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

Moving towards clean and sustainable transportation system, electrification of railway systems along with the use of electric vehicles (EVs) are of great interest. For economic operation of such systems, the peer-to-peer (P2P) energy trading policy became more applicable. Therefore, this paper proposes a double-layer individual-based optimization algorithm for P2P based optimal energy management (EM) of a smart distribution network incorporating flexible smart railway substation taking into consideration multiple energy sources of the traction system and the wayside distribution network with the integration of both EVs and energy hubs (EHs). For efficient operation of the railway substation, captured regenerative braking energy (RBE) are considered. In addition, photovoltaic (PV) units are used on station's and platforms' roof-tops for clean energy generation along with a multistory parking garage of electric vehicles (EVs). The wayside distribution network of district distribution area with flexible loads also contains several energy sources including distributed generation and multiple EHs. Optimal energy management is carried out including optimal demand side management of flexible loads and EHs. The optimization study takes into consideration several operational uncertainties arising from several components of the system. The simulation results show the feasibility of applying the proposed EM algorithm with an improvement of the energy economics of the system. The results show a reduction of 6.5 % in energy cost of the distribution network loads, 48 % in net energy cost of EHs, 70.8 % in net energy cost of EVs, and 59.1 % in the cost of energy bought by the traction substation. [ABSTRACT FROM AUTHOR]

Given the accelerating global movement towards decarbonization, the importance of promoting renewable energy (RE) adoption and ensuring efficient transactions in energy markets is increasing worldwide. However, renewable energy sources, including photovoltaic (PV) systems, are subject to output fluctuations due to weather conditions, requiring large-scale backup power to balance supply and demand. This makes trading electricity from large-scale PV systems connected to the existing grid challenging. To address this, peer-to-peer (P2P) energy markets where individual prosumers can trade excess power within their local communities have been garnering attention. This study introduces a simulator for P2P energy trading, designed to account for the diverse behaviors and objectives of participants within a market mechanism. The simulator incorporates two risk aversion parameters: one related to transaction timing, expressed through order prices, and another related to forecast errors, managed by adjusting trade volumes. This allows participants to customize their trading strategies, resulting in more realistic analyses of trading outcomes. To explore the effects of these risk aversion settings, we conduct a case study with 120 participants, including both consumers and prosumers, using real data from household smart meters collected on sunny and cloudy days. Our analysis shows that participants with higher aversion to transaction timing tend to settle trades earlier, often resulting in unnecessary transactions due to forecast inaccuracies. Furthermore, trading outcomes are significantly influenced by weather conditions: sunny days typically benefit buyers through lower settlement prices, while cloudy days favor sellers who execute trades closer to their actual needs. These findings demonstrate the trade-off between early execution and forecast error losses, emphasizing the simulator's ability to analyze trading outcomes while accounting for participant risk aversion preferences. [ABSTRACT FROM AUTHOR]

The rapid growth of distributed renewable energy penetration is promoting the evolution of the energy system toward decentralization and decentralized and digitized smart grids. This study was based on energy blockchain, and developed a dual-biding mechanism based on the real-time energy surplus and demand in the local smart grid, which is expected to enable reliable, affordable, and clean energy supply in smart communities. In the proposed system, economic benefits could be achieved by replacing fossil-fuel-based electricity with the high penetration of affordable solar PV electricity. The reduction of energy surplus realized by distributed energy production and P2P energy trading, within the smart grid results in less transmission loss and lower requirements for costly upgrading of existing grids. By adopting energy blockchain and smart contract technologies, energy secure trading with a low risk of privacy leakage could be accommodated. The prototype is examined through a case study, and the feasibility and efficiency of the proposed mechanism are further validated by scenario analysis. [ABSTRACT FROM AUTHOR]

With the large-scale development of distributed energy on the demand side, the trend of "supply exceeding demand" has gradually become prominent, and regional peer-to-peer (P2P) energy trading has become an important measure to improve the local consumption of distributed energy. However, most existing studies usually assume that prosumers behave entirely rationally with the goal of maximum benefit, which has been proved to deviate from the observed actual behaviors. Aiming at the optimal energy of prosumers participating in the P2P market, a prospect theory-based two-stage stochastic optimization model considering the bounded rationality was proposed to accurately simulate the decision-making behavior. Then, a benefit maximization model for the energy trading service provider (ETSP) was constructed considering the power flow constraint to ensure the safe operation of the system. Finally, an improved R-ADMM algorithm considering timeout was proposed to solve the above model and improve the convergence speed. The effectiveness of the proposed model and algorithm was verified via simulation. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) trading in a local energy market (LEM) offers various participants the opportunity to negotiate and strike energy deals among themselves using a distributed ledger technology called blockchain. In this paper, a new local model is presented using a layer-2 scalability solution for second-generation (Gen2) blockchain technology to enable P2P trading among four types of participants: consumers, prosumers with solar photovoltaic (PV) systems, prosumers with solar PV systems and battery energy storage systems (BESSs), and electric vehicles (EVs). The proposed LEM trading platform involves several critical steps, including the creation of typical forecasting profiles for load consumption, solar generation, and battery state-of-charge (SOC) through a forecasting solution. Next, the LEM participants place their pricing bids using a trading agent service, and the trading engine collects the profiles data and bid prices, which performs matchmaking in a forward-facing market. The output of the trading engine consists of dispatch signals for prices and energy values that are sent to each participant to execute actual trading. Furthermore, the trading engines store the accepted and past bidding data and energy values of P2P trades for each participant in blockchain technology, which can be retrieved and displayed on the LEM user interface screens of participants and administrators using their blockchain addresses at any time during the trading process. This study focuses on simulating proposed LEM models, incorporating functional limitations and market rules. These rules aim to reduce energy costs, enhance margins for utilities and retailers, and mitigate grid congestion through BESSs, resulting in reduced operational and capital expenditure. LEM outcomes are analysed and compared with a Business-as-usual (BAU) model. Participants’ energy trading behaviour, cost-revenue dynamics, grid impact, and blockchain implementation costs are explored. The study highlights LEM benefits in terms of reduced CO2 emissions by 984 kg CO2, increased self-sufficiency by 2.2%, and improved financial benefits of all participants by 21.6%. The use of modern blockchain technology guarantees secure data storage and rapid, cost-effective energy trading, thereby making the proposed LEM platform a viable solution in the distribution market.

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.

With the rapid development of distributed energy resource, more and more building users have installed photovoltaic (PV) modules on the rooftops and household energy storage systems (ESS). In this paper, an optimal peer to peer (P2P) energy trading method for building microgrid based on data envelopment analysis (DEA) is proposed. The method consists of a three-layer optimization model. The first layer of which optimizes the capacity allocation and scheduling for buildings participating in P2P energy trading. The second layer forms a cost-sharing scheme based on the shared contribution index of each building. The third layer optimization model proposes a dynamic pricing mechanism for P2P energy trading. In case studies, three possible scenarios are simulated, and the effectiveness of the proposed method is verified by applying comparative analysis.

Multi-energy microgrid technology is an essential for addressing the diversification of energy demand and local consumption of renewable energy sources. Peer-to-peer energy trading has emerged as a promising paradigm for the design of a decentralized trading framework. Therefore, this paper investigated the external peer-to-peer energy trading problem and internal energy conversion problem of interconnected multi-energy microgrids. The concept of energy trading consistency to avoid unreasonable energy trading behavior is first proposed and an off-design performance model of the energy conversion device is considered to more accurately reflect the operating status of the device. The complex decision-making problem with significantly large high-dimensional data is formulated as a partially observable Markov decision process and solved using the proposed multi-agent deep reinforcement learning approach combining the centralized training decentralized execution framework and soft actor-critic algorithm. Finally, the effectiveness of the proposed method was verified through a case simulation. The simulation results showed that the proposed method can reduce the total cost compared with the rule-based method.

The emergence of distributed energy has led to a change in the role of the consumer in the traditional sense over the past decade. The proliferation of emerging generators and distributors has created opportunities for a more decentralised and open energy market. In particular, the emergence of peer-to-peer (P2P) energy trading models, challenged by the surge in demand for sustainable energy, has eliminated the need for intermediaries in energy transactions between consumers, producers, and sellers. Due to the great promise of sustainable energy, both in terms of its contribution to the environment and production costs, this paper reviews a number of well-known P2P energy trading platforms to understand what makes P2P energy trading platforms more functional. As a result, areas for consideration were identified and grouped into five themes: (1) set-up, (2) market, (3) information, (4) price, and (5) regulation. [ABSTRACT FROM AUTHOR]

Abstract This study predicts future values of energy consumption demand from a novel dataset that includes the energy consumption during COVID-19 lockdown, using up-to-date deep learning algorithms to reduce peer-to-peer energy system losses and congestion. Three learning algorithms, namely Random Forest (RF), Bi-LSTM, and GRU, were used to predict the future values of a building’s energy consumption. The results were compared using the RMSE and MAE evaluation metrics. The results show that predicting the future energy demand with accurate results is achievable, and that Bi-LSTM and GRU perform better, especially when trained as univariate models with only the energy consumption values and no other features included.

P2P energy trading is a new technology for increasing the integration of DERs with the power system. This technology enables customers to locally trade energy with each other. The DERs can actively participate in the day ahead and real-time balancing markets. This paper proposes a new two-level framework for the integrated operation of the power and natural gas systems through P2P energy trading considering the demand response capability. In the first level, the optimal operation schedules of the customer are determined through the MINLP optimization problem considering the AC power flow and natural gas steady-state model. In the following (level two), the customers trade energy with each other through the P2P framework. In order to increase the customers' profits and simulate the human trader behaviors, we employed the ZIP trader assumption in the proposed framework. In order to evaluate the introduced framework, it is implemented on the standard IEEE 33 bus test system and 33-node modified gas network. The results of the numerical study revealed that the proposed method can dramatically reduce the total operation cost

Blockchain technology offers great value in terms of decentralization, data integrity, transparency, and traceability, however the transactional data is public, and accessible raising concerns about violating privacy regulations. For example, in the peer-to-peer energy trading and demand response use cases, the data stored in blockchain may allow a third party to infer the load profiles or even identify the behind the meter assets. In this paper, we employ homomorphic techniques to encrypt the energy transactional data stored on the blockchain allowing the smart contracts functions responsible for implementing the business logic of the energy flexibility trading and settlement to perform computations on encrypted data. As computations on smart contracts and public blockchains can be expensive, we have used the lighter version of the Partial Homomorphic Encryption scheme to obfuscate the energy data. To ensure the validity of the smart contracts' functions executed on encrypted data, we leverage on the consensus mechanism of the blockchain network, thus ensuring computation correctness. The solution was validated considering a micro-grid with 12 prosumers that trade their flexibility peer-to-peer (P2P). The results demonstrate the feasibility of maintaining encrypted energy data on the blockchain, executing smart contract functions on encrypted data, and preserving the privacy of computations. As anticipated, the trade-off for better privacy is the gas consumption overhead of the smart contracts’ functions which is higher compared to the non-encrypted case, depending on the length of the public-private keys pair. Nonetheless, our solution exhibits consistent execution times for smart contracts, making it suitable for private networks where gas costs are of minimal concern.

Peer-to-peer (P2P) energy trading refers to a type of decentralized transaction, where the energy from distributed energy resources is directly traded between peers. A key challenge in peer-to-peer energy trading is designing a safe, efficient, and transparent trading model and operating mechanism. In this study, we consider a P2P trading environment based on blockchain technology, where prosumers can submit bids or offers without knowing the reports of others. We propose an Arrow-d’Aspremont-Gerard-Varet (AGV)-based mechanism to encourage prosumers to submit their real reserve price and determine the P2P transaction price. We demonstrate that the AGV mechanism can achieve Bayesian incentive compatibility and budget balance. Kernel density estimation (KDE) is used to derive the prior distribution from the historical bid/offer information of the agents. Case studies are carried out to analyze and evaluate the proposed mechanism. Simulation results verify the effectiveness of the proposed mechanism in guiding agents to report the true reserve price while maximizing social welfare. Moreover, we discuss the advantages of budget balance for decentralized trading by comparing the Vickrey-Clarke-Groves (VCG) and AGV mechanisms.

A deeper decarbonization perspective is undergoing with the increasing engagement of new distributed players and the deployment of innovative behind-the-meter flexibility services. Peer-to-Peer (P2P) trading has emerged as an innovative mechanism to foster the direct energy sharing among multi-level market players with pre-determined responsibility and privacy. However, compared with other P2P assets trading, P2P energy trading is facing huge challenges in achieving a large-scale commercialization due to the cooperation obstacles between non-professional distributed players and regulated players as Distributed System Operator (DSO), Transmission System Operator (TSO) and utilities. It is related to not only business and marketing, but also energy system operation to keep secure and reliable with injection of new roles, new utilization patterns, and new markets. This paper investigates the socio-technical interaction and mechanism for the sustainable P2P energy trading from the social dimension on the cooperation of multi-level market players, the technical dimension on the cutting-edge exchange of flexibility, and the economical dimension on the inter-operative decentralized/regulated marketplaces. Three questions are targeted on: (1) How Information and Communication Technology (ICT) enables co-creation of decentralized heterogeneous user-centered digital frameworks for the large-scale P2P trading interaction, (2) What specific energy services drive the cross-border interactions for exchange of multi-scale P2P flexibility, (3) How operational framework in P2P energy trading achieves the inter-operative marketplaces with the formation of trusted P2P energy society and the injection of multi-scale flexibility services. Finally, regulation challenges on P2P energy trading implementation are discussed for the guide of future work.

For a decade, distributed energy resources in Malaysia have growth as one of the paths in battling with sustainable energy crisis and environmental pollution. Several intriguing initiatives and incentives have been established to encourage the use and sales-side of renewable energy at the distribution consumers. However, Malaysia's distributed energy resources penetration is still at its slow pace, with only 7.6% (excluding large hydropower) shared in energy mix generation. Therefore, innovation in power systems is required to drive the uptake of distributed energy resources. This paper reviews the business model innovation that allows distributed energy resources to participate in national grid services and the wholesale electricity market. Different technical and non-technical challenges with high shares of variable renewable energy in power systems are highlighted, and the current update on compensation scheme, Net-Energy-Metering 3.0 is also discussed. Along with these challenges, stance the prospect of adopting distributed energy resources innovation projects such as peer-to-peer energy trading and virtual power plant in the electricity market. It could further furnish the benefits to a better environmental and power system in terms of carbon dioxide avoidance, grid flexibility and increase revenue for distributed energy resources owners respectively. Through the review, it led to observation that policy and regulatory in Malaysia are the main factors in accelerating the distributed energy resources deployment. Therefore, the abilities and roles of Malaysia Energy Commission and Sustainable Energy Development Authority as a regulator and implementing agencies are crucial in determining the present and future distributed energy resources business model.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Sustainable microgrid is a feasible approach to handle environmental impacts and satisfy customer demand due to its economic, environmental, and social benefits. Due to the high initial investment cost, the installation rate of microgrids has been limited. Various studies investigated the influence of government subsidies on sustainable microgrid design to increase the installation rate. However, the effect of government subsidies, financial factors, and elasticity coefficient of demand under peer-to-peer energy trading has not been explored in the current sustainable microgrids design literature. To overcome this limitation, this paper investigates the sustainable microgrid design problem to simultaneously consider the effect of government subsidies, peer-to-peer energy trading, time value of money, elasticity coefficient of demand, and uncertainties. The objectives are to maximize total profit and minimize the total environmental cost to satisfy electric energy demand. Fuzzy multi-objective programming is applied to determine the optimal decisions on the number, location, type of renewable energy, the capacity of renewable distributed generation sources, electricity flows, price for selling electricity to demand areas and P2P energy trading, and government subsidy rates. A genetic algorithm and its hybrid versions to include tabu search and simulated annealing are then used to solve the proposed model. Numerical experiments used to evaluate the performance of the proposed model and algorithms show that the proposed genetic algorithm is most effective in maximizing total profit and minimizing environmental cost. Computational results demonstrate that on average, the inclusion of the peer-to-peer trading and government subsidies in the proposed model increases total profit by 13.23% and reduces total environmental cost by 6.29%. [Display omitted] • Sustainable microgrid design problem under impacted factors is investigated. • The effect of government subsidies and peer-to-peer energy trading are assessed. • Economic, environmental, and social objectives are considered under impacted factors. • Fuzzy multi-objective programming model is developed to handle the uncertainty. • Genetic algorithm and its hybrid versions are used to solve the propose model. [ABSTRACT FROM AUTHOR]

Rooftop solar power generation is becoming more widespread in residential microgrids. As well as new concepts of electricity markets, such as peer-to-peer (P2P) markets, where consumers and prosumers can directly exchange locally generated energy with each other without any intermediary third party for sustainable development. Data security is a big concern with energy trading; therefore, blockchain technology is being used more and more in energy markets. It has the potential to simplify P2P energy trading. In this paper, blockchain is designed to fit into the decentralized nature of the P2P market, securing the payment mechanism and transaction data store. The blockchain-enabled platform is developed using the Proof-of-Work (PoW) consensus algorithm, and is verified with the help of the Postman application programming interface (API). All transactions involving the buying and selling of energy are handled by a miner without the help of any third parties. The study of a five-user residential community, whether the strategy is recommended or not, is validated through simulation findings. An overview of the results revealed that all users benefited from the developed, secure P2P platform. [ABSTRACT FROM AUTHOR]

The number of electric vehicles (EVs) is increasingly growing day by day and the charging infrastructure for covering this growing number of EVs should be developed. The construction of charging stations is one of the main solutions for supporting EVs while it costs huge investments for installation. Thus, this is not financially logical to invest in charging stations in remote areas with lower demands. An alternative way of constructing charging stations is to provide a peer-to-peer (P2P) energy exchange system in order to support out-of-charge EVs. In this paper, a private cloud-edge emergency energy trading framework is proposed to facilitate energy exchange among consumers and providers. Furthermore, a bidding system is suggested to encourage EVs with extra charges to exchange their energy. Moreover, a matching strategy for pairing consumers and providers is suggested in this paper that considers the benefit of both consumers and providers. In the proposed matching system, a measurement strategy is also suggested for considering the effect of the reliability and punctuality of the providers. To develop the accuracy and efficiency of the proposed framework, employing deep learning methods is also suggested in different layers of the framework. The performance of the proposed framework is evaluated on several case studies in the presence of EVs with realistic features to prove its efficiency, feasibility, and scalability. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) trading is seen as a viable solution to handle the growing number of distributed energy resources in distribution networks. However, when dealing with large-scale consumers, there are several challenges that must be addressed. One of these challenges is limited communication capabilities. Additionally, prosumers may have specific preferences when it comes to trading. Both can result in serious asynchrony in peer-to-peer trading, potentially impacting the effectiveness of negotiations and hindering convergence before the market closes. This paper introduces a connection-aware P2P trading algorithm designed for extensive prosumer trading. The algorithm facilitates asynchronous trading while respecting prosumer's autonomy in trading peer selection, an often overlooked aspect in traditional models. In addition, to optimize the use of limited connection opportunities, a smart trading peer connection selection strategy is developed to guide consumers to communicate strategically to accelerate convergence. A theoretical convergence guarantee is provided for the connection-aware P2P trading algorithm, which further details how smart selection strategies enhance convergence efficiency. Numerical studies are carried out to validate the effectiveness of the connection-aware algorithm and the performance of smart selection strategies in reducing the overall convergence time. [Display omitted] • Develop the connection-aware P2P trading algorithm to address the asynchrony challenge in P2P trading. • Propose smart selection strategies within the context of connection limits in P2P trading. • Provide theoretical guarantees on the convergence of the connection-aware P2P trading algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of blockchain technology to transform energy markets through peer-to-peer (P2P) energy trading. Blockchain enables decentralized, transparent, and secure energy transactions, allowing consumers to trade electricity directly, thereby reducing reliance on traditional centralized systems. The study examines the main challenges facing current energy markets, including inefficiencies, pricing complexities, and the integration of renewable energy sources. By utilizing smart contracts, blockchain automates and secures energy exchanges, giving consumers a more active role in the market. The paper also explores the technical aspects of implementing blockchain in energy trading, such as infrastructure needs and scalability issues, while addressing the regulatory, legal, economic, and environmental implications of this technology. Real-world examples and case studies underscore the potential of blockchain to foster more resilient, efficient, and sustainable energy markets.

This paper presents a new hybrid approach that is suitable for application to energy smart meter gateways, based on combining both blockchain and Secure Element (SE) technologies serving the roles of a secure distributed data storage system and an essential component for building a "root of trust" in IoT platforms simultaneously. Blockchain technology alone may not completely secure a transaction because it only guarantees data immutability, while in most cases, the data has to be also secured at the point of generation. The proposed combinational approach aims to build a robust root of trust by introducing the SE, which will provide IoT devices with trusted computed resources. The feasibility of the proposed method is validated by testing three different implementation scenarios, using different Secure Element systems (SES) combined with blockchain and LPWAN communication technologies to encrypt, transmit, and save data. This hybrid approach aids in overcoming the obstructions of using any one technology alone, and its use is demonstrated with a case study for an Energy Smart Metering gateway that enables the implementation of a local Peer to Peer energy trading scheme that is end-to-end secure and decentralized. [ABSTRACT FROM AUTHOR]

Blockchain technology (BCT) enables the automated execution of smart contracts in peer-to-peer (P2P) energy trading. BCT-based P2P platforms allow the sharing, exchange and trade of energy among consumers or prosumers as peers, fostering the decarbonization, decentralization and digitalization of the energy industry. On the other hand, BCT-based P2P energy trading relies on the collection, storage and processing of a large amount of user data, posing interdisciplinary challenges, including user anonymity, privacy, the governance of BCT systems and the role of energy market players. First, this paper seeks to review the state of the art of European data protection law and regulations by focusing on BCT compliance with the General Data Protection Regulation (GDPR) of 2018. Second, it explores both the potentials and the challenges of BCT-based P2P energy trading from a legal–economic perspective. To do so, the paper adopts an interdisciplinary approach which intertwines both law and economics, by reviewing the recent literature on BCT and P2P energy trading. Findings have revealed that the deployment of BCT-based P2P energy trading is still in its pilot stage because of technology immaturity, data protection uncertainty, incomplete disintermediation and the lack of both user awareness and collaboration among market players. Drawing on the review, the paper also proposes a selection of solutions to foster the implementation of BCT-based P2P energy trading. [ABSTRACT FROM AUTHOR]

Due to the lack of improper access control policies and decentralized access controllers, security and privacy-aware peer-to-peer (P2P) energy trading among electric vehicles (EVs) and the smart grid is challenging. Most of the solutions reported in the literature for P2P energy trading are based upon centralized controllers having various security flaws resulting in their limited applicabilities in real-world scenarios. To handle these issues, in this paper, we propose a P2P energy trading scheduling scheme called as P2P Energy Trading Scheduling (PETS) using blockchain technology. PETS is based on real-time energy consumption monitoring for balancing the energy gap between service providers (SPs), i.e., smart grids and service consumers, i.e., EVs. In PETS, the Stackelberg game theory-based 1-leader multiple-followers scheme is proposed to depict the interactions between EVs and the SP. The selection of the leader among all SPs is made using a second-price reverse auction. As per the announced energy price by the leader, EVs manage energy consumption by minimizing their energy bills. In PETS, on the leader’s side, we propose the Genetic algorithm to maximize its profit. In contrast, on the followers’ side, i.e., EVs, we use the Stackelberg Equilibrium to minimize their energy bills. Simulation results demonstrate that the proposed PETS scheme outperforms the existing state-of-the-art schemes using various performance evaluation metrics. Specifically, it reduces the peak-to-average ratio (PAR) by 12.5% of EVs’ energy load in comparison to the existing state-of-the-art scheme. [ABSTRACT FROM AUTHOR]

Multi-stakeholder participation is crucial in facilitating the development of park-integrated energy systems (PIES). Balancing the diverse interests of various stakeholders, each with its distinct requirements presents a notable challenge. Concurrently, the model's complexity increases due to the engagement of various stakeholders, posing challenges to its resolution through traditional methods. In this context, this paper aims to investigate an optimal scheduling model that incorporates shared energy storage (SES) system, microgrids operator (MGO), electric vehicles station (EVS), and user aggregator (UA) with multiple prosumers. To comprehensively address the interests of all stakeholders, this study introduces a tri-level optimization model. The proposed model integrates a bidirectional Stackelberg-Nash game framework, in which the SES acts as the leader, the MGO acts as the secondary leader, and the UA-EVS acts as the followers while allocating benefits based on the asymmetric Nash bargaining theory. The Stackelberg game model between MGO and UA-EVS is analyzed using the Karush-Kuhn-Tucker (KKT) condition, while the Stackelberg game model between SES and MGO is resolved using the bisection method. Meanwhile, the Nash bargaining method among users is solved using the alternating direction method of multipliers (ADMM) technique. The analysis indicates that the proposed strategy can reduce PIES's costs and carbon emissions, yielding a win-win situation for all stakeholders. • A tri-level model based on bidirectional Stackelberg-Nash game theory is proposed. • The KKT condition is combined with the bisection method to solve the tri-level model. • The duality of the MGO as both secondary leader and follower is emphasized. • The profits of each stakeholder are considered and the PIES's carbon emissions are reduced. [ABSTRACT FROM AUTHOR]

In this paper, a peer-to-peer energy trading framework for multi-industrial micro-grids is proposed to promote the efficient utilization of local renewable energy resources. There are two levels in the proposed framework. In the upper level, each industrial micro-grid chooses to sell or buy electricity with neighbors by responding to renewable energy generation, where the manufacturing sequences within typical assembly processes are characterized as deferrable loads, thereby offering flexible adjustments for energy trading. Simultaneously, the intricate spatiotemporal coupling between production and assembly lines is thoroughly contemplated and formulated as two distinct sets of constraints. Moreover, uncertainties stemming from renewable energy are articulated through a joint distributionally robust chance constraint, and are subsequently transformed utilizing the Wasserstein metric. In the lower level, the non-cooperative game in market participants is conceptualized to determining the price of energy trading. Following this, a distributed hybrid algorithm combining improved constrained differential evolution and neurodynamic approach is introduced. This innovative blend not only markedly augments the quality of the solutions but also proficiently addresses the continuous distributed peer-to-peer energy trading problem. In the end, the case studies illustrate the effectiveness of the proposed hybrid algorithm and highlight the imperative for micro-grids to engage in energy trading. • The P2P energy trading framework in multi-industrial micro-grids is designed. • The typical manufacturing assembly process is formulated as deferrable loads. • PV uncertainty is described by joint distributionally robust chance constraints. • A fully distributed hybrid algorithm is designed for solving non-convex problem. [ABSTRACT FROM AUTHOR]

• A stochastic optimization-based transactive energy management is proposed. • Cloud energy storage (CES) is considered in the transactive framework. • The optimization model includes different costs of smart homes and CES. • The solving process is implemented in an automated and decentralized manner. • Smart contract is deployed in Ethereum blockchain. In modern paradigm of smart grid, prosumers can trade energy peer-to-peer in a transactive energy market to reduce energy costs and improve energy efficiency. In addition, cloud energy storage (CES) is a type of shared energy storage systems with high economic efficiency that can provide energy storage services for prosumers and become an active player in energy trading. However, transactive energy implementation in power systems has several challenges such as data privacy and security. In existing researches, energy trading mechanisms rely on central authorities, where security and privacy cannot be maintained. Therefore, this paper presents a decentralized stochastic optimization model of transactive energy management based on blockchain in the presence of CES. In this framework, the smart contract based on Alternating Direction Method of Multipliers technique is deployed on the Ganache and Ethereum blockchains, and the YALMIP toolbox along with CPLEX solver handle decentralized optimization in the MATLAB software. The numerical results demonstrate that not only the capacity of distributed generations is fully utilized, but also total costs of smart homes are reduced by more than 27%, and the total grid's delivery power to smart homes is decreased by more than 24% compared to the inflexible scheduling. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Designed a new cooling-heating-power-hydrogen integrated energy systems (IESs). • Developed a novel multi-market P2P trading model for IESs considering uncertainties. • Game equilibrium-based ADMM is employed to obtain the P2P trading volume and prices. • The operating profit of P2P trading is improved by the proposed heuristic method. • A new online learning model for efficient prediction of uncertainties in P2P trading. [ABSTRACT FROM AUTHOR]

The decentralization of power generation driven by the rise in the adoption of distributed energy resources paves the way for a new paradigm in grid operations. P2P energy trading is beneficial to the grid as well as the connected peers. A blockchain-based smart contract is well suited to transparently facilitate trades between energy consumers and producers without the services of intermediaries. In this paper, Ethereum-based smart contracts that facilitate double energy auction and spinning reserve trading are developed with Solidity, compiled, and deployed within the Remix IDE. Willing energy sellers/buyers submit offers/bids to a contract that implements the double auction procedure. In order to fulfil energy supply obligations, sellers are also able to purchase spinning reserves via another smart contract. The smart contracts' effectiveness in performing the auction procedure and making payments is confirmed using an energy/reserve market scenario. The proposed scheme encourages further adoption of distributed energy resources and participation in local P2P energy trading. [ABSTRACT FROM AUTHOR]

The increasing penetration of distributed renewable generations has given rise to a novel energy management mechanism, peer-to-peer (P2P) energy trading. The concept of Energy Internet (EI) is proposed as a new energy system framework to facilitate P2P energy trading where all distributed electrical devices are interconnected via energy routers (ERs). A proper market clearing approach is required to coordinate decentralized decision making in P2P market for demand-supply balance and feasible end-to-end energy delivery. In this paper, a decentralized market clearing mechanism considering energy path conflict management is proposed for P2P energy trading in energy local area network (e-LAN). An adjusted conflict-based search (ACBS) algorithm is proposed to deal with the non-convexity of the social welfare maximization problem where the nonconvex problem is transformed into multiple independent convex problems. Considering the privacy protection of participants, a dual decomposition based approach is proposed to solve these convex problems in a decentralized manner where self rationality of each participant can be satisfied by maximizing its personal welfare. In addition, energy path conflicts caused by decentralized optimization is resolved by imposing penalty fee and observing the rule of energy sharing maximization. Numerical simulations are presented to verify the effectiveness of the proposed market clearing approach. [ABSTRACT FROM AUTHOR]

Building a reliable information exchange scheme is the prerequisite for Peer-to-Peer (P2P) energy trading in the smart grid. This paper proposes a multi-stage information protection scheme to meet the data security requirements including personal privacy-preserving, message authentication and confidentiality. In this study, the adversary is supposed to attack the continuous double auction (CDA)-based energy trading system in both the bidding period and implementation period. To prevent the smart grid from all potential cyber-attacks, our protection scheme is proposed to deal with personal privacy leakage, malicious data injection and impersonation attack. Before the trading period, in the home energy data collection stage, we propose a dual privacy-preserving scheme based on the short key homomorphic encryption and the decentralized framework. In the trading period, an encryption-signature mixed (E-S) communication model is proposed to achieve the secure transmission of trading messages (e.g., energy bidding from prosumer). At last, in the implementation period, a decentralized field measurement monitoring scheme is proposed to detect the node compromise attack to the power measurements. In addition, the computational feasibility, time cost and communication cost are evaluated with the home-use PC. The anti-attack performance of the proposed scheme is verified on the IEEE 39-bus distribution network with the CDA-based P2P energy trading market. [ABSTRACT FROM AUTHOR]

The massive adoption of electric vehicles (EVs) has caused an increasing demand for electric energy to charge the vehicles. Efficiently managing energy trading between energy providers and energy consumers can lead to meet the high demand for charging EVs while reducing its cost compared to traditional power provided by the utility company. However, a large portion of the existing systems leveraged for trading energy between EVs are centralized and fall short in providing transparency, reliability, audit, security, and trustworthy features. In this paper, we propose blockchain-based energy trading using an auctioning and reputation scheme. We develop Ethereum smart contracts which enable owners of EVs to automatically request electricity to charge their vehicles in a reliable, cost-effective, secure, and trustworthy manner. The proposed approach ensures the lowest rate available by implementing a reverse auctioning scheme for fair competition between providers to provide the requested service at the lowest cost. The proposed solution enforces high quality of service through a reputation-based approach that quantifies the performance of the service providers and gives an advantage to more reputable providers. We present the implementation details of the deployed system on a test Ethereum blockchain platform. We perform system testing and evaluation to validate and assess the functionality and performance of the proposed solution. Furthermore, we present security and cost analyses to show the affordability, robustness, and practicality of the proposed approach.

Peer-to-peer (P2P) energy trading platform is an upcoming energy generation and effective energy managing strategy that rewards proactive customers (acting as prosumers) in which individuals trade energy for products and services. On the other hand, P2P trading is expected to give multiple benefits to the grid in minimizing the peak load demand, energy consumption costs, and eliminating network losses. However, installing P2P energy trading on a broader level in electrical-based networks presents a number of modeling problems in physical and virtual network layers. As a result, this article presents a thorough examination of P2P studies of energy trade literature. An overview is given with the essential characteristics of P2P energy trading and comparatively analyzed with multiple advantages for the utility grid and individual prosumers. The study then addresses the physical and virtual levels that systematically categorize the available research. Furthermore, the technological techniques have been gone through multiple problems that need to overcome for P2P energy trading in electrical networks. Finally, the article concludes with suggestions for further research.

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

The number of electric vehicles (EVs) is increasingly growing day by day and the charging infrastructure for covering this growing number of EVs should be developed. The construction of charging stations is one of the main solutions for supporting EVs while it costs huge investments for installation. Thus, this is not financially logical to invest in charging stations in remote areas with lower demands. An alternative way of constructing charging stations is to provide a peer-to-peer (P2P) energy exchange system in order to support out-of-charge EVs. In this paper, a private cloud-edge emergency energy trading framework is proposed to facilitate energy exchange among consumers and providers. Furthermore, a bidding system is suggested to encourage EVs with extra charges to exchange their energy. Moreover, a matching strategy for pairing consumers and providers is suggested in this paper that considers the benefit of both consumers and providers. In the proposed matching system, a measurement strategy is also suggested for considering the effect of the reliability and punctuality of the providers. To develop the accuracy and efficiency of the proposed framework, employing deep learning methods is also suggested in different layers of the framework. The performance of the proposed framework is evaluated on several case studies in the presence of EVs with realistic features to prove its efficiency, feasibility, and scalability.

This paper presents a novel blockchain-based energy trading architecture for electric vehicles (EVs) within smart cities. By allowing local renewable energy providers to supply public charging stations, EV drivers can gain access to affordable energy and optimally plan for their charging operations. For this purpose, we present a smart-contract based trading platform that runs on top of a private Ethereum network. Contrary to existing solutions, we rely on the legacy billing and metering of the existing utility company in order to avoid making major changes to the existing infrastructure. The trading logic, including the auction mechanism, used to exchange energy can be defined in a smart-contract and applied within the platform. We conduct extensive experiments to evaluate the performance of some existing auction mechanisms and the underlying private Ethereum network in supporting the corresponding energy trading transaction load. We develop a virtualization-based simulator for Ethereum and measure both the transaction throughput and latency under different network and workload scenarios. The obtained results have shown that the current Ethereum implementation can support charging requests from EVs during peak hours in very crowded cities, such as Singapore.

As the world strives to decarbonize, the effective use of renewable energy has become an important issue, and P2P power trading is expected to unlock the value of renewable energy and encourage its adoption by enabling power trading based on user needs and user assets. In this study, we constructed a bidding agent that optimizes bids based on electricity demand and generation forecasts, user preferences for renewable energy (renewable energy-oriented or economically oriented), and owned assets in a P2P electricity trading market, and automatically performs electricity trading. The agent algorithm was used to evaluate the differences in trading content between different asset holdings and preferences by performing power sharing in a real scale environment. The demonstration experiments show that: EV-owning and economy-oriented users can trade more favorably in the market with a lower average execution price than non-EV-owning users; forecasting enables economy-enhancing moves to store nighttime electricity in batteries in advance in anticipation of future power generation and market prices; EV-owning and renewable energy-oriented users can trade more favorably in the market with other users. EV-owning and renewable energy-oriented users can achieve higher RE ratios at a cost of about +1 yen/kWh compared to other users. By actually issuing charging and discharging commands to the EV and controlling the charging and discharging, the agent can control the actual use of electricity according to the user’s preferences.

Peer-to-peer (P2P) energy trading is gaining attention as a technology to effectively handle already existing distributed energy resources (DER). In order to manage a large number of DER, it is necessary to increase the number of P2P energy trading participants. For that, designing incentives for participants to engage in P2P energy trading is important. This paper describes a user-centric cooperative mechanism that enhances user participation in P2P energy trading. The key components of this incentive for participants to engage in P2P energy trading are described and evaluated in this study. The goal of the proposal is to make it possible to conduct economic transactions while reflecting the preferences of the traders in the ordering process, making it possible to conduct transactions with minimal effort. As a case study, the Higashi-Fuji demonstration experiment conducted in Japan verified the proposed mechanism. In this experiment, 19 households and 9 plugin hybrid vehicles (PHV) were evaluated. As a result, the study confirmed that prosumers were able to sell their surplus electricity, and consumers were able to preferentially purchase renewable energy when it was available. In addition, those trades were made economically. All trades were made automatically, and this efficiency allowed the users to continue using the P2P energy trading.

Abstract Peer to Peer (P2P) Energy trading in a lower voltage distribution system is one of the effective approaches to increase renewable energy penetration from decentralized generators (DG). In this paper, an energy auction is proposed as a marketplace and mechanism of the market design is established to ensure a fair and efficient bidding in the auction. An optimal bidding strategy is very important to the energy auction. To solve this problem, the Bayesian Game Theory is adopted as the strategy in the energy auction to enable efficient and cost-effective bidding for each buyer. This paper proposes a procedure for the energy auction to ensure that the power losses are within acceptable range. An algorithm to transform the power loss issue to become part of bidding strategy of Bayesian Game Theory. The proposed method is showed that it has maximized the utility for prosumer on a typical distribution network. [ABSTRACT FROM AUTHOR]

• P2P energy trading policy combined with energy management of FSTS. • Integrating both EVs parking lots and EHs with the FSTS. • Both regular and random EVs' behavior of multiple type of users are considered. • Introducing next-journey dependent minimum state of charge to EVs. • Adding energy sharing willingness factor to EV model. In an open energy market, the peer-to-peer (P2P) energy trading policy became more applicable. Therefore, while planning for new electrified systems, such a trading policy is taken into consideration. On the other hand, as the electrification of railway systems along with the use of electric vehicles serve several sustainable development goals, plans are studied for the electrification of the inter-city railway system in Alexandria, Egypt, in order to connect it with the high-speed inter-provincial electric railway system. In addition, other plans are studied for local production of electric vehicles. For this purpose, this paper presents a study regarding P2P based optimal energy management (EM) of a flexible smart railway substation taking into consideration multiple energy sources of the traction system and the wayside distribution network. The energy sources of the substation include captured regenerative braking energy (RBE), photovoltaic (PV) units and a multistory parking garage of electric vehicles (EVs). The wayside distribution network also contains several energy sources including distributed generation and multiple energy hubs (EHs) in addition to district distribution area with flexible loads. Optimal energy management is carried out including optimal demand side management of flexible loads and EHs. The optimization study takes into consideration several operational uncertainties arising from several components of the system. The simulation results show the feasibility of applying the proposed EM algorithm with an improvement of the energy economics of the system. [ABSTRACT FROM AUTHOR]