these needs. This paper proposes an efficient and secure energy trading scheme in IoEV energy trading using the IOTA blockchain. EVs privacy protection algorithm is proposed in which somebody cannot track EV's exact position. The Stackelberg game theory technique is used to select the best seller at a given time slot and to perform the negotiation between buyers and sellers on the energy price in an off-blockchain manner. The empirical analysis shows that the proposed scheme performs better than the traditional techniques in terms of efficiency, privacy, and energy price.

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.

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Renewable distributed generations are associated with generation intermittency. Exacerbated by the consumption and demand uncertainty and their resulting mismatch, its energy trading suffers similar uncertainty. The situation is severe in the standalone distributed generations (SDG) for lacking transaction access to the utility grid. This paper proposes the energy transaction time determination and minimization algorithm for consumers in the SDG arena. First, blockchain technology is adopted for transaction enhancement and transaction data acquisition. The acquired blockchain data includes the hourly nodes (number of blockchain members), transaction sizes, and corresponding transaction durations. Next, the blockchain-recorded transaction data are fitted using the linear regression (LR) algorithm to obtain their fitting formula. The fitting formula was subsequently optimized in hourly intervals to obtain the optimal transaction time (energy delivery time) using particle swarm optimization (PSO). Finally, the optimal results are presented in a decision tree (DT) to energy consumers in the blockchain platform. Consequently, their transaction decision-making is guided by the result against the inherent transaction time uncertainty. Consumers can thus correctly adjust their transaction habits to suitably adapt to the transaction duration fluctuations in the energy trading arena. Energy transaction delays and transaction costs are consequently minimized leading to greater penetration of renewables and bridging the generation and consumption gap.

Predicting future demand for distributed energy sources is difficult because of the energy generations is unpredictability. We proposed an auto executable blockchain-based peer-to-peer energy transaction market with Long ShortTerm Memory (LSTM) neural network for energy trading and predictions. Our proposed energy transaction market and trading strategy are built via smart contracts inside the blockchain. The energy trading process in the market is auto executable and does not need a long waiting time as in the conventional bidding strategy. LSTM is integrated into the market for predicting future energy usage. Experimental results show that the proposed blockchain-based peer-to-peer energy transaction market can earn more profit than the traditional method.

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Energy markets are being transformed rapidly all over the world due to an increased integration of renewable energy sources. Blockchain technology is emerging as a prime contender, as it can provide a secure and efficient transactional platform for such markets. In a typical microgrid energy market, the consumers and prosumers belonging to the microgrid have the ability to trade energy in a peer-to-peer fashion. However, the existing energy markets suffer from multiple issues security and privacy issues. To handle the aforementioned issues, this research work proposes a blockchain based secure Decentralized Transaction System (DTS) for energy trading in microgrids. The proposed system comprises of a secure market model that facilitates energy trade between energy users. A simplistic energy exchange mechanism has been formulated that ensures data integrity and privacy of the participating energy users. A prosumer centric consensus mechanism has been employed to incentivize the prosumers and ensure the availability of energy in the microgrid at all times. An efficient and dynamic pricing mechanism has been used to reduce the supply and demand disparity. A comprehensive trust model based on commitments has been adopted for ensuring the reliability of the participating energy user. Additionally, a hardware based access control mechanism has been utilized to make the proposed DTS a physical and cyber secure system. Other than this, a framework of smart contracts has been deployed to provide a comprehensive solution that ensures privacy, security, anonymity, auditability and confidentiality of the generated energy information. To demonstrate its practicality, the system has been implemented on Ethereum platform. The proposed DTS is validated using realistic data with the Ethereum Virtual Machine (EVM) environment of Goerli Test Network.

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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 the traditional electricity trading model, centralized transaction data and centralized regulatory agencies will bring about problems such as low data security and a crisis of trust in regulatory agencies. As an emerging distributed shared database technology in the Internet era, blockchain is related to the characteristics and it have a natural fit with the energy Internet, which brings a development opportunity to the traditional power transaction. This paper selects the microgrid operator (MO), energy aggregator (EA) and large power user (LC) as the distribution side the main body of electricity trading, built a power distribution side power market trading platform under blockchain technology, with the goal of maximizing benefits and minimizing costs, and established an active distribution network power trading optimization model based on cooperative evolutionary games,and use NSGA2 genetic algorithm to solve. And on the basis of the obtained optimization results, the Shapley value method is used to distribute the benefits of the power purchase cost of large consumer. The solution result of the optimization model is the basis for the generation of the smart contract. The calculation example results verify the feasibility of the smart contract model and the functional characteristics of the blockchain as a distributed ledger.

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The concept of P2P (Peer to peer) energy transfer includes prosumers (producers + consumers) which complements the emerging technology of blockchain. Blockchain use cases are not just confined to crypto currency; it is used in numerous fields including financial services, Internet of Things (IoT) and voting systems. This technology is reshaping the digital world by bringing a new outlook to security, efficiency and stability of systems and databases. This project aims to provide a potential solution of how blockchain facilitates cost reduction when used in microgrids. The energy is transferred from producer to consumer and the additional service charges are eliminated when using the blockchain method. This idea uses a bidding mechanism in the grid so that the producer who gives energy at a minimum cost is automatically mapped to the consumer. The modified model shows an approximate cost reduction (cost for energy consumption) of 24% in our specified use case. The cost is always reduced when using blockchain in any scenario, but the percentage may vary. The smart contract is included in blockchain shared by the participants and keeps immutable transaction records.

This article studies an energy transaction management of multienergy prosumers (MEPs) in integrated energy community (IEC). A novel MEPs coalitional game model with transferable utility is proposed to coordinate the electricity–heat transactions and enhance the local energy consumption among MEPs in IEC. The superadditivity of the proposed coalitional game is rigorously proven to ensure coalition incentives. A superadditivity-directed coalition formation algorithm is developed to achieve a stable and efficient coalition partition with significantly reduced computational burden. Furthermore, the nonemptiness of the core for the proposed coalitional game is rigorously proven, and a Shapley value-based payoff allocation mechanism is designed and proven to align with the core, ensuring both fairness and stability in the proposed coalitional game. Simulation results show that the proposed model and method achieve the highest payoff across all cases, ensure the fair and stable payoff allocation, achieve reductions of 99.2% in iterations and 98.98% in solving time, and confirm their feasibility for large-scale applications.

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The growing energy demands within community microgrids and smart villages present significant challenges, especially due to their limited connectivity to centralized power grids. In particular, the development of recent ambitious plans to modernize and grow the agricultural sector, with the aim of ensuring food security, greatly increases energy and water needs. In MGFARM project, we are aiming to develop a Hard- ware/Software platform that allows seamless interconnection between microgrids (e.g., neighboring farms), e.g., the excess produced energy might be shared by neighboring farms. This paper presents a Blockchain-based IoT platform designed to address the energy management challenges within community microgrids, particularly in smart farming and village contexts. By integrating Blockchain technology with IoT, the proposed platform offers a secure and decentralized approach to energy sharing, enhancing both transparency and operational efficiency. Moreover, it enables transparent and equitable energy distribution among stakeholders, promoting sustainability and resilience in agricultural settings. By leveraging collaborative approaches, this research addresses key challenges in decentralized energy sharing, including trust establishment and efficient resource management. This study shed further light on the effectiveness of IoT and Blockchain technologies in enhancing operational efficiency while fostering a culture of shared responsibility within the community microgrid environment.

Distributed energy users participate in demand response through load aggregators, and often do not have the right to speak and choose in the transaction, resulting in trust problems such as opaque information and excessive third-party power; At the same time, due to the poor ability of information acquisition and real-time load control, it is difficult for distributed energy users to enjoy equal rights of decision-making, information and discourse in aggregate response. Therefore, based on the blockchain technology, a non cooperative game incentive mechanism based on aggregators is constructed for the blockchain nodes of three types of flexibly loaded distributed energy users and aggregators to ensure the decision-making power and discourse power of distributed energy user nodes; A distributed energy trading mechanism based on continuous two-way auction mechanism is designed, and the principles of energy distribution and pricing are established.

The global energy issues due to fossil fuels has turned the world into having a transition to renewable resources but the global transition into renewable resources are introducing new challenges and one of the most important one is the current centralized energy system existing. Implementing renewable resources need a decentralized system or which is challenging the current traditional energy markets around the globe. While the other aspect of this transition is transparency and efficiency. A blockchain enabled trading model called P2P (PEER - TO - PEER) as solution to these challenges is a way to decentralize, secure and make a transparent energy market which will facilitate a direct energy transaction between user and producers of energy and this solution is not based on central intermediaries. Lack of transparency in current tradition energy mark is going to be solved by having smart contracts based on automated settlements, enforced rules and regulations, and last but not the least real time response, this is going to be the result of P2P blockchain technology implementation into energy market. while on the other hand, E-commerce platform for energy market which will be introducing Energy as service is another option for implementation of renewable resources in different range for different users in any society. Through the paper will review how blockchain - enabled P2P solution will be answering the issues and challenges and how E-Commerce energy business solution model works, the e-commerce (Solaris hub) platform is a conceptual business model at the moment which does not exist.

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This paper proposes a demand response-based model to manage the energy resources of an energy community, considering the data privacy of the respective community members and electric vehicles. First, through a day-ahead forecasting algorithm, the implemented model identifies possible demand response events to be launched in the energy community. Then, the community members and electric vehicles are ranked to identify which will be selected to participate in the respective demand response event. This selection is based on several metrics created to assess different types of community members' data through clustering algorithms. Furthermore, this model also considers real-time monitoring and evaluation of the demand response event. Finally, the implemented model was tested with energy community data containing 750 possible candidates to participate in the demand response event with their flexibility, where these candidates are households and several different electric vehicles. The results show that the implemented model enables a saving of 13.66 EUR while reducing the CO2 emissions by 32.34 kg in a single demand response event.

This paper shows the behaviour of a Demand Response program designed to be implemented in Energy Communities, where they take advantage of photovoltaic production. The primary objective is to manage both photovoltaic overproduction and village consumption efficiently. The DR program focuses on looking for consecutive periods that exceed a target peak set by the aggregator after analysing the consumption of the given energy community. The case study includes three villages, where participants are expected to be members of a community. The results are that participants will see a reduction in costs and electricity consumption.

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The decentralization trend of the power system highlights the role of consumers in future plans. Mediatory infrastructures, such as the energy community, play a vital role in the realization of customer-centric behaviors like Demand Response (DR). This paper proposes a two-stage DR strategy within a residential energy community, integrating Price-Based DR (PBDR) and Incentive-Based DR (IBDR). Multi-agent reinforcement Learning (MARL) is utilized for optimal policy training of the community aggregator and Home Energy Management System (HMES) of the consumers. The PBDR, as the initial stage, involves dynamic pricing of the aggregator to influence consumers' behavior. This leads to a significant reduction in peak load and smoothing of the load pattern by reducing/ shifting the consumption in/ from high-priced timeslots. The PBDR program alone may be insufficient for meeting consumption thresholds imposed by the upstream operator. Therefore, an IBDR program is introduced as the second stage, offering incentives to consumers to reduce their demands further. The synergy of these two programs can successfully modify the community demand. Simulation studies show the improvements in load characteristics of the community and a positive overall reward for both aggregator and consumers, suggesting a proper balance of objectives and efficient energy management in the energy community.

This paper proposes a tool for the optimal real-time management of the resources of a renewable energy community. According to Italian Regulations, the renewable energy community members are fed by distribution networks connected to the upstream high-voltage network through the same substation. The community includes renewable energy systems, battery energy storage systems, and controllable and non-controllable loads. The proposed approach implements the optimal operation within a probabilistic framework and is aimed at maximising the community's shared energy, providing demand response to the distribution network, and providing available capacity for delivering services to the distribution grid and/or to the upstream grid. Numerical applications allow testing of the proposed real-time control tool showing its potential in terms of monitoring and control of the resources and of the required services.

A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms. Its emergence provides a new solution to the problems of energy utilization and environmental pollution. To coordinate the integrated demand response and uncertainty of renewable energy generation (RGs), a data-driven two-stage distributionally robust optimization (DRO) model is constructed. A comprehensive norm consisting of the 1-norm and infinity-norm is used as the uncertainty probability distribution information set, thereby avoiding complex probability density information. To address multiple uncertainties of RGs, a generative adversarial network based on the Wasserstein distance with gradient penalty is proposed to generate RG scenarios, which has wide applicability. To further tap the potential of the demand response, we take into account the ambiguity of human thermal comfort and the thermal inertia of buildings. Thus, an integrated demand response mechanism is developed that effectively promotes the consumption of renewable energy. The proposed method is simulated in an actual CIES in North China. In comparison with traditional stochastic programming and robust optimization, it is verified that the proposed DRO model properly balances the relationship between economical operation and robustness while exhibiting stronger adaptability. Furthermore, our approach outperforms other commonly used DRO methods with better operational economy, lower renewable power curtailment rate, and higher computational efficiency.

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Within the context of renewable energy communities, this paper focuses on optimal operation of producers equipped with energy storage systems in the presence of demand response. A novel strategy for optimal scheduling of the storage systems of the community members under price-volume demand response programs, is devised. The underlying optimization problem is designed as a low-complexity mixed-integer linear program that scales well with the community size. Once the optimal solution is found, an algorithm for distributing the demand response rewards is introduced in order to guarantee fairness among participants. The proposed approach ensures increased benefits for producers joining a community compared to standalone operation.

Energy-extensive industrial users have high emission attributes and also have considerable demand response potential. This paper presents a low-carbon economic dispatch of industrial community considering the demand response of energy-extensive industrial loads. Based on their operational characteristics, the energy-extensive industrial loads are developed for demand response. While the steel load is divided into continuous and intermittent load, the cement load is divided according to its raw material and clinker. Considering the aluminum smelting from electrolysis process, the electrolytic cell is the main power consumption determined by electrolysis temperature and current. Based on the spatial-temporal distribution characteristics of air pollutants and the environmental policy, the environmental protection costs of thermal power units and energy-extensive industrial loads are calculated to formulate a low-carbon economic objective function. Case studies on an industrial community is carried out to show its effectiveness and superiority on cost and carbon emission reductions.

Community energy consumption is a crucial aspect of the overall societal energy consumption landscape. The allocation rate of distributed photovoltaic (PV) systems within communities is steadily increasing. However, managing and optimizing the consumption of PV resources while mitigating the impact of their inherent randomness and volatility, along with minimizing electricity costs, presents a significant challenge. This paper proposes a mechanism for community energy sharing that utilizes rooftop PV systems, energy storage systems, and bi-directional electric vehicles. To achieve the goal of finding the minimum cost of electricity in the worst scheduling scenarios, a two-stage robust optimization model is established. This model considers the two-sided uncertainty of source and load as well as flexible load demand response. The simulation outcomes prove the proposed method's efficacy in substantially mitigating residential electricity costs and enhancing PV utilization. Notably, during peak summer demand, a substantial 24.78% reduction in electricity costs was achieved, while PV utilization witnessed a significant 16.52% increase.

A multi-carrier energy system (MCES) is a system that combines multiple energy sources to supply electricity, heating as well as colling. Energy management has become a critical issue fashionable the development then processes of smart cities, prompted through the increasing need for vigor. Simultaneously, demand response programs (DRPs) consume garnered considerable courtesy fashionable recent years due towards their substantial influence happening the electricity scheme. This study presents a method designed to reduce operating expenses then ecological emissions in a smart city dependent on grid-connected energy. A revolutionary green energy preparation system aimed at a multi-carrier energy community is proposed to attain maintainable growth. The suggested strategy prioritizes the optimization of renewable energy source use. This technique aims to improve system dependability by tackling the inherent uncertainty associated with green energy supplies. The chief aim of this effort is to speech the complex issue of power organization fashionable a smart city, including the doubts related with renewable energy sources then the incorporation of demand response programs (DRPs). To tackle these difficulties, the paper utilizes the integrated inside the Python program, while doubts fashionable the scheme is demonstrated using the Monte Carlo technique. The findings indicate that accounting for the variability of green energy supplies may decrease the overall system cost by 504 $ via the use of Demand Response Programs (DRP).

Formation of “duck curve” is becoming a concerning topic in the power industry. This causes the power systems to dispatch expensive power generation during peak hours, and creates a burden to the utility in power system planning and operation. Although community microgrid is an effective solution, the cost of deployment becomes a barrier. The costliest component of the microgrid is the energy storage. To overcome this problem, a smart demand response technology is introduced at the planning stage to size the battery economically. Smart demand response technology consists of two parts, as forecasting model to predict the demand curve and real time pricing model to guide the consumers to follow the expected demand curve. Expected demand curve is obtained through peak clipping of the predicted demand curve by a predetermined percentage. Real time pricing is implemented based on the expected demand curve, which allowed to reduce the battery size.

This paper deals with the optimal scheduling of prosumers equipped with energy storage facilities within renewable energy communities, and proposes a novel strategy for optimizing storage usage within a price–volume demand response framework. The problem is formulated as a scalable, low-complexity mixed-integer linear program. Furthermore, a heuristic procedure is introduced to ensure redistribution of demand response rewards among participants according to their contribution to achieving demand–response goals. The proposed approach is designed to enhance the benefits for prosumers operating within a community compared to running independently.

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With the appearance of energy communities and the proposal of novel energy management models for these communities, it is imperative the proposal of novel deployment infrastructures that could accommodate the needs of energy communities. The deployment of models in pilot sites and energy communities is needed to enable the correct testing and validation of models. Therefore, this work presents a distributed energy community infrastructure based on software containers, known as CARAVELS. The system is designed to support energy communities by enabling the deployment, testing, and validation of novel energy management models. The platform was tested in a virtual energy community consisting of 7 real buildings/members and 20 simulated buildings/members, demonstrating its capability to deploy and run energy models in real-world settings. The results suggest that CARAVELS is effective for managing energy in energy communities.

Energy Communities have emerged as a key mechanism for promoting citizen participation in the energy transition. In Greece, recent legislation replaced the virtual net-metering scheme with a virtual net-billing framework, introducing new economic and regulatory conditions for shared renewable energy investments. This study develops an optimization tool for determining the optimal PV system size and Demand Response actions for individual EC members under this new framework. The model is constructed to align closely with the current regulatory and legal context, incorporating technical, economic, and policy-related constraints. It uses real electricity production and consumption data from existing Greek ECs, as well as 2024 Day Ahead Market prices, grid fees, and surcharges. The results emphasize the importance of customized sizing strategies and suggest that policy refinements may be needed to ensure equitable participation and maximize community-level benefits.

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A community integrated energy system (CIES) with an electric vehicle charging station (EVCS) provides a new way for tackling growing concerns of energy efficiency and environmental pollution, it is a critical task to coordinate flexible demand response and multiple renewable uncertainties. To this end, a novel bi-level optimal dispatching model for the CIES with an EVCS in multi-stakeholder scenarios is established in this paper. In this model, an integrated demand response program is designed to promote a balance between energy supply and demand while maintaining a user comprehensive satisfaction within an acceptable range. To further tap the potential of demand response through flexibly guiding users energy consumption and electric vehicles behaviors (charging, discharging and providing spinning reserves), a dynamic pricing mechanism combining time-of-use and real-time pricing is put forward. In the solution phase, by using sequence operation theory (SOT), the original chance-constrained programming (CCP) model is converted into a readily solvable mixed-integer linear programming (MILP) formulation and finally solved by CPLEX solver. The simulation results on a practical CIES located in North China demonstrate that the presented method manages to balance the interests between CIES and EVCS via the coordination of flexible demand response and uncertain renewables.

Demand response (DR) strategies are recieving much attention recently for their applications in the residential sector. Electric vehicles (EVs), which are considered to be a fairly new consumer load in the power sector, have opened up new opportunities by providing the active utilization of EVs as a storage unit. Considering their storage capacities, they can be used in vehicle-to-grid (V2G) or vehicle-to-community (V2C) options instead of taking power in peak times from the grid itself. This paper suggests a community-based home energy management system for microgrids to achieve flatter power demand and peak demand shaving using particle swarm optimization (PSO) and user-defined constraints. A dynamic clustered load scheduling scheme is proposed, including a method for managing peak shaving using rules specifically designed for PV systems that are grid-connected alongside battery energy storage systems and electric vehicles. The technique being proposed involves determining the limits of feed-in and demand dynamically, using estimated load demands and profiles of PV power for the following day. Additionally, an optimal rule-based management technique is presented for the peak shaving of utility grid power that sets the charge/discharge schedules of the battery and EV one day ahead. Utilizing the PSO algorithm, the optimal inputs for implementing the rule-based peak shaving management strategy are calculated, resulting in an average improvement of about 7% in percentage peak shaving (PPS) when tested using MATLAB for numerous case studies.

In recent years, user-side energy storage has begun to develop. At the same time, independent energy storage stations are gradually being commercialized. The user side puts shared energy storage under coordinated operation, which becomes a new energy utilization scheme. To solve the many challenges that arise from this scenario, this paper proposes a community power coordinated dispatching model based on blockchain technology that considers shared energy storage and demand response. First of all, this paper analyzes the operating architecture of a community coordinated dispatching system under blockchain. Combined with the electricity consumption mode of communities using a shared energy storage station service, the interactive operation mechanism and system framework of block chain for coordinated dispatching are designed. Secondly, with the goal of minimizing the total cost of coordinated operation of the community alliance, an optimal dispatching model is established according to the relevant constraints, such as the community demand response, shared energy storage system operation and so on. Thirdly, the blockchain application scheme of community coordinated dispatching is designed, including the incentive mechanism based on the improved Shapley value allocation coordination cost, and the consensus algorithm based on the change rate of users’ electricity utilization utility function. Finally, the simulation results show that the proposed community coordinated dispatching strategy in this paper can effectively reduce the economic cost, reduce the pressure on the power grid, and promote the consumption of clean energy. The combination of the designed cost allocation and other methods with blockchain technology solves the trust problem and promotes the innovation of the power dispatching mode. This study can provide some references for the application of blockchain technology in user-side energy storage and shared energy storage.

The reduction of CO2 emissions is a critical imperative in the pursuit of sustainable energy solutions. A viable avenue for mitigating CO2 emissions within the power sector is adopting energy communities, which empowers communities to achieve decentralization and sustainability. Furthermore, the convergence of energy communities and machine learning algorithms represents a crucial boundary in developing contemporary energy systems. In this context, this paper implements a demand response-based model to balance the energy community’s consumption and generation, considering the support of unsupervised learning algorithms and the active participation of the members in the planned demand response events. The planning of a demand response event is based on a ranking obtained by a set of unsupervised learning evaluation metrics that evaluate the members’ data. This work’s novelty consists of analyzing the impact of these metrics on the ranking of members and, consequently, on the demand response event’s planning. For that, different combinations of these metrics will be considered during the members’ ranking. The reliability of this approach was assessed using an energy community consisting of 50 buildings. The results show that an energy community could reduce 1.1 kg of CO2 emissions and increase sustainability by 13% through implementing the presented model.

With the advent of the Distributed Energy Resources within smart grid systems, traditional demand response management (DRM) models need to be redesigned to capture prosumers’ energy consumption requests and dynamic behavior within the energy market. In this paper, a coalitional DRM model is introduced based on the principles of Game Theory and reinforcement learning to dynamically determine prosumers’ formation in local energy trading communities and their optimal energy consumption. A hedonic game-theoretic model is introduced to enable prosumers to autonomously and dynamically select an energy trading community considering the partially available information regarding prosumers’ energy generation and consumption characteristics and utility companies’ provided rewards per community. Then, a log-linear reinforcement learning model is proposed to enable each prosumer to distributedly determine their optimal energy consumption. A detailed evaluation of the proposed coalitional DRM model is performed based on real data collected from the southwest area of the USA.

Due to the uncertain nature of renewable sources (RS), microgrids (MGs) are becoming inefficient and unreliable. Further, battery energy storage systems (BESS) ensure uninterrupted power supply in MGs. However, integrating BESS can increase MG's operating costs and make the system uneconomical. This paper proposes an optimal energy management (OEM) algorithm that can minimize MG's operating and maintenance (O&M) cost and improve the system's efficiency. In addition, to improve the system's reliability, the demand response (DR) strategy is integrated with the proposed OEM, i.e., OEM-DR. This strategy focuses on maximizing the utilization of RS and minimizing the dependency of MG on a grid, along with fulfilling the objectives of OEM. The formulated objective function is solved using linear programming (LP). In order to evaluate the efficacy of these strategies, the analysis is performed with the real-time data of three seasons, i.e., summer, winter, and rainy. The real-time data is obtained by the MG installed at the commercial building of BITS, Pilani, India. It is found from the results that with the OEM-DR, a significant decrement in operating cost, as well as power imported from the grid, is achieved.

The expected development and massification of Local Energy Markets (LEM), in particular the ones associated with Renewable Energy Communities, poses new challenges, and requires new operations strategies to their promoters, aggregators, and end-consumers. One of the mechanisms that can be used to speed up the spreading of this kind of market is the use of Demand Response (DR) programs since they can be designed to increase the community's savings and profits. In this framework, the end customers are induced to change their normal consumption patterns by temporarily reducing and/or shifting their electricity consumption away from periods with low local generation in response to a signal from a service provider, i.e., aggregator. To this purpose, this paper presents an Agent Based Model (ABM) using the Q-Learning mechanism to implement and to simulate a LEM and its interaction with the Wholesale Market (WSM), using also and incentive-based DR program. The overall objective of this design is to decrease average energy costs by moving the demand to periods of large availability of wind or solar resources or to store energy for future use. The developed model was tested considering real data regarding energy consumption and PV generation. The proposed paper describes and discusses the obtained market strategy and the profits that can be obtained with this approach.

Demand response manages energy demand to match available energy in the smart grid. Residential community loads in the smart grid are diverse and flexible. To integrate user preferences with demand response scheduling for residential communities, a centralized demand response scheduling algorithm is proposed. In this paper, user willingness price is first introduced through fuzzy c-means to quantify user preferences. Secondly, a complete residential community DR scheduling algorithm is established based on use preference and mixed integer linear programming to minimize the total energy cost of the residential community. Simulation results show that the proposed algorithm can reduce the total energy cost of the residential community and communication traffic.

The present study proposes clustering techniques for designing demand response (DR) programs for commercial and residential prosumers. The goal is to alter the consumption behavior of the prosumers within a distributed energy community in Italy. This aggregation aims to: a) minimize the reverse power flow at the primary substation, occuring when generation from solar panels in the local grid exceeds consumption, and b) shift the system wide peak demand, that typically occurs during late afternoon. Regarding the clustering stage, we consider daily prosumer load profiles and divide them across the extracted clusters. Three popular machine learning algorithms are employed, namely k-means, k-medoids and agglomerative clustering. We evaluate the methods using multiple metrics including a novel metric proposed within this study, namely peak performance score (PPS). The k-means algorithm with dynamic time warping distance considering 14 clusters exhibits the highest performance with a PPS of 0.689. Subsequently, we analyze each extracted cluster with respect to load shape, entropy, and load types. These characteristics are used to distinguish the clusters that have the potential to serve the optimization objectives by matching them to proper DR schemes including time of use, critical peak pricing, and real-time pricing. Our results confirm the effectiveness of the proposed clustering algorithm in generating meaningful flexibility clusters, while the derived DR pricing policy encourages consumption during off-peak hours. The developed methodology is robust to the low availability and quality of training datasets and can be used by aggregator companies for segmenting energy communities and developing personalized DR policies.

The community-integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention. A scheduling model based on chance-constrained programming is proposed for integrated demand response (IDR) enabled CIES in uncertain environments to minimize the system operating costs, where an IDR program is used to explore the potential interaction ability of electricity–gas–heat flexible loads and electric vehicles. Moreover, power to gas (P2G) and microgas turbine (MT), as the links of multienergy carriers, are adopted to strengthen the coupling of different energy subsystems. Sequence operation theory and linearization methods are employed to transform the original model into a solvable mixed-integer linear programming model. The simulation results on a practical CIES in North China demonstrate an improvement in the CIES operational economy via the coordination of IDR and renewable uncertainties, with P2G and MT enhancing the system operational flexibility and user comprehensive satisfaction. The CIES operation is able to achieve a tradeoff between the economy and system reliability by setting a suitable confidence level for the spinning reserve constraints. Besides, the proposed solution method outperforms the hybrid intelligent algorithm in terms of both optimization results and calculation efficiency.

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.

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.

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.

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.

Recently, the increasing availability of renewable energy plants has changed the market of electrical energy. The concept of energy community enables prosumers to exploit and exchange the energy produced locally and reduce the need for external energy sources. This can help to obtain significant cost savings and increase the percentage of green energy. In this paper, we present the Cascade model, which aims to achieve a twofold goal: compute an energy schedule that satisfies the needs of single prosumers, and maximize the energy sharing at the community level, thus minimizing the overall cost. The Cascade model partitions the prosumers in groups: at each step, an optimization problem is solved for all the users of a group. The solution enables defining a super-user that summarizes the energy requirements of the groups considered before. Then, a new group is considered in the next step, and so on, until all the groups have been processed. This approach enables preventing the exponential increase in computing complexity that is inevitable when all the prosumers are considered together, using the model referred to as Unified. Experimental results show that the Cascade model leads to a great reduction of computing time, while the overall cost closely approximates the optimal solution ensured by the Unified model.

In response to increasing privacy concerns and the limitations of traditional optimization methods in decentralized energy systems, this paper proposes a scalable and privacy-preserving community energy trading framework based on multi-agent reinforcement learning (MARL). A unified prosumer model is constructed, incorporating diverse distributed energy resources (DERs), and a local trading mechanism is developed using mid-market rate (MMR) pricing and a peak-valley penalty scheme. The community trading process is formulated as a partially observable Markov decision process (POMDP), and a mean-field soft actor-critic (MF-SAC) algorithm is introduced to improve scalability and address non-stationarity by approximating agent interactions with aggregate market statistics. Extensive case studies on real-world datasets demonstrate that the proposed framework achieves up to 54.32% faster convergence, reduces daily operational costs by 50.34%, and substantially enhances peak shaving and valley filling effectiveness, underscoring its suitability for scalable and efficient community energy management.

Urban smart-city transitions increasingly depend on distributed, digitally coordinated, and reliable energy infrastructures. Among these, community microgrids supported by cloud-based peer-to-peer (P2P) coordination provide a practical pathway for integrating distributed energy resources, reducing transaction costs, and improving operational flexibility. This paper presents a structured cloud-coordinated P2P energy-sharing framework for microgrid energy systems, with explicit emphasis on cost reduction, system reliability, and scalable energy management for urban communities. The framework integrates distributed energy resources (DERs), a cloud-based microgrid energy management system (EMS), a peer-Multi Agent System (p-MAS) for peak-load coordination, and Modelling Leveraging Agents (MLA) for bill estimation and prosumer settlement. Internal market operation is governed by a supply-demand-ratio (SDR) pricing rule, while a performance measure is defined to evaluate the realized benefit of energy sharing within the Energy Shared Region (ESR). The empirical study uses the India Residential Energy Survey (IRES) 2020 and associated Microgrid Load Explorer profiles, covering more than 10,000 households in 500+ villages, 50+ districts, and 10 states. Experimental comparisons are conducted against established P2P settlement models, including Bill Sharing (BS), Mid-Market Rate (MMR), and SDR. The source-grounded results indicate that the proposed cloud-based P2P model improves consumer-side cost efficiency and reliability, achieving an overall performance increase of approximately 5% and consumer cost savings of about 8% relative to comparator methods. Reported operational outcomes further indicate that shared energy coordination increases effective ESS utilization from 125 MW per hour without sharing to approximately 200.5–300.5 MWh with sharing, while the model maintains stable operation under node failure conditions. The study demonstrates that cloud-enabled P2P microgrid coordination is a relevant smart-city energy strategy for resilient, data-driven, and economically efficient urban development.

Extreme peak power demand is a major factor behind high electricity prices, despite occurring only for a few hours annually. This peak demand drives the need for costly upgrades for the network asset, which is ultimately passed on to the end-users through higher electricity network tariffs. To alleviate this issue, we propose a solution for cost-effective peak demand reduction in a local neighbourhood using prosumer-centric flexibility and community battery storage (CBS). Accordingly, we present a CBS sizing framework for peak demand reduction considering receding horizon operation and a bilevel program in which a profit-making entity (leader) operates the CBS and dynamically sets mark-up prices. Through the dynamic mark-up and real-time wholesale market prices, the CBS operator can harness the demand-side flexibility provided by the load-shifting behaviour of the local prosumers (followers). To this end, we develop a realistic price-responsive model that adjusts prosumers' behaviour with respect to fluctuations of dynamic prices while considering prosumers' discomfort caused by load shifting. The simulation results based on real-world data show that adopting the proposed framework and the price-responsive model not only increases the CBS owner's profit but also reduces peak demand and prosumers' electricity bills by 38% and 24%, respectively.

Energy storage systems (ESS) can provide flexible and reliable energy management due to the rapid development of distributed renewable energy systems (RES). Second-life batteries (SLB) can overcome the environmental concerns and high investment costs related to fresh battery production. However, the integration of storage-based RES poses several challenges regarding optimal control strategy, power quality, and participation in the energy market. These questions can be listed as follows: Which economic strategy favors the integration of SLB-based storage? Which policy among the individual financial strategies can further improve the decision criteria in favor of the prosumer without causing power quality issues? Which measures should be taken, and which hybrid incentive mechanism should be proposed to complement the lacking feasibility performance of individual economic plans? To fill this gap, this study evaluates the feasibility of different incentive policies, individual or hybrid, considering prosumers’ self-consumption. Moreover, sensitivity analyses consider carbon tax (CT) and investment subsidies (INVs) for prosumers. The results show that the high purchase price of SLBs can be eliminated, provided that the 20% above investment subsidy for prosumers purchasing cheaper electricity. Adopting $1\,{\$}/\text{t}$ CT could reduce carbon emissions by up to 1.9 t/yr, and a 1% total investment subsidy could increase photovoltaic panel (PV) capacity by 11.28 kW. The prosumer benefit under net metering can be maximized if the total INV and CT are managed at 20% and $40\,{\$}/\text{t}$ in Türkiye. This study encourages investors and prosumers to sensibly plan a shared ESS in individual and hybrid incentive mechanisms.

No abstract available

Considering the differentiated characteristics of prosumers in new power systems, the high importance of privacy in energy trading and the limitations of traditional model-based optimization methods within multiple uncertainties, this paper proposes a multi-agent reinforcement learning method with differentiated characteristics and privacy preservation for energy management. Firstly, the differentiated characteristics of prosumer are analyzed and corresponding typical prosumer models are established. Secondly, based on the community market structure, a community energy trading model based on the mid-market rate pricing is constructed. Finally, taking market benefits and operating costs as optimization objectives, the energy trading optimization of prosumers participating in community energy trading is constructed into a partially observable Markov decision process. Aiming at the non-stationary problem of multi-agent environment, this paper proposes to approximate the central Q function of the soft actor-critic algorithm by the mean-field approximation mechanism. The proposed algorithm is then employed to obtain the prosumers’ energy management decision. Results of case study show that the proposed algorithm has outstanding advantages in aspects of convergence, efficiency and economy in energy management within community market considering differentiated characteristics and privacy preservation.

This paper presents a framework of transactive energy marketplace (TEM) in a community microgrid with multiple prosumers as market participants for facilitating peer-to-peer (P2P) energy trading with demand response consideration. The internal market prices are determined using generation-to-demand ratio with the help of transactive energy market operator (TEMO). The TEMO is a non-profited retail energy manager that aims to enable prosumers to participate in the TEM, and to sustain equilibrium between community generation and demand. The participants in TEM are encouraged to trade their net demand with the neighborhood besides the upstream utility. The interactions between TEMO and prosumers are modeled with the game-theory. The optimal time slots for the schedulable loads of the prosumers in TEM are obtained using genetic algorithm while optimizing the energy bills. With the presented system, the reduction in the community energy bills is in the range of 18-52% in the P2P marketplace compared to conventional peer-to-grid (P2G) marketplace under different scenarios.

The uncertainties from the distributed energy resources (DERs) and the prosumer’s heterogeneous characteristics bring great challenges to the operation of the community energy market. In this paper, the real-time energy sharing and management in the community market are decomposed into two-layered sub-problems: the household appliance scheduling problem and the internal energy trading pricing problem. A hierarchical deep reinforcement learning (HDRL) based scheme is proposed for the community energy trading with multiple households, containing a two-stage learning process. In the inner stage, a multi-agent deep reinforcement learning (MADRL) based approach is developed to learn the real-time appliances scheduling policy based on the local observations and given internal electricity price in a decentralized way. In the outer stage, a deep reinforcement learning (DRL) based pricing approach is proposed to determine the real-time internal electricity prices based on the participants’ historical net power and external energy supplier’s electricity prices. The scheduling policy and households’ state are not required in the pricing process. Finally, the simulations with real-world datasets demonstrate that the internal energy trading can significantly reduce the prosumers’ daily cost and the control performance of the proposed scheme is superior to the existing studies.

Technological developments in the electricity sector and the shift towards more prosumer-centric market structures enable the emergence of local electricity markets. In this study, the possibility of developing an energy exchange pertaining a network of prosumers belonging to a local community is investigated. In addition, the influence of heterogeneous prosumers' preferences on the feasibility and viability of local market structures is of interest. To this end, a dataset with highly granular measurements of electricity consumption and production of residential end-users is used. Moreover, survey data are used in order to identify behavioral characteristics and preferences regarding electricity and to perform prosumer segmentation. The results of the case study indicate that despite the heterogeneity of preferences, similar participant objectives might emerge, reinforcing the feasibility of local energy exchanges.

No abstract available

Optimal day-ahead scheduling for a system-centric community energy management system (CEMS) is proposed to provide economic benefits and user comfort of energy management at the community level. Our proposed community includes different types of homes and allows prosumers to trade energy locally using mid-market rate (MMR) pricing. A mathematical model of the community is constructed and the optimization problem of this model is transformed into an MILP problem that can be solved in a short time. By solving this MILP problem, the optimization of the overall energy cost of the community and satisfaction of the thermal comfort at every home are achieved. For comparison, we also establish two different CEMSs for the same community: a prosumer-centric CEMS and no CEMS. The simulation results demonstrate that the community with the proposed CEMS has the lowest daily energy cost among three CEMSs. In particular, the community with the proposed CEMS only has 78%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$78\%$$\end{document} of the daily energy cost of the community with the prosumer-centric CEMS. Moreover, by using linear transformation, the computational time of the optimization problem of the proposed system-centric CEMS is only 118.2 s for a 500-home community, which is a short time for day-ahead scheduling of a community. We finally investigate the trade-off of the MMR pricing in the local energy trading of the community, which allows the profits of different types of homes to be flexibly adjusted.

In this study, we proposed a mixed-integer linear programming model to determine the optimal trading and operational strategies necessary to enable efficient peer-to-peer (P2P) energy trading and resource utilization within fully cooperative community microgrids. The proposed model considers tiered utility tariffs accounting for (i) the time-of-use (TOU) rate and (ii) the level of cumulative consumption. Given the heterogenous mix of prosumers and consumers common in community microgrids, the proposed model seeks to provide decision support for the optimal utilization of generated electricity by determining if it should be self-consumed, stored for future use, curtailed, or traded with peers. Likewise, the proposed approach determines operational strategies for non-prosumer peers with regards to sourcing electricity to satisfy their respective energy deficits. The model presents a scalable approach for energy cost savings for both prosumers and energy consumers regardless of their role in the peer market. To demonstrate this functionality, we leverage the proposed model to solve for the optimal trading strategy within a 5-building community microgrid. Real-world energy demand and generation data pertinent to 5 households in the New York region was sampled using the Pecan Street Inc. Dataport database. Results were compared to that of a traditional centralized grid model. The results highlight the benefits of P2P market design in comparison with the traditional unidirectional grid model. In addition, the outcomes underline that energy consumers satisfy most of their demand from the P2P market during peak hours to obtain greater cost savings.

Energy sharing between energy community members should be fair and supportive. Prosumers and consumers can effectively interact within the local energy community (LEC), while a fair energy-sharing mechanism is required to guarantee market liquidity. From the prosumer's point of view, the power injection to the LEC is more attractive and profitable than selling the energy surplus to the grid. However, the consumers can preserve their energy demand at lower prices with the contribution of the prosumers at the LEC level. In this work, a fair mechanism for energy sharing between peers at the LEC level is developed to deal with the contributions of the prosumers in providing energy surplus and the net demands of each consumer. The community energy management system (CEMS) allocates energy between sectors. The developed strategy for the energy sharing mechanism considers behind-the-meter (BTM) energy management to enhance the financial liquidity in the pool energy market framework. Different performance indicators have been suggested to compare the functionality of the sharing energy mechanism developed in this work.

The global pursuit of cleaner energy alternatives has significantly accelerated the integration of renewable sources into modern power systems. Among them, solar photovoltaic (PV) technology has emerged as a key driver of decentralized generation due to its accessibility, modular design, and environmental advantages. This study introduces a novel framework for local photovoltaic energy markets, focusing on distributed prosumer interactions and autonomous energy management. We develop and implement a modular system architecture designed for deployment in embedded platforms, enabling smart energy management at the household and community level. The architecture supports decentralized control, localized decisionmaking, and energy exchange between prosumers, forming the foundation of a market-based energy ecosystem. To assess the system's feasibility and performance, we employ the Ausgrid dataset, a comprehensive source of real consumption and generation profiles from the Australian distribution network. Using this data, we conduct detailed simulations that replicate the behavior of a local energy market under realistic conditions, capturing interactions among distributed agents and evaluating energy flows, storage coordination, and pricing dynamics. The findings validate the proposed architecture as a viable solution for implementing energy communities capable of operating under market-based principles. This work contributes to the advancement of decentralized energy models, supporting transitions toward resilient, participatory, and low-carbon power systems.

Urban microgrids are evolving into socially coupled energy systems in which prosumer decisions are shaped by both market incentives and peer influence. Conventional optimization approaches overlook this behavioral interdependence and offer limited adaptability under environmental disturbances. This study develops a behaviorally embedded multi-agent optimization framework that integrates social influence propagation with physical power network coordination. Each prosumer’s decision process incorporates economic, comfort, and behavioral components, while a community operator enforces system-wide feasibility. The resulting bilevel structure is formulated as an equilibrium problem with equilibrium constraints (EPEC) and solved using an iterative hierarchical algorithm. A modified 33-bus urban microgrid with 40 socially connected agents is assessed under stochastic wildfire ignition and propagation scenarios to evaluate resilience under hazard-driven uncertainty. Incorporating behavioral responses increases welfare by 11.8%, reduces cost variance by 9.1%, and improves voltage stability by 23% compared with conventional models. Under wildfire stress, socially cohesive agents converge more rapidly and maintain more stable dispatch patterns. The findings highlight the critical role of social topology in shaping both equilibrium behavior and resilience. The framework provides a foundation for socially responsive and hazard-adaptive optimization in next-generation human-centric energy systems.

Following recent Danish legislation promoting energy communities, we explore how to enable these communities to provide grid services to distribution system operators. In particular, we focus on"capacity limitation services", where we propose a bilateral agreement in which an energy community is given reduced grid import tariffs by setting a cap to its consumption level in certain hours. This requires a coordination mechanism between the community manager and the prosumers within the community. We enable this coordination by developing a bilevel optimization model to be solved by the community manager, aiming to set dynamic, i.e., time- and prosumer-differentiated, prices. This coordination mechanism enabled by dynamic pricing ensures desirable market properties including budget balance for the community manager and individual rationality for prosumers, while encouraging (but not guaranteeing) a fair allocation of collected benefits among prosumers.

This work models a distributed community-based market with diverse assets (photovoltaic generators and energy storage systems), accounting for network constraints and adopting the relaxed branch flow model. The market is modeled in a single and fully distributed approach, employing the alternating direction method of multipliers (ADMM) to prevent voltage and line capacity problems in the community network and improve data privacy and reduce the communication burden. Different scenarios, based on the penalty term and the agents' number, are tested to study the efficiency of the algorithm and the convergence rate of the ADMM distributed model. The proposed method is tested on 10-bus, 22-bus, and 33-bus medium voltage radial distribution networks, where each node contains a large prosumer with one or several assets. One important conclusion is that the implemented residual balancing technique improves the efficiency of the ADMM distributed algorithm by increasing the convergence rate and reducing the computational time.

Growing energy demand, developments in renewable energy and grid modernization has encouraged the consumers to participate as prosumers in the energy market using their localized generation. A prosumer is customer who not only consumes energy but also share its surplus with other customers in the locality or with the grid. Recently, prosumers based community concept has gained ample importance to reduce supply-demand gap within the community. Realizing the importance of prosumers based community this paper is an attempt to present a hardware model of energy sharing among prosumers within a community of three consumers/prosumers. The model has successfully implemented energy sharing concept among consumers/prosumers to fulfill community energy demand.

The term “energy prosumer” refers to a new job category that has emerged with the advent of distributed energy production via residential and commercial photovoltaic (PV) applications. The conventional distinction between energy providers and consumers becomes muddled as a result. For the procedure to be automated, blockchain technology has been used. This will make it easier for consumers, prosumers, and utilities to deal directly with energy safely, conveniently, and cost-effectively. This research aims to provide an agent-based modeling (ABM) simulation framework for energy exchange, exhibiting the anticipated power profiles of families and illustrating the operation of blockchain operations (see Figure. 1). A distributed energy resource (DER) of the transactive energy (TE) type was simulated inside the Education City Community Housing (ECCH) microgrid using a reliable multi-agent framework. Blockchain technology is necessary for this. Current blockchain-based local energy market (LEM) plans depend on precise short-term energy generation forecasts and home consumption to balance supply and demand. The present research evaluated the precision of cutting-edge energy forecasting methods in estimating homes’ energy production and consumption. It examined the effects of forecasting mistakes on market results under various supply scenarios. While LSTM models may provide few forecasting errors, the researchers discovered that the prediction process must be changed for a LEM constructed on a blockchain. Since this study tries to predict the timeline of smart meters generally, it sets itself apart from past investigations.

Prosumer consortium energy transactive models can be one of the solutions for energy costs, increasing performance and for providing reliable electricity utilizing distributed power generation, to a local group or community, like a university. This research study demonstrates the simulation of blockchain based power trading, supplemented by the solar power prediction using MLFF neural network training in two prosumer nodes. This study can be the initial step in the implementation of a power trading market model based on a decentralized blockchain system, with distributed generations in a university grid system. This system can balance the electricity demand and supply within the institute, enable secure and rapid transactions, and the local market system can be reinforced by forecasting solar generation. The performance of the MLFF training can predict almost 90% accuracy of the model as short term ahead forecasting. Because of it, the prosumer bodies can complete the decision making before trading to their benefit.

The concept of "energy prosumer" is becoming an increasingly important part of our energy landscape due to the emergence of distributed energy sources, including photovoltaic (PV) technology. This new phenomenon has blurred the line between energy producers and consumers, creating a new class of prosumers. Blockchain technology has been instrumental in facilitating secure, cost-effective energy transactions between prosumers, consumers, and utilities, automating the process and making it more efficient. In the present study, an agent-based model (ABM) simulation framework for energy exchange was developed, demonstrating the power profiles of households and the operation of blockchain-aware energy transactions. The study 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) works to balance supply and demand by using precise short-term energy generation forecasts and home consumption estimates. To evaluate the accuracy of the current state-of-the-art energy forecasting methods, the researchers conducted a simulation to evaluate the accuracy of energy forecasts in predicting household energy generation and consumption. They examined the impact of forecasting errors on market outcomes under different supply scenarios. The study found that while models using long short-term memory (LSTM) may provide low forecasting errors, the prediction process needs to be modified for a blockchain-based LEM. This study stands out from previous research because it attempts to forecast the timeline of smart meters in general, rather than simply focusing on short-term energy forecasting. This is an important step in the development of more secure and cost-effective energy transactions and will pave the way for a more efficient and seamless energy market.

The aggregation of prosumers into energy communities brings multiple benefits in terms of use of the PV and reducing costs. However, it is also a challenge to determine the sharing cost (installation and operation) for each user. The main contribution of this work is a cost-sharing rule based on the influence that each prosumer has on the total cost of the community. In addition, the proposed sharing factors allow to determine in which equipment of the installation (PV, Battery, Grid connection) is more beneficial for the user to participate. The proposed sharing factors are illustrated on an energy community with ten users under different market energy prices and PV, battery costs. As a main conclusion, it is confirmed that taking part in an energy community is beneficial in terms of total (amortization + operation) savings for each participant.

It is now clear that the future of energy passes through Smart Grids and prosumers, a new energy community, which encourages the reduction of traditional power plants, implementing the distributed generation formed by small self-production plants (micro-grid) by RES, allowing to the private sector to produce clean electricity. This transformation is also made possible by Smart Grids, which combine electronics and digital technologies, as ICT and IoT, to allow 2-way communication between the various points of the network. This allows monitoring, analysis, control and exchange of information in real time between the parties involved, to optimize the efficiency and reliability of the network, consumption and energy costs, for all participants in the energy market. There remains a strong limit linked to consumer interests, which often cannot decide between saving and energy efficiency and above all a lack of guarantee of its desires in terms of consumption and sales. With this paper we want introduce a model of control, through the fuzzy logic, for better prosumer experience, so that it can be helped in an objective way to the definition of its interests, whether economic or well-being for a real improvement of the quality of life.

The impact of renewable energies on the power grid is continuously increasing. Besides the emission-free power generation, the renewable energies often are the cause for congested grids, component failure and costly interventions by the distribution system operators (DSO)and transmission system operators (TSO)in order to maintain grid stability. The scientific community discusses in recent years the usability of distributed energy resources (DER)as flexible devices. However, no approach can be found that actually quantifies the potential flexibility and sets a price to it. The model presented in this paper optimizes the charging operation of an electric vehicle (EV)according to a price signal with a state of the art exhaustive search algorithm. Furthermore, this model offers all possible deviations from the optimal operation as flexibility to a corresponding market platform and sets a price to each offer, which is dependent on the future price level of the energy. With this model, it is possible to offer positive and negative prices for flexibility. The proposed model shows that an exhaustive enumeration algorithm is feasible to calculate flexibility offers, prices and applicable on currently discussed platform models. The example of an EV charging schedule is successfully modelled and described in this paper.

Integrating prosumers equipped with renewable energy resources (RES) and battery energy storage systems (BESS) in local energy markets (LEMs) offers economic benefits, in addition to significant environmental advantages. In this paper, a comprehensive energy management system (EMS) framework for battery scheduling is proposed to optimize the use of distributed energy resources (DERs) among prosumers in smart grid communities. A novel decentralized evolutionary game theory (EGT) algorithm is introduced to enhance energy management while preserving privacy and scalability. The primary objective is to develop an efficient, reliable, private, and scalable algorithm for battery scheduling that ensures economic efficiency and system stability, even under dynamic market conditions. The results show that, with different feed-in tariffs (FIT), the EGT algorithm reduces the standard deviation of hourly prices by 51% and 57% when having 20% and 80% FIT rates, respectively, in prosumer-based smart grid community (SGC), demonstrating its effectiveness in stabilizing market conditions. A comprehensive comparative analysis is conducted between the proposed EGT algorithm and established methods such as centralized optimization (CO), game theory (GT), and auction-based approaches. The comparison focuses on key performance metrics, including peak-to-average ratio (PAR), price volatility, economic efficiency, and computational performance. The results highlight the computational capabilities of the decentralized EGT algorithm in optimizing battery scheduling and effectively mitigating price fluctuations for SGCs with increasing prosumer participation.

Electrical demand in the world especially residential demand is increasing. Instead of building new conventional power plants, distributed generation based on renewable resources are an alternative solution to cope the demand. Simultaneously, government of some countries release their regulation to encourage households be active player in the market. However, in several years now, Feed in Tariff (FiT) is decreased by the regulation. Demand side management strategies especially load shifting are used to reduce the electric cost and trying to handle household satisfaction. To reach the similar goal in the community, coordination of energy management is necessary where customers maximize their self-consumption through PV sharing. Using energy contract and Particle Swarm Optimization it is expected to help the costumers to find their optimal cost, and to reach a maximum self-consumption rate.

No abstract available

This paper presents an advanced control framework for an Integrated Community Energy (ICE) system which includes a unidirectional thermal network and centralized energy generation and storage. This control structure utilizes the concepts of Multi-Agent Systems and Sequential Logic Control, in combination with conventional controllers to form a self-regulating closed loop with the ICE system. The operation of this system is able to satisfy the thermal and electrical demand of different consumers while maintaining system stability and adjusting its own set-points in real time. The strategy implemented by the developed control framework facilitates the reduction of peak electricity use and natural gas heating by approximately 42% and 65%, respectively, resulting in a 62% reduction of GHG emissions.

: The growing diversity of energy demands, combined with the integration of renewable and distributed energy systems, calls for more adaptive energy management strategies. This study presents a comparative simulation-based analysis of three energy management approaches applied to a local energy community: A Multi-Agent System (MAS), a centralized rule-based method, and a game-theory-based optimization using the Alternating Direction Method of Multipliers (ADMM). The MAS approach models buildings, Electric Vehicles (EVs), and energy storage systems as autonomous agents that dynamically allocate energy based on user preferences. Simulations were conducted using the Multi-Agent Simulation Environment (MESA) framework in Python, with a focus on optimizing energy allocation while minimizing costs and ensuring user comfort. This decentralized approach enables each agent to make local decisions while collectively achieving system-wide objectives. The comparison uses the same use case and dataset across all three methods, ensuring methodological consistency and strengthening the reliability of the performance evaluation. The results demonstrate that the MAS approach achieves lower overall energy costs compared to the rule-based method and ADMM in scenarios prioritizing balanced energy distribution and self-sufficiency, where 'balanced' refers to a scenario that equally weighs user comfort, cost, and local renewable usage objectives. The MAS achieves a total community cost of €359.72 per day in the balanced scenario, compared to €395.54 per day for the rule-based approach and €375.94 per day for the ADMM method, representing cost savings of 9.1 and 4.3%, respectively.

No abstract available

The development of the integrated energy technology has given birth to various community integrated energy systems (CIES) in demand side, such as industrial CIES, and commercial CIES. Notably, this CIES usually involves diversified participants with the openness of the retail market and the influx of various social capitals. Belonging to different stakeholders, the involved agents in CIES usually operate noncooperatively according to their own objectives, to maximize their own benefits. In order to improve further the benefits of those various entities as well as balance their interests, this paper proposes a cooperative interaction mechanism for CIES using Nash bargaining theory. In this bargaining-based cooperative game, the involved agents synergistically operate and negotiate with each other, to realize sharing economy. A distributed optimization approach is applied to find the bargaining solution of the cooperative system, to guarantee the autonomous operation and information privacy of the involved agents. Numerical studies demonstrate the validity of the bargaining-based cooperative interaction mechanism, and also show the improvement of the benefits of the system.

No abstract available

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.

The field of voltage regulation and energy management in active distribution networks is rapidly evolving, driven by the integration of advanced technologies and innovative control strategies. This study proposes a decentralised operational framework for distribution networks using a Deep Reinforcement Learning (DRL) approach based on the Soft Actor-Critic (SAC) algorithm. The focus lies in managing uncertainties caused by variable electricity consumption patterns among residential users, while optimally incorporating contributions from community energy systems that integrate wind power, lithium battery storage, and hydrogen storage technologies. Results reveal that the proposed framework allows more heat pumps and electric vehicles in the distribution network. When training was stable, the agents' returned action show proper correlation with input states from environment. Although state forecast is set at 85% accuracy, the model handles power-system unpredictability well.

The traditional centralized optimization method encounters challenges in representing the interaction among multi-agents and cannot consider the interests of each agent. In traditional low-carbon scheduling, the fixed carbon quota trading price can easily cause arbitrage behavior of the trading subject, and the carbon reduction effect is poor. This paper proposes a two-layer dynamic community integrated energy system (CIES) low-carbon collaborative optimization operation method. Firstly, a multi-agent stage feedback carbon trading model is proposed, which calculates carbon trading costs in stages and introduces feedback factors to reduce carbon emissions indirectly. Secondly, a two-layer CIES low-carbon optimal scheduling model is constructed. The upper energy seller (ES) sets energy prices. The lower layer is the combined cooling, heating, and power (CCHP) system and load aggregator (LA), which is responsible for energy output and consumption. The energy supply and consumption are determined according to the ES energy price strategy, which reversely affects the energy quotation. Then, the non-dominated sorting genetic algorithm embedded with quadratic programming is utilized to solve the established scheduling model, which reduces the difficulty and improves the solving efficiency. Finally, the simulation results under the actual CIES example show that compared with the traditional centralized scheduling method, the total carbon emission of the proposed method is reduced by 16.34%, which can improve the income of each subject and make the energy supply lower carbon economy.

Deployment of shared energy storage systems (SESS) allows users to use the stored energy to meet their own energy demands while saving energy costs without installing private energy storage equipment. In this paper, we consider a group of building users in the community with SESS, and each user can schedule power injection from the grid as well as SESS according to their demand and real-time electricity price to minimize energy cost and meet energy demand simultaneously. SESS is encouraged to charge when the price is low, thus providing as much energy as possible for users while achieving energy savings. However, due to the complex dynamics of buildings and real-time external signals, it is a challenging task to find high-performance power dispatch decisions in real-time. By designing a multi-agent reinforcement learning framework with state-aware reward functions, SESS and users can realize power scheduling to meet the users’ energy demand and SESS’s charging/discharging balance without additional communication, so as to achieve energy optimization. Compared with the baseline approach without the participation of the SESS, the energy cost is saved by around 2.37% to 21.58%.

EV charging optimization with utilization of decentralized renewable energy resources can be seen as a promising tool towards domestic EV fleet decarbonization. However, optimization is usually conducted on an individual household level, and community-driven approaches with shared resources are heavily underexplored. In this work, a community-driven smart EV charging optimization scheme with Multi-Agent Deep Reinforcement Learning is proposed. Compared to existing single-agent approaches, the employment of a multi-agent one allows for concurrent EV charging optimization for each household within a community, deployed in a centralized, shared, energy management system, while utilizing a community-owned solar photovoltaic (PV) panel. Our approach results in reduced cost barriers for domestic EV owners that desire to optimize their charging profile, as it eliminates the investment on an individual PV panel and energy management system. Experimental results on the Pecan Street dataset validate the effectiveness of our approach compared to individual household optimization, resulting in cost savings up to 17.65%, increase in PV power utilization of up to 133.10%, as well as network stress reduction of up to 18.75%.

Highlighting the need to evaluate power system flexibility associated with incorporating variable renewable resources, there is a shift towards leveraging demand-side flexibility. By promoting the collaborative association of consumers and distributed energy resources (DERs) in the form of local energy communities (LECs), flexibility service provision is entrusted to community managers. This paper proposes a data-driven framework suitable for bidding strategy in LECs to enable energy trading and providing flexibility services. Employing a model-free decision-making approach through deep Q network (DQN) methods based on the coordination of multiple agents combines the advantages of manager-centric (centralized) architecture and a data-driven approach. The proposed deep reinforcement learning (DRL) algorithm optimizes the community manager’s bidding in local energy and flexibility markets simultaneously. Moreover, dissatisfaction and operation cost functions are presented to increase flexible user engagement incentives. Results of test cases prove that designing a LEC where flexibility bids are integrated with day-head (DA) energy scheduling mitigates the imposed agent’s costs.

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.

The paper deals with an Energy Management System of a micro-grid feeding a community of several residential buildings. The proposed micro-grid includes a photovoltaic energy production and a storage system based on batteries. The energy management system object is to optimize the main grid energy efficiency. To ensure the concept of management decentralization, we propose in this study the integration of an Artificial Intelligence Algorithm: The Multi Agent Systems. A specific agent is dedicated to each Micro-Grid component. Physical agents using Raspberry Pi are also presented and the communication between the different agents is insured by Wi-Fi technology.The simulation results of Micro-Grid based Multi Agent Systems management are mentioned and a porotype of experimental exchange between two physical agents is also propose.

With the gradual transformation of the integrated energy system to intelligence and decentralization, users with distributed generation gradually replace traditional users. To realize the rationality of multi-agent power transactions in a community-integrated energy system and improve the income of each subject in the system, this paper presents the optimal operation strategy of a community-integrated energy system based on the two-level game. The first layer is a cooperative game between energy communities, which can promote the consumption of new energy in communities. The second layer is a master-slave game between operators and communities, which can promote effective interaction between the upper and lower levels. By using the Karush-Kuhn-Tucker condition, the two-layer model is transformed into a single-layer model and uses McCormick to relax the model. The example verifies that the trading strategy proposed in this paper can effectively improve the benefits of each energy community.

Multi-energy system (MES) has garnered a great deal of attention at the district building level owing to its advantages of unlocking the system flexibility using the coupling across multiple energy sectors. However, the flexibility of MES may not be fully exploited if it is not being used properly. Peer-to-peer (P2P) energy trading that allows intelligent buildings as an individual to engage in flexible energy trading with each other, is regarded as an attractive and productive mechanism to benefit the economic perspectives. On the other hand, introducing emissions trading schemes (ETS) to decarbonize building energy management can additionally enhance the utilization of such highly flexible MES. To approach the above two perspectives simultaneously, this paper raises a Joint P2P energy and Carbon emissions trading mechanism for a building community. In this setup, each individual building is treated as an agent and then models the trading mechanism as a Multi-Agent reinforcement learning paradigm, named as MAJPC. Specifically, an abstracted critic network is introduced by involving the extra information of community net energy and carbon emission during the centralized training process. In experiments, we also compare MAJPC with the other two baseline mechanisms, i.e., multi-agent peer-to-grid energy trading (MAP2G) and multi-agent peer-to-peer energy trading(MAP2P). Experimental results obtained in real-world situations show that MAJPC outperforms MAP2G and MAP2P in realizing economic and environmental benefits, agnostic to the RL algorithm types such as A2C, DDPG and PPO.

With energy costs on the rise and with ever growing concern for environmental impact, energy providers and regulatory bodies have been pushing for dynamic energy prices as a means to encourage load shifting to reduce daily energy demand variance. Coupled with recent advancements in photovoltaics (PV) power generation and Battery Energy Storage System (BESS) technology, this has encouraged the development of energy communities with one of the goals to mitigate the effect of dynamic prices on homeowners’ energy bills without sacrificing comfort, while at the same time utilizing aggregation of Distributed Energy Resources (DER) to contribute to grid flexibility. In this paper, we present Mathematical Optimization and Deep Reinforcement learning for Energy Cost minimization (MODREC), a decentralised Community Energy Management System (CEMS). MODREC leverages Multi-Agent Deep Reinforcement Learning (MADRL) coupled with Linear Programming (LP) to minimize cost in an energy community by intelligently charging and discharging household BESSs while assuming non-elastic consumer loads. MODREC follows an LP-guided training pipeline, where an optimal strategy computed with LP on historical data is employed to train a set of Deep Reinforcement Learning (DRL) agents, each assigned to a household in the community, that minimize a common cost function. Our main contribution lies in the system-level integration of LP-derived expert supervision with decentralized multi-agent control for community energy cost minimization under dynamic pricing. With MODREC, we manage to save up to 30% of energy costs compared to conventional approaches and efficiently shift energy load to off-peak hours.

Microgrids contain different nanogrids with various power capacities and fluctuations in production. An overall strategy for managing power flow between all interconnected nanogrids is needed. This paper presents a multi-agent system approach for energy management among different nanogrids constituting a microgrid. In this paper, using multi-agent systems, the concept of collaborative microgrids with shareable resources is introduced. That allows the householders of an isolated district or community to collaborate and interact with eatch other in order to create a stable and proprietary microgrid. In addition, the proposed strategy emphasizes stockage decentralization, programming facilities for the designer. The results show that this approach is perfectly valid and can respond to most problems of centralized energy management systems while establishing a reliable and robust microgrid.

Energy communities represent a possible way to involve citizens in a more active manner and subsequently improve their behaviour towards energy consumption. We propose in this paper a multi-agent system as a simulation instrument for modeling energy communities, with the aim to investigate the potential of collective action in terms of maximizing performances (such as self-consumption and self-sufficiency). More specifically, the tool presents insights into the economical and environmental collective benefit that would be achieved in an energy community of citizens who respect every recommendation given by an intelligent management system.

No abstract available

This study proposes an intelligent energy management framework based on Constrained Deep Reinforcement Learning (CDRL) tailored for Community Multi-Energy Systems (CMES), aiming to enhance energy efficiency and mitigate carbon emissions by coordinating the optimization of various energy vectors in a synchronized manner. Initially, a multidimensional, continuous, stochastic action and state space constrained Markov Decision Process (CMDP) is employed to model the sequential multi-energy scheduling problem. Subsequently, an intelligent agent is iteratively trained through interactions with the environment, utilizing constrained policy Policy Optimization (CPO) algorithm, to learn the near-optimal system schedule while ensuring near-zero constraint violations. The effectiveness of the proposed approach is then substantiated through numerical validation conducted on authentic datasets, which incorporate electricity consumption records from fifteen households in London, Ontario, Canada, covering the years from 2014 to 2016. The results underline the potential of CDRL in enhancing sustainable energy management practices, offering insights into scalability and application in broader contexts.

Building-to-Grid (B2G) technology is an innovative approach that integrates buildings considered as multi-energy microgrids (MEMs) into the electrical grid, making them not only consumers, but also active participants in managing energy consumption and electricity generation. The paper proposes a model of an intelligent system “autonomous dispatcher” of an urban MEM community within the framework of the B2G concept. The structure of such a model implements learning agents based on multi-agent reinforcement learning methodology to achieve the goals of both the distribution network and the buildings connected to it within a single energy community. The proposed model relies on a multi-agent version of the PPO method to manage building demand and regulate voltage in the distribution network. The effectiveness of the proposed approach is demonstrated by the example of a real urban power district, including various types of buildings with climate control systems and distributed energy generation facilities.

The transition from standalone renewable energy systems to local energy communities requires intelligent management of distributed energy resources. Building upon previous studies on standalone house energy management systems, the approach scales into a configuration where a community of 5 houses is powered by a shared array of PV panels, a common battery storage unit, and the main electrical grid as a centralized backup source. This setup ensures optimal utilization of locally generated energy while maintaining reliability through grid connectivity. The paper proposes an architecture for a community smart grid design and describes an agent-based model designed in Python as a solution for energy management and forecasting. Agent functionalities are described to reflect informed and autonomous decision-making. This analysis provides preliminary insights for continued simulations, developments, and the integration of intelligent predictive algorithms within multi-house microgrids.

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This research describes a multi-agent system model of a microgrid with energy prosumers (such as domestic units) represented as selfish agents- each equipped with fixed and shiftable loads, batteries to store energy, and renewable energy generation units. Each agent modulates its energy usage patterns across multiple time steps to maximize its utility. With the pricing being regulated through an external service provider, the microgrid model is studied under three different scenarios. In the simplest scenario, no energy transaction is allowed so that each agent consumes the energy that it generates. The next scenario allows energy transactions so that agents can sell their surplus energy to other agents. In addition to enabling energy trade, the last scenario also incorporates a community level energy storage facility so that energy can be stored and reused later. With energy balance constraints being imposed on the microgrid to simulate conditions of grid isolation, Nash equilibrium conditions are established within the multi-agent system using a relaxation algorithm. Simulation results shed light on equilibrium energy consumption patterns under each scenario. It demonstrates how the social welfare of the community of prosumers can change under different pricing schemes.

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In this paper, a short-term optimal dispatch method based on multi-agent systems is proposed for the independent community microgrid that contains wind power, photovoltaic power generation, diesel generators, energy storage devices and load. In order to update the result of day-ahead dispatch and minimize the usage of diesel generations, concrete dispatch strategies are made according to the result of the day-ahead dispatch and current state of energy storage devices. The compensation should be classified when considering load-side management. An optimal model of load-shedding distribution is established to minimize power outage loss of users. Finally, an independent community microgrid example is given to verify the validity of the short-term optimal dispatch proposed in this paper.

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

For the current research of game theory in shared energy storage technology needs to be further explored, and the application of intelligent solution algorithms still needs to be further explored. A joint operating and trading mechanism of diverse community energy systems and shared energy storage was proposed. Firstly, diverse community energy systems (CESs) and shared energy storage (SES) joint operating and trading framework was constructed. Secondly, operation and transaction models were constructed for the main participants, i.e. CESs and SES. On this basis, the multi-agent Stackelberg game relationship was analyzed in detail. The optimization framework of deep reinforcement learning algorithm based on actor-critic framework and Deep Deterministic Policy Gradient algorithm was proposed to improve the accuracy of model solving. The effectiveness and applicability of the proposed theoretical method in reducing operation cost and promoting local clean energy consumption were verified by the practical simulation cases.

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