P2P Electricity Research Articles

THE POTENTIAL FOR PEER-TO-PEER ELECTRICITY TRADING IN GEORGIA

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

Optimization of a Bayesian Game for Peer-to-Peer Trading among Prosumers under Incomplete Information via a CNN-LSTM-ATT

In modern low-carbon industrial parks, various distributed renewable energy resources are employed to fulfill production needs. Despite the growing capacity of renewable energy generation, a significant portion of the power produced by these renewable resources remains unconsumed, resulting in a waste of resources. Within an industrial park, microgrids that both generate and consume energy resources act as energy prosumers. Peer-to-peer (P2P) trading provides an efficient means of utilizing renewable energy among these energy prosumers, who possess both power generation and consumption capabilities. However, within the current market mechanism, each prosumer retains private information that is not disclosed on the network. To address the issue of incomplete information among multiple prosumers during the decision-making process, we develop a Bayesian game model based on the CNN-LSTM-ATT prediction method for P2P electricity transactions among multiple prosumers. The energy prosumers in each industrial park aim to minimize their energy consumption costs by adjusting strategies that include P2P energy trading and managing thermal loads. Prosumers make decisions on the basis of their own characteristics and estimates of other prosumer characteristics, which are obtained from the joint probability distribution predicted by the CNN-LSTM-ATT method. These decisions are aimed at minimizing each prosumer's electricity costs. The simulation results demonstrate the effectiveness of the Bayesian game model proposed in this study.

Peer-to-peer energy trading framework for an autonomous DC microgrid using game theoretic approach

Peer-to-peer (P2P) electricity trading has become the next generation of energy management strategies that economically benefits prosumers by trading electricity as goods and services. The P2P electricity market is expected to support the grid to minimize reserve requirements, lower investment and operational costs, reduce peak demand, and improve reliability. This study proposes a peer-to-peer (P2P) energy trading framework for an autonomous DC microgrid. The motivation is to overcome several issues related to P2P reported in the literature: the lack of physical microgrid modeling, absence of an energy management system (EMS) before the P2P trading simulation, and full autonomy of P2P participants. To address these shortcomings, a framework that integrates physical layer (modeling of the microgrid), information layer (EMS), and application layer (P2P trading scheme) is suggested. The P2P market clearance utilizes a non-cooperative game theory incorporating the alternating direction method of multipliers (ADMM) algorithm. To demonstrate the proposed framework, the P2P trading of four households (prosumers) that consists of rooftop PV, local energy storage (LES), and independent community energy storage (CES) is simulated. The objective is to prove the effectiveness of P2P trading in comparison with a framework without it. The MATLAB simulation results show that the system that utilizes P2P trading can reduce the daily overall cost of energy by 47.48 %, that is, from $28.85 (without P2P) to $15.15 (with P2P). This study demonstrated the benefits of P2P energy trading for prosumers and promoted the development of the energy market.

P2P electricity trading model for urban multi-virtual power plants based on double-layer energy blockchain

The urban virtual power plant (VPP) enhances the overall performance of the urban power system by aggregating diverse distributed energy resources (DERs) to provide energy services. With the advancement of smart grid technology, peer-to-peer (P2P) electricity trading has emerged a new paradigm of electricity trading within urban VPPs. In this study, we propose a P2P electricity trading model for multi-VPPs in urban areas, based on a double-layer energy blockchain framework. Initially, users submit their electricity trading information for matching at the user layer; any remaining unfulfilled electricity demand is then further matched at the VPP layer. The user participation process in trading encompasses several stages: trading start, matching, validation, execution, and settlement. Additionally, this study introduces a double-layer blockchain structure consisting of one main chain and multiple side chains. The main chain records real-time trading information from the VPP layer while the side chains are dedicated to recording real-time trading information from the user layer. The feasibility of the proposed model is demonstrated through its implementation in Hyperledger Fabric. Experimental results indicate that the proposed model effectively reduces urban users' reliance on the main grid and decreases their electricity expenses.

Multi-market P2P trading of cooling–heating-power-hydrogen integrated energy systems: An equilibrium-heuristic online prediction optimization approach

In this paper, an equilibrium-heuristic online prediction optimization approach is proposed for multi-market peer-to-peer (P2P) electricity–hydrogen trading of integrated energy systems (IESs) with uncertainties. First, the IES, consisting of a hydrogen energy storage subsystem and a combined cooling, heating, and power subsystem, is constructed in the distribution network to improve energy utilization and market efficiency. Then, a bi-level optimization model for IESs, participating in the P2P electricity–hydrogen energy trading, the real-time electricity, and the ancillary service markets, is proposed, in which the top-level model can be formulated as a P2P electricity–hydrogen trading pricing model through the social welfare maximization problem, and the lower-level model is used to maximize the IES operating profit. To effectively solve the bi-level model, the game equilibrium-based ADMM distributed algorithm is used to obtain the P2P trading volume and prices of the top-level model, and a new hybrid heuristic algorithm, called hybrid sand cat swarm optimization and improved honey badger algorithm (SCIHB), is proposed to solve the lower level model. SCIHB utilizes a hybrid mechanism and an adaptive learning rate parameter to balance local exploitation and global exploration. Furthermore, a new online learning probabilistic prediction method based on the hidden Markov model and wavelet transform is introduced to describe the uncertainties of wind power, electricity prices, and frequency regulation prices. Finally, case studies are conducted on an IEEE 33-bus test system, and the numerical results verify the effectiveness of the proposed P2P trading model and solution approach.

Distributed optimization for joint peer‐to‐peer electricity and carbon trading among multi‐energy microgrids considering renewable generation uncertainty

AbstractThe increasing penetration of renewable energy and the further coupling of the electricity and carbon markets have hindered the realization of efficient and low‐carbon transformation processes in new power systems. This study addresses the optimization problems of joint peer‐to‐peer (P2P) electricity and carbon trading in multi‐energy microgrids (MEMGs), taking into account the risks associated with renewable generation in a distributed manner. First, a coordinated operation model is developed to describe the joint P2P electricity and carbon trading issues among MEMGs, aiming to minimize operating costs, mitigate potential risk losses, and reduce renewable energy wastage. Second, the conditional value‐at‐risk technique, paired with stochastic programming, is employed to quantify potential risk losses arising from uncertainties. Finally, a distributed optimization approach is developed based on the alternating direction method of multipliers to maintain the privacy and independence of decision‐making in individual MEMGs. During the trading processes, the Lagrangian multipliers are used as price signals to ensure fairness in optimal trading schemes among MEMGs. Moreover, a parallel solution mechanism is implemented to improve overall operational efficiency with minimal calculation expenditure. The simulation results demonstrate that the proposed method can reduce operation costs and carbon emissions while also preventing a significant amount of renewable energy abandonment.

Distributed transaction optimization model of multi-integrated energy systems based on nash negotiation

Integrated energy system (IES) can achieve multi energy complementarity, but it still faces the problem of energy surplus or shortage. Therefore, there are demands for energy sharing among different IESs, but how to determine the trading quantity and trading price? To address this confusion, a distributed optimization model for multi-IES participating in peer-to-peer (P2P) transactions based on asymmetric Nash negotiation is innovatively proposed. First, to facilitate the energy sharing of several IESs with different structures, a P2P trading framework is designed. Second, to meet the needs of individual rationality and alliance cooperation at the same time, a distributed transaction optimization model based on Nash negotiation is proposed. Considering the discourse power determined by contribution, an asymmetric bargaining mechanism is designed. Third, to protect the privacy and improve solution performance, the improved adaptive step-size alternating direction multiplier algorithm (ADMM) is used for the distributed sequential solution. Finally, the effectiveness of the proposed trading framework, bargaining mechanism, optimization model, and solution algorithm are verified by the implementation of simulation. The simulation results show that: 1) The proposed model and algorithm can assist managers in determining the quantity and price of P2P electricity transactions. 2) Compared with the independent operation mode, the cooperative operation mode has significantly improved the overall interests and individual interests. 3) In distributed transactions, managers only need to submit limited information, which protects the privacy and security of each agent. 4) The asymmetric Nash negotiation mechanism can measure discourse power based on contribution. 5) The improved ADMM has the advantages of stronger convergence performance and faster solution speed.

Honesty Verification Approach to Address Dishonest Bidding Behavior in Blockchain-Based P2P Electricity Trading

Honesty Verification Approach to Address Dishonest Bidding Behavior in Blockchain-Based P2P Electricity Trading

Power flow traceable P2P electricity market segmentation and cost allocation

This study explores peer-to-peer (P2P) electricity trading, emphasizing not just the export and consumption, but also the feasible physical supply of electricity and the use of distribution network assets. Building on a transaction-oriented dynamic power flow tracing model, a novel P2P market architecture is proposed. This architecture integrates the electricity market with the power network, considering technical constraints, network losses, and asset usage. The network is segmented into potential markets using second-order cone programming (SOCP), with an optimization problem introduced for loss-allocation. This problem merges network physical analysis and variable outputs from distributed energy resources (DERs). A graph-based P2P electricity trading model is designed to determine optimal transaction cost allocation and maximize benefits for both DERs and consumers. A case study on a modified IEEE 33-node test feeder substantiates the benefits of this market structure, demonstrating increased revenues for DERs and reduced bills for consumers compared to traditional feed-in-tariffs.

Risk-averse stochastic capacity planning and P2P trading collaborative optimization for multi-energy microgrids considering carbon emission limitations: An asymmetric Nash bargaining approach

The increasing penetration of renewable energy with stochastic characteristics and continuous refinement of carbon emission policies put forward higher requirements for the construction of new power systems. This paper proposes a risk-averse stochastic capacity planning and peer-to-peer (P2P) trading collaborative optimization method for multi-energy microgrids (MEMGs) considering carbon emission limitations. First, a cooperative operation model for MEMGs considering the capacity planning of distributed generation units, uncertainty from renewable energy generations, carbon emission limitations and P2P electricity trading among MEMGs is formulated. Second, a risk-averse stochastic programming method is applied to avoid the potential risk losses caused by randomness and intermittence of renewable energy. Third, an asymmetric Nash bargaining approach is adopted to ensure the fair allocation of benefits and maintain the willingness of individual MEMGs to participate in cooperation. Then, to protect the privacy of individual MEMGs belonging to different stakeholders, the alternating direction method of multipliers is used to solve the two subproblems in a distributed manner. Meanwhile, to further alleviate the computation burdens, a diagonal quadratic approximation method is applied to linearize the quadratic penalty term in the augmented Lagrangian function and realize the parallel solution of all optimization subproblems. Moreover, the influence of different carbon emission targets on the optimal resource combination strategy of the system is investigated by introducing carbon emission factors. Simulations on different models, strategies, and distributed algorithms are conducted to verify the effectiveness and superiority of the proposed method.

Peer-to-peer trading price and strategy optimization considering different electricity market types, tariff systems, and pricing models

This study investigated the peer-to-peer (P2P) trading price and strategy based on market-driven methods within a residential microgrid consists of two prosumers (i.e., P1 and P2) and one consumer (i.e., C1), which establishes a diverse microgrid trading market in terms of peer characteristics (e.g., system configurations and electrical load profile). Five cases are proposed in this study considering different electricity market types (i.e., peer-to-grid and P2P markets), retail tariff systems (i.e., progressive and time-of-use (ToU) tariff systems) as well as market-driven P2P trading pricing models (i.e., uniform and individual pricing models). Peer electrical load data are collected from three typical four-person households in Hong Kong along with their domestic electrical appliance utilization pattern. The results indicate that the individual pricing model has led to the dynamics of P2P electricity trading price than the uniform pricing model, and P2P trading is more economically efficient under the ToU tariff system. The maximum electricity bill saving of the entire microgrid can be achieved by 31.33% in summer and by 43.02% in winter. Besides, it is observed that the installation of battery energy storage system (BESS) has facilitated the self-consumption ratio of the renewable energy system to 91.98% in summer and 100% in winter. This implies that the BESS plays a pivotal role in improving the flexibility in managing the P2P trading strategy, and enhancing the efficiency in electricity dispatching within the microgrid. This study contributes to the novelty in science since it provides a comprehensive framework that can adapt to changing market conditions.

P2P trading mode for real-time coupled electricity and carbon markets based on a new indicator green energy

The carbon trading market has introduced new opportunities for the low-carbon transformation of China's power industry. To realize real-time coupling of the electric–carbon market at the mechanism level, this study constructs a P2P trading mode that improves economic and environmental benefits through innovative mechanisms. First, we propose a novel indicator, Green Energy, which has multidimensional composite attributes and accurately characterizes the supply of clean and low-carbon energy. Green Energy is used as a matchmaking indicator in the P2P electricity market to increase the rate of renewable energy consumption and encourage emission reduction of fossil energy suppliers. Second, the carbon price is dynamically adjusted according to real-time electricity consumption information and it is transmitted to the electricity market through Green Energy. Third, blockchain cross-chain interoperability technology is introduced to establish a real-time data-sharing network for electricity and carbon co-trading. Moreover, a case study is conducted, and the simulation results show that the proposed method improves social welfare by over 18% compared to the traditional trading mode. And it can improve renewable energy utilization efficiency by 26.78% and reduce carbon emissions by more than 17%. This study provides a reference for the real-time coupled electricity and carbon markets.

AC Network-Constrained Peer-to-Peer Electricity Market Model in Low-voltage Power Distribution Networks

The rapid growth of Distributed Energy Resources (DERs) and demand response in modern power grids calls for decentralized electricity trading mechanisms to effectively induce and coordinate responses from massive DERs and load entities. This paper proposes a fully decentralized peer-to-peer (P2P) distribution electricity trading mechanism backboned by a modified version of Alternating Direction Method of Multipliers (ADMM). Comparing with the state-of-the-art P2P electricity markets, the proposed electricity trading mechanism comprehensively incorporates power network constraints formulated by an alternating current power flow model. In the proposed method, nodal voltages are designed as the only public variables shared among participants. Privacy of each participant is thus sufficiently preserved without exposing the bid data to the others. Meanwhile, the proposed P2P method can also ensure the global optimality of social welfare in electricity transactions. Comprehensive case studies are carried out using the IEEE 33-node distribution system to demonstrate the feasibility and efficiency of proposed P2P electricity trading mechanism. The results indicate that the proposed model can efficiently integrates network constraints in the market clearing process while maintaining computational performance, ensuring stable convergence regardless of distribution system congestion. The proposed P2P electricity market can achieve global maximization of social welfare, with a relative difference of no more than 5.0% compared to the conventional centralized market. Even in worst-case scenarios, the relative difference remains within 7.32%. Meanwhile, the method accurately accounts for network losses, guaranteeing the feasibility of trading plans within the physical network.© 2017 Elsevier Inc. All rights reserved.

Peer-to-peer electricity trading considering voltage-constrained adjustment and loss allocation in blockchain-enabled distribution network

Interest in peer-to-peer (P2P) electricity trading has been growing, especially with respect to distributed energy resources (DERs) accommodation in distribution networks, which utilizes P2P trading mechanism to realize the decentralized operation of DERs. A key technology underpinning P2P electricity trading is the blockchain, which ensures that P2P trading is efficient and transparent. However, due to the limited calculation performance of blockchain, the blockchain-based P2P trading process has to be simplified, which makes on-chain grid security-constrained trading adjustment and on-chain power loss allocation become difficult to be solved. To address this challenge, a new on-chain P2P electricity trading framework with easy implementation is proposed. First, an on-chain linear power flow (OC-LPF) model is developed to reduce the difficulty of on-chain power flow calculation. Second, a novel self-adjustment loss allocation model with low complexity is proposed, which integrates the power loss into OC-LPF-based virtual power variable, and enables power flow calculation and power loss allocation to be performed synchronously. Third, a distributed market clearing algorithm with accelerated convergence based on the OC-LPF model is designed to improve the efficiency of on-chain convergence while protecting the privacy of participants. In addition, the effectiveness of the proposed mechanism has been verified with simulation studies on Ethereum.

P2P Electricity Trading Considering User Preferences for Renewable Energy and Demand-Side Shifts

In the global trend towards decarbonization, peer-to-peer (P2P) energy trading is garnering increasing attention. Furthermore, energy management on the demand side plays a crucial role in decarbonization efforts. The authors have previously developed an automated bidding agent that considers user preferences for renewable energy (RE), assuming users own electric vehicles (EVs). In this study, we expand upon this work by considering users who own not only EVs but also heat pump water heaters, and we develop an automated bidding agent that takes into account their preferences for RE. We propose a method to control the start time and presence of daytime operation shifts for heat pump water heaters, leveraging their daytime operation shift function. Demonstration experiments were conducted to effectively control devices such as EVs and heat pumps using the agent. The results of the experiments revealed that by controlling the daytime operation of heat pumps with our method, the RE utilization rate can be improved compared to scenarios without daytime operation shifts. Furthermore, we developed a simulator to verify the outcomes under different scenarios of demand-side resource ownership rates, demonstrating that higher ownership rates of EVs and heat pumps enable more effective utilization of renewable energy, and that this effect is further enhanced through P2P trading. Based on these findings, we recommend promoting the adoption of demand-side resources such as EVs and heat pumps and encouraging P2P energy trading to maximize the utilization of renewable energy in future energy systems.

Introducing user preferences for peer-to-peer electricity trading through stochastic multi-objective optimization

Peer-to-peer electricity markets are dedicated markets that enable the direct participation of small electricity end-users in energy trading activities. They are seen as a promising alternative that can empower end-users and accelerate the energy transition, by researchers, business developers, and legislators. Moreover, they can include environmental, social, or altruistic preferences that are relevant to end-users, in addition to the economic perspective. Such preferences are sometimes included in the modeling of P2P markets in the existing literature, but the assumptions behind them are rarely validated in practice. To investigate the desired attributes and preferences of end-users to participate in P2P markets, an online survey including a discrete choice experiment was conducted in The Netherlands The results of the survey are used to design a P2P electricity market with product differentiation. The participants in the market are residential end-users that are equipped with a home energy management system that can control some of the household appliances and automate the decision-making process for participation in the market. To facilitate this, a multi-objective stochastic optimization model is presented that incorporates results from the discrete choice experiment and real smart-meter measurements. The case study results demonstrate user preferences’ influence on market outcomes.

Peer-to-Peer Energy Trading Under Network Constraints Based on Generalized Fast Dual Ascent

The wide deployment of renewable energy resources, combined with a more proactive demand-side management, is inducing a new paradigm in both power system operation and electricity market trading, which especially boosts the emergence of the peer-to-peer (P2P) market. A more flexible local market mechanism is highly desirable in response to fast changes in renewable power generation at the distribution network level. Moreover, large-scale implementation of P2P energy trading inevitably affects the secure and economic operation of the distribution network. This paper presents a new P2P electricity trading framework with distribution network security constraints considered using the generalized fast dual ascent method. First, an event-driven local P2P market framework is presented to facilitate short-term or immediate local energy transactions. Then, the sensitivity analysis of nodal voltage and network loss with respect to nodal power injections is used to evaluate the impacts of P2P transactions on the distribution network, which ensures the secure operation of the distribution system. Thereby, the external operational constraints are internalized, and the cost of P2P energy trading can be appropriately allocated in an endogenous way. Moreover, a generalized fast dual ascent method is employed to implement distributed market-clearing efficiently. Finally, numerical results indicate that the proposed model could guarantee secure operation of the distribution system with P2P energy trading, and the solution method enjoys good convergence performance.

Pricing and Simulating Energy Transactions in Energy Communities

Extensive literature is available for modeling and simulating local electricity markets, often called P2P electricity markets, and for pricing local energy transactions in energy communities. Market models and pricing mechanisms provide simulation tools to better understand how these new markets behave, helping to design their main rules for real applications, and assessing the financial compensations of the internal energy transactions. As such, pricing mechanisms are often needed in energy management systems when centralized management approaches are preferred to market-based ones. First, this paper highlights the links between local electricity markets, pricing mechanisms for local electricity transactions, and other approaches to sharing the collective benefits of participating in transactive energy communities. Then, a standard nomenclature is defined to review some of the main pricing mechanisms for local energy transactions, an innovative pricing mechanism based on the economic principles of a post-delivery pool market is proposed, and other relevant approaches for local electricity market simulation such as Nash equilibrium or agent-based simulation are also revisited. The revision was based on systematic searches in common research databases and on the authors’ experience in European and national projects, including local industrial applications for the past five years. A qualitative assessment of the reviewed methods is also provided, and the research challenges are highlighted. This review is intended to serve as a practical guide to pricing mechanisms and market simulation procedures for practical designs of internal financial compensation to share the collective benefits of energy communities.

A privacy-preserving trading strategy for blockchain-based P2P electricity transactions

With the rising demand for energy in distribution networks and the growing development of communication technology, peer-to-peer (P2P) electricity trading realized by blockchain technology not only promotes the participation of distributed generation resources in the energy supply but also enhances the fairness of P2P electricity trading through the decentralized and transparent characteristics of the blockchain. However, owing to the openness and transparency of the on-chain data in the blockchain, the order information submitted by P2P market participants to the blockchain may encounter threats of information disclosure. To address this challenge, we have designed a novel three-layer architecture for P2P electricity trading, whose primary objective is to protect the private information submitted to the blockchain through a new privacy-preserving trading strategy. The proposed three-layer architecture is composed of a physical layer, information layer and market layer. An on-chain data protection method with homomorphic encryption and secure multi-party computing (SMPC) is used to sort and clear encrypted orders in the information layer, and the trading checking and safe settlement process of encrypted orders in continuous double auction (CDA) market are realized through grid security-constrained market mechanism in the market layer. In addition, the effectiveness of the proposed method is verified by case studies running on the IBM Hyperledger Fabric.

Multi-Party Audit and Regulatory Mechanism for P2P Electricity Transaction Based on Distributed Traceable Linkable Group Signature

Multi-Party Audit and Regulatory Mechanism for P2P Electricity Transaction Based on Distributed Traceable Linkable Group Signature