Microgrids (MGs) and active distribution networks (ADNs) are important platforms for distributed energy resource (DER) consumption. The increasing penetration of DERs has motivated the development ADNs coupled with MG...Microgrids (MGs) and active distribution networks (ADNs) are important platforms for distributed energy resource (DER) consumption. The increasing penetration of DERs has motivated the development ADNs coupled with MGs. This paper proposes a distributedco-optimization method for peer-to-peer (P2P) energy trading and network operation for an ADN integrated with multiple microgrids(MMGs). A framework that optimizes P2P energy trading among MMGs and ADN operations was first established. Subsequently, anenergy management model that aims to minimize the operation and energy trading costs was constructed for each MG. Accordingly, theMMGs’ cooperative game model was established based on Nash bargaining theory to incentivize each stakeholder to participate in P2Penergy trading, and a distributed solution method based on the alternating direction method of multipliers was developed. Moreover, analgorithm that adjusts the amount of energy trading between the ADN and MG is proposed to ensure safe operation of the distributionnetwork. With the communication between the MG and ADN, the MMGs’ P2P trading and ADN operations are optimized in a coordinated manner. Finally, numerical simulations were conducted to verify the accuracy and effectiveness of the proposed method.展开更多
As a promising solution to address the“energy trilemma”confronting human society,peer-to-peer(P2P)energy trading has emerged and rapidly developed in recent years.When carrying out P2P energy trading,customers with ...As a promising solution to address the“energy trilemma”confronting human society,peer-to-peer(P2P)energy trading has emerged and rapidly developed in recent years.When carrying out P2P energy trading,customers with distributed energy resources(DERs)are able to directly trade and share energy with each other.This paper summarizes and analyzes the global development of P2P energy trading based on a comprehensive review of related academic papers,research projects,and industrial practice.Key aspects in P2P energy trading are identified and discussed,including market design,trading platforms,physical infrastructure and information and communication technology(ICT)infrastructure,social science perspectives,and policy.For each key aspect,existing research and practice are critically reviewed and insights for future development are presented.Comprehensive concluding remarks are provided at the end,summarizing the major findings and perspectives of this paper.P2P energy trading is a growing field with great potential and opportunities for both academia and industry across the world.展开更多
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 desig...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.展开更多
The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants.Besides,the game relationship between transaction subjects needs to be furth...The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants.Besides,the game relationship between transaction subjects needs to be further explored.This paper proposes a Peer-to-Peer energy trading method for multi-virtual power plants based on a non-cooperative game.Firstly,a coordinated control model of public buildings is incorporated into the scheduling framework of the virtual power plant,considering the energy consumption characteristics of users.Secondly,the utility functions of multiple virtual power plants are analyzed,and a non-cooperative game model is established to explore the game relationship between electricity sellers in the Peer-to-Peer transaction process.Finally,the influence of user energy consumption characteristics on the virtual power plant operation and the Peer-to-Peer transaction process is analyzed by case studies.Furthermore,the effect of different parameters on the Nash equilibrium point is explored,and the influence factors of Peer-to-Peer transactions between virtual power plants are summarized.According to the obtained results,compared with the central air conditioning set as constant temperature control strategy,the flexible control strategy proposed in this paper improves the market power of each VPP and the overall revenue of the VPPs.In addition,the upper limit of the service quotation of the market operator have a great impact on the transaction mode of VPPs.When the service quotation decreases gradually,the P2P transaction between VPPs is more likely to occur.展开更多
In response to the additional load impact caused by the integration of electric vehicles (EVs) into the grid or microgrids (MGs), as well as the issue of low responsiveness of EV users during vehicle-to-vehicle (V2V) ...In response to the additional load impact caused by the integration of electric vehicles (EVs) into the grid or microgrids (MGs), as well as the issue of low responsiveness of EV users during vehicle-to-vehicle (V2V) power exchange processes, this paper explores a multi-party energy trading model considering user responsiveness under low carbon goals. The model takes into account the stochastic charging and discharging characteristics of EVs, user satisfaction, and energy exchange costs, and formulates utility functions for participating entities. This transforms the competition in multi-party energy trading into a Bayesian game problem, which is subsequently resolved. Furthermore, this paper primarily employs sensitivity analysis to evaluate the impact of multi-party energy trading on user responsiveness and green energy utilization, with the aim of promoting incentives in the electricity trading market and aligning with low-carbon requirements. Finally, through case simulations, the effectiveness of this model for the considered scenarios is demonstrated.展开更多
Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing an...Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing and energy trading confronts security and privacy challenges.In this paper,we exploit consortium blockchain and Directed Acyclic Graph(DAG)to propose a new secure and distributed spectrum sharing and energy trading framework in power IoT,named spectrum-energy chain,where a set of local aggregators(LAGs)cooperatively confirm the identity of the power devices by utilizing consortium blockchain,so as to form a main chain.Then,the local power devices verify spectrum and energy micro-transactions simultaneously but asynchronously to form local spectrum tangle and local energy tangle,respectively.Moreover,an iterative double auction based micro transactions scheme is designed to solve the spectrum and energy pricing and the amount of shared spectrum and energy among power devices.Security analysis and numerical results illustrate that the developed spectrum-energy chain and the designed iterative double auction based microtransactions scheme are secure and efficient for spectrum sharing and energy trading in power IoT.展开更多
With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the grid.The classical grid can be u...With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the grid.The classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the grids.The TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them.At the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)models.Though some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,etc.into account.In this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in TEM.The proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence rate.Moreover,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading systems.In order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive outcome.The simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.展开更多
Blockchain,known for its secure encrypted ledger,has garnered attention in financial and data transfer realms,including the field of energy trading.However,the decentralized nature and identity anonymity of user nodes...Blockchain,known for its secure encrypted ledger,has garnered attention in financial and data transfer realms,including the field of energy trading.However,the decentralized nature and identity anonymity of user nodes raise uncertainties in energy transactions.The broadcast consensus authentication slows transaction speeds,and frequent single-point transactions in multi-node settings pose key exposure risks without protective measures during user signing.To address these,an alliance blockchain scheme is proposed,reducing the resource-intensive identity verification among nodes.It integrates multi-signature functionality to fortify user resources and transac-tion security.A novel multi-signature process within this framework involves neutral nodes established through central nodes.These neutral nodes participate in multi-signature’s signing and verification,ensuring user identity and transaction content privacy.Reducing interactions among user nodes enhances transaction efficiency by minimizing communication overhead during verification and consensus stages.Rigorous assessments on reliability and operational speed highlight superior security performance,resilient against conventional attack vectors.Simulation shows that compared to traditional solutions,this scheme has advantages in terms of running speed.In conclusion,the alliance blockchain framework introduces a novel approach to tackle blockchain’s limitations in energy transactions.The integrated multi-signature process,involving neutral nodes,significantly enhances security and privacy.The scheme’s efficiency,validated through analytical assessments and simulations,indicates robustness against security threats and improved transactional speeds.This research underscores the potential for improved security and efficiency in blockchain-enabled energy trading systems.展开更多
A secured and scalable Peer-to-Peer (P2P) energy trading platform can facilitate the integration of renewable energy and thus contribute to building sustainable energy infrastructure. The decentralized architecture of...A secured and scalable Peer-to-Peer (P2P) energy trading platform can facilitate the integration of renewable energy and thus contribute to building sustainable energy infrastructure. The decentralized architecture of blockchain makes it a befitting candidate to actualize an efficient P2P energy trading market. However, for a sustainable and dynamic blockchain-based P2P energy trading platform, few critical aspects such as security, privacy and scalability need to be addressed with high priority. This paper proposes a blockchain-based solution for energy trading among the consumers which ensures the systems’ security, protects users’ privacy, and improves the overall scalability. More specifically, we develop a multilayered semi-permissioned blockchain-based platform to facilitate energy transactions. The practical byzantine fault tolerant algorithm is employed as the underlying consensus for verification and validation of transactions which ensures the system’s tolerance against internal error and malicious attacks. Additionally, we introduce the idea of quality of transaction (QoT)—a reward system for the participants of the network that eventually helps determine the participant’s eligibility for future transactions. The resiliency of the framework against the transaction malleability attack is demonstrated with two uses cases. Finally, a qualitative analysis is presented to indicate the system’s usefulness in improving the overall security, privacy, and scalability of the network.展开更多
In the new competitive environment of the electricity market, risk analysis is a powerful tool to guide investors under both contract uncertainties and energy prices of the spot market. Moreover, simulation of spot pr...In the new competitive environment of the electricity market, risk analysis is a powerful tool to guide investors under both contract uncertainties and energy prices of the spot market. Moreover, simulation of spot price scenarios and evaluation of energy contracts performance, are also necessary to the decision maker, and in particular to the trader to foresee opportunities and possible threats in the trading activity. In this context, computational systems that allow what-if analysis, involving simulation of spot price, contract portfolio optimization and risk evaluation are rather important. This paper proposes a decision support system not only for solving the problem of contracts portfolio optimization, by using linear programming, but also to execute risks analysis of the contracts portfolio performance, with VaR and CVaR metrics. Realistic tests have demonstrated the efficiency of this system.展开更多
Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer.It also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainiti...Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer.It also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer.In recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction stability.In order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in SGs.The proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter optimization.In addition,MOGOA-based FS technique is utilized in the selection of optimum subset of features.Besides,DELM-based predictive model is also applied in forecasting the load requirements.The proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive outcome.To inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter dataset.In the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.展开更多
The proliferation of distributed energy resources(DERs)and the large-scale electrification of transportation are driving forces behind the ongoing evolution for transforming traditionally passive consumers into prosum...The proliferation of distributed energy resources(DERs)and the large-scale electrification of transportation are driving forces behind the ongoing evolution for transforming traditionally passive consumers into prosumers(both consumers and producers)in coordinated power distribution network(PDN)and urban transportation network(UTN).In this new paradigm,peer-to-peer(P2P)energy trading is a promising energy management strategy for dynamically balancing the supply and demand in elec-tricity markets.In this paper,we propose the application of Blockchain(BC)to electric vehicle charging station(EVCS)op-erations to optimally transact energy in a hierarchical P2P framework.In the proposed framework,a decentralised privacy-preserving clearing mechanism is implemented in the transactive energy market(TEM)in which BC's smart contracts are applied in a coordinated PDN and UTN operation.The effectiveness of the proposed TEM and its solution approach are validated via numerical simulations which are performed on a modified IEEE 123-bus PDN and a modified Sioux Falls UTN.展开更多
Peer-to-peer(P2P)energy trading provides a promising solution for integrating distributed microgrids(MGs).However,most existing research works on P2P energy trading among MGs ignore the influence of the dynamic networ...Peer-to-peer(P2P)energy trading provides a promising solution for integrating distributed microgrids(MGs).However,most existing research works on P2P energy trading among MGs ignore the influence of the dynamic network usage fees imposed by the distribution system operator(DSO).Therefore,a method of P2P energy trading among MGs based on the optimal dynamic network usage fees is proposed in this paper to balance the benefits of DSO.The interaction between DSO and MG is formulated as a Stackelberg game,in which the existence and uniqueness of optimal dynamic network usage fees are proven.Additionally,the optimal dynamic network usage fees are obtained by transforming the bi-level problem into single-level mixed-integer quadratic programming using Karush-Kuhn-Tucker conditions.Furthermore,the underlying relationship among optimal dynamic network usage fees,electrical distance,and power flow is revealed,and the mechanism of the optimal dynamic network usage fee can further enhance P2P energy trading among MGs.Finally,simulation results on an enhanced IEEE 33-bus system demonstrate that the proposed mechanism achieves a 17.08%reduction in operation costs for MG while increasing DSO revenue by 15.36%.展开更多
The rapid evolution of distributed energy resources,particularly photovoltaic systems,poses a formidable challenge in maintaining a delicate balance between energy supply and demand while minimizing costs.The integrat...The rapid evolution of distributed energy resources,particularly photovoltaic systems,poses a formidable challenge in maintaining a delicate balance between energy supply and demand while minimizing costs.The integrated nature of distributed markets,blending centralized and decentralized elements,holds the promise of maximizing social welfare and significantly reducing overall costs,including computational and communication expenses.However,achieving this balance requires careful consideration of various hyperparameter sets,encompassing factors such as the number of communities,community detection methods,and trading mechanisms employed among nodes.To address this challenge,we introduce a groundbreaking neural network-based framework,the Energy Trading-based Artificial Neural Network(ET-ANN),which excels in performance compared to existing algorithms.Our experiments underscore the superiority of ET-ANN in minimizing total energy transaction costs while maximizing social welfare within the realm of photovoltaic networks.展开更多
Peer-to-peer(P2P)energy trading enables an efficient regulation of distributed renewable energy among prosumers,implicitly promoting low-carbon operation.This study proposes a novel P2P energy trading scheme with coup...Peer-to-peer(P2P)energy trading enables an efficient regulation of distributed renewable energy among prosumers,implicitly promoting low-carbon operation.This study proposes a novel P2P energy trading scheme with coupled electricity-carbon(E/C)market that co-optimizes both power and carbon emission flows.To facilitate the low-carbon operations in the market,we introduce a prosumer-driven carbon-aware distribution locational marginal price(PDC-DLMP)to serve as a pricing signal for the distribution system operator(DSO).To efficiently determine the optimal trading solutions,we adopt a two-layer data-driven approach.The first layer employs a reinforcement learning algorithm named multi-agent twin-delayed deep deterministic policy gradient(MATD3);the second layer uses a deep neural network(DNN)driven surrogate model,which is designed to map the PDC-DLMP signals,thereby eliminating the need for direct DSO intervention during market operation.This approach protects the physical model parameters of the distribution network and ensures multi-level privacy protection.Simulation results validate the effectiveness of the proposed P2P energy trading scheme with coupled E/C market,demonstrating its ability to achieve both reduced carbon emissions and lower operational costs for microgrid prosumers.展开更多
Peer-to-peer(P2P)energy trading in active distribution networks(ADNs)plays a pivotal role in promoting the efficient consumption of renewable energy sources.However,it is challenging to effectively coordinate the powe...Peer-to-peer(P2P)energy trading in active distribution networks(ADNs)plays a pivotal role in promoting the efficient consumption of renewable energy sources.However,it is challenging to effectively coordinate the power dispatch of ADNs and P2P energy trading while preserving the privacy of different physical interests.Hence,this paper proposes a soft actor-critic algorithm incorporating distributed trading control(SAC-DTC)to tackle the optimal power dispatch of ADNs and the P2P energy trading considering privacy preservation among prosumers.First,the soft actor-critic(SAC)algorithm is used to optimize the control strategy of device in ADNs to minimize the operation cost,and the primary environmental information of the ADN at this point is published to prosumers.Then,a distributed generalized fast dual ascent method is used to iterate the trading process of prosumers and maximize their revenues.Subsequently,the results of trading are encrypted based on the differential privacy technique and returned to the ADN.Finally,the social welfare value consisting of ADN operation cost and P2P market revenue is utilized as a reward value to update network parameters and control strategies of the deep reinforcement learning.Simulation results show that the proposed SAC-DTC algorithm reduces the ADN operation cost,boosts the P2P market revenue,maximizes the social welfare,and exhibits high computational accuracy,demonstrating its practical application to the operation of power systems and power markets.展开更多
The rapid development of distributed energy resources has led to an increasing number of prosumers enhancing their energy utilization,thereby raising the demands on energy management technologies.As a result,the devel...The rapid development of distributed energy resources has led to an increasing number of prosumers enhancing their energy utilization,thereby raising the demands on energy management technologies.As a result,the development of future smart grids is becoming increasingly important,with a particular emphasis on integrating demand-side flexibility into electricity market.To facilitate distributed interaction among prosumers,the double-side auction market enables peer-to-peer(P2P)energy trading,maximizing the social welfare within the dynamic local electricity market.In this setup,prosumers can set their own bidding prices and optimize their operations and trading strategies.However,trading in double-side auction market faces limitations due to the complexity of the market clearing algorithm and the difficulty of predicting other participants’bidding behaviors.To address these challenges,this paper models the P2P energy trading problem in the double-side auction market as a multi-agent reinforcement learning(MARL)task.The concept of federated learning is introduced to enhance scalability among market participants while protecting the private information of individual prosumers.Additionally,the parameter-sharing framework is proposed to accelerate the learning process.To further improve the stability of MARL training,the global information of P2P energy trading price is integrated into the critic network.The proposed federated MARL algorithm is evaluated using a real-world open-source dataset from an European residential community of 250 households with a 15-minute resolution.The evaluation assesses both the training performance of the algorithm as well as the economic and operational benefits of the P2P energy trading market compared to a traditional electricity retail market.展开更多
Utility maximization is a major priority of prosumers participating in peer-to-peer energy trading and sharing(P2P-ETS).However,as more distributed energy resources integrate into the distribution network,the impact o...Utility maximization is a major priority of prosumers participating in peer-to-peer energy trading and sharing(P2P-ETS).However,as more distributed energy resources integrate into the distribution network,the impact of the communication link becomes significant.We present a multi-commodity formulation that allows the dual-optimization of energy and communication resources in P2P-ETS.On one hand,the proposed algorithm minimizes the cost of energy generation and communication delay.On the other hand,it also maximizes the global utility of prosumers with fair resource allocation.We evaluate the algorithm in a variety of realistic conditions including a time-varying communication network with signal delay signal loss.The results show that the convergence is achieved in a fewer number of time steps than the previously proposed algorithms.It is further observed that the entities with a higher willingness to trade the energy acquire more satisfactions than others.展开更多
Peer-to-peer(P2P)transactive energy trading offers a promising solution for facilitating the efficient and secure operation of a distribution system consisting of multiple prosumers.One critical but challenging task i...Peer-to-peer(P2P)transactive energy trading offers a promising solution for facilitating the efficient and secure operation of a distribution system consisting of multiple prosumers.One critical but challenging task is how to avoid system network constraints to be violated for the distribution system integrated with extensive P2P transactive energy trades.This paper proposes a security constrained decentralized P2P transactive energy trading framework,which allows direct energy trades among neighboring prosumers in the distribution system with enhanced system efficiency and security in which no conventional intermediary is required.The P2P transactive energy trading problem is formulated based on the Nash Bargaining theory and decomposed into two subproblems,i.e.,an OPF problem(P1)and a payment bargaining problem(P2).A distributed optimization method based on the alternating direction method of multiplier(ADMM)is adopted as a privacy-preserving solution to the formulated security constrained P2P transactive energy trading model with ensured accuracy.Extensive case studies based on a modified 33-bus distribution system are presented to validate the effectiveness of the proposed security constrained decentralized P2P transactive energy trading framework in terms of efficiency improvement,loss reduction,and voltage security enhancement.展开更多
This paper investigates a double auction-based peer-to-peer(P2P)energy trading market for a community of renewable prosumers with private information on reservation price and quantity of energy to be traded.A novel co...This paper investigates a double auction-based peer-to-peer(P2P)energy trading market for a community of renewable prosumers with private information on reservation price and quantity of energy to be traded.A novel competition padding auction(CPA)mechanism for P2P energy trading is proposed to address the budget deficit problem while holding the advantages of the widely-used Vickrey-Clarke-Groves mechanism.To illustrate the theoretical properties of the CPA mechanism,the sufficient conditions are identified for a truth-telling equilibrium with a budget surplus to exist,while further proving its asymptotical economic efficiency.In addition,the CPA mechanism is implemented through consortium blockchain smart contracts to create safer,faster,and larger P2P energy trading markets.The proposed mechanism is embedded into blockchain consensus protocols for high consensus efficiency,and the budget surplus of the CPA mechanism motivates the prosumers to manage the blockchain.Case studies are carried out to show the effectiveness of the proposed method.展开更多
基金supported by the State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks Program(grant number SGNR0000KJJS2302139).
文摘Microgrids (MGs) and active distribution networks (ADNs) are important platforms for distributed energy resource (DER) consumption. The increasing penetration of DERs has motivated the development ADNs coupled with MGs. This paper proposes a distributedco-optimization method for peer-to-peer (P2P) energy trading and network operation for an ADN integrated with multiple microgrids(MMGs). A framework that optimizes P2P energy trading among MMGs and ADN operations was first established. Subsequently, anenergy management model that aims to minimize the operation and energy trading costs was constructed for each MG. Accordingly, theMMGs’ cooperative game model was established based on Nash bargaining theory to incentivize each stakeholder to participate in P2Penergy trading, and a distributed solution method based on the alternating direction method of multipliers was developed. Moreover, analgorithm that adjusts the amount of energy trading between the ADN and MG is proposed to ensure safe operation of the distributionnetwork. With the communication between the MG and ADN, the MMGs’ P2P trading and ADN operations are optimized in a coordinated manner. Finally, numerical simulations were conducted to verify the accuracy and effectiveness of the proposed method.
基金the Horizon 2020 project P2P-SmarTest,EPSRC Supergen Hub on Energy Networks(EP/S00078X/1)and MISTRAL(EP/N017064/1).
文摘As a promising solution to address the“energy trilemma”confronting human society,peer-to-peer(P2P)energy trading has emerged and rapidly developed in recent years.When carrying out P2P energy trading,customers with distributed energy resources(DERs)are able to directly trade and share energy with each other.This paper summarizes and analyzes the global development of P2P energy trading based on a comprehensive review of related academic papers,research projects,and industrial practice.Key aspects in P2P energy trading are identified and discussed,including market design,trading platforms,physical infrastructure and information and communication technology(ICT)infrastructure,social science perspectives,and policy.For each key aspect,existing research and practice are critically reviewed and insights for future development are presented.Comprehensive concluding remarks are provided at the end,summarizing the major findings and perspectives of this paper.P2P energy trading is a growing field with great potential and opportunities for both academia and industry across the world.
基金supported by National Natural Science Foundation of China(U2066211,52177124,52107134)the Institute of Electrical Engineering,CAS(E155610101)+1 种基金the DNL Cooperation Fund,CAS(DNL202023)the Youth Innovation Promotion Association of CAS(2019143).
文摘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.
基金supported by the Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.,China,under Grant 2021200.
文摘The current electricity market fails to consider the energy consumption characteristics of transaction subjects such as virtual power plants.Besides,the game relationship between transaction subjects needs to be further explored.This paper proposes a Peer-to-Peer energy trading method for multi-virtual power plants based on a non-cooperative game.Firstly,a coordinated control model of public buildings is incorporated into the scheduling framework of the virtual power plant,considering the energy consumption characteristics of users.Secondly,the utility functions of multiple virtual power plants are analyzed,and a non-cooperative game model is established to explore the game relationship between electricity sellers in the Peer-to-Peer transaction process.Finally,the influence of user energy consumption characteristics on the virtual power plant operation and the Peer-to-Peer transaction process is analyzed by case studies.Furthermore,the effect of different parameters on the Nash equilibrium point is explored,and the influence factors of Peer-to-Peer transactions between virtual power plants are summarized.According to the obtained results,compared with the central air conditioning set as constant temperature control strategy,the flexible control strategy proposed in this paper improves the market power of each VPP and the overall revenue of the VPPs.In addition,the upper limit of the service quotation of the market operator have a great impact on the transaction mode of VPPs.When the service quotation decreases gradually,the P2P transaction between VPPs is more likely to occur.
文摘In response to the additional load impact caused by the integration of electric vehicles (EVs) into the grid or microgrids (MGs), as well as the issue of low responsiveness of EV users during vehicle-to-vehicle (V2V) power exchange processes, this paper explores a multi-party energy trading model considering user responsiveness under low carbon goals. The model takes into account the stochastic charging and discharging characteristics of EVs, user satisfaction, and energy exchange costs, and formulates utility functions for participating entities. This transforms the competition in multi-party energy trading into a Bayesian game problem, which is subsequently resolved. Furthermore, this paper primarily employs sensitivity analysis to evaluate the impact of multi-party energy trading on user responsiveness and green energy utilization, with the aim of promoting incentives in the electricity trading market and aligning with low-carbon requirements. Finally, through case simulations, the effectiveness of this model for the considered scenarios is demonstrated.
基金supported by the National Key R&D Program of China(2020YFB1807801,2020YFB1807800)in part by Project Supported by Engineering Research Center of Mobile Communications,Ministry of Education(cqupt-mct-202003)+2 种基金in part by Key Lab of Information Network Security,Ministry of Public Security under Grant C19603in part by National Natural Science Foundation of China(Grant No.61901067 and 61901013)in part by Chongqing Municipal Natural Science Foundation(Grant No.cstc2020jcyj-msxmX0339).
文摘Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing and energy trading confronts security and privacy challenges.In this paper,we exploit consortium blockchain and Directed Acyclic Graph(DAG)to propose a new secure and distributed spectrum sharing and energy trading framework in power IoT,named spectrum-energy chain,where a set of local aggregators(LAGs)cooperatively confirm the identity of the power devices by utilizing consortium blockchain,so as to form a main chain.Then,the local power devices verify spectrum and energy micro-transactions simultaneously but asynchronously to form local spectrum tangle and local energy tangle,respectively.Moreover,an iterative double auction based micro transactions scheme is designed to solve the spectrum and energy pricing and the amount of shared spectrum and energy among power devices.Security analysis and numerical results illustrate that the developed spectrum-energy chain and the designed iterative double auction based microtransactions scheme are secure and efficient for spectrum sharing and energy trading in power IoT.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the grid.The classical grid can be updated to the smart grid by the integration of Information and Communication Technology(ICT)over the grids.The TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among them.At the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)models.Though some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,etc.into account.In this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in TEM.The proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence rate.Moreover,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading systems.In order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive outcome.The simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.
文摘Blockchain,known for its secure encrypted ledger,has garnered attention in financial and data transfer realms,including the field of energy trading.However,the decentralized nature and identity anonymity of user nodes raise uncertainties in energy transactions.The broadcast consensus authentication slows transaction speeds,and frequent single-point transactions in multi-node settings pose key exposure risks without protective measures during user signing.To address these,an alliance blockchain scheme is proposed,reducing the resource-intensive identity verification among nodes.It integrates multi-signature functionality to fortify user resources and transac-tion security.A novel multi-signature process within this framework involves neutral nodes established through central nodes.These neutral nodes participate in multi-signature’s signing and verification,ensuring user identity and transaction content privacy.Reducing interactions among user nodes enhances transaction efficiency by minimizing communication overhead during verification and consensus stages.Rigorous assessments on reliability and operational speed highlight superior security performance,resilient against conventional attack vectors.Simulation shows that compared to traditional solutions,this scheme has advantages in terms of running speed.In conclusion,the alliance blockchain framework introduces a novel approach to tackle blockchain’s limitations in energy transactions.The integrated multi-signature process,involving neutral nodes,significantly enhances security and privacy.The scheme’s efficiency,validated through analytical assessments and simulations,indicates robustness against security threats and improved transactional speeds.This research underscores the potential for improved security and efficiency in blockchain-enabled energy trading systems.
文摘A secured and scalable Peer-to-Peer (P2P) energy trading platform can facilitate the integration of renewable energy and thus contribute to building sustainable energy infrastructure. The decentralized architecture of blockchain makes it a befitting candidate to actualize an efficient P2P energy trading market. However, for a sustainable and dynamic blockchain-based P2P energy trading platform, few critical aspects such as security, privacy and scalability need to be addressed with high priority. This paper proposes a blockchain-based solution for energy trading among the consumers which ensures the systems’ security, protects users’ privacy, and improves the overall scalability. More specifically, we develop a multilayered semi-permissioned blockchain-based platform to facilitate energy transactions. The practical byzantine fault tolerant algorithm is employed as the underlying consensus for verification and validation of transactions which ensures the system’s tolerance against internal error and malicious attacks. Additionally, we introduce the idea of quality of transaction (QoT)—a reward system for the participants of the network that eventually helps determine the participant’s eligibility for future transactions. The resiliency of the framework against the transaction malleability attack is demonstrated with two uses cases. Finally, a qualitative analysis is presented to indicate the system’s usefulness in improving the overall security, privacy, and scalability of the network.
文摘In the new competitive environment of the electricity market, risk analysis is a powerful tool to guide investors under both contract uncertainties and energy prices of the spot market. Moreover, simulation of spot price scenarios and evaluation of energy contracts performance, are also necessary to the decision maker, and in particular to the trader to foresee opportunities and possible threats in the trading activity. In this context, computational systems that allow what-if analysis, involving simulation of spot price, contract portfolio optimization and risk evaluation are rather important. This paper proposes a decision support system not only for solving the problem of contracts portfolio optimization, by using linear programming, but also to execute risks analysis of the contracts portfolio performance, with VaR and CVaR metrics. Realistic tests have demonstrated the efficiency of this system.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work through Research Groups under grant number(RGP.1/282/42)This work is also supported by the Faculty of Computer Science and Information Technology,University of Malaya,under Postgraduate Research Grant(PG035-2016A).
文摘Peer-to-Peer(P2P)electricity trading is a significant research area that offers maximum fulfilment for both prosumer and consumer.It also decreases the quantity of line loss incurred in Smart Grid(SG).But,uncertainities in demand and supply of the electricity might lead to instability in P2P market for both prosumer and consumer.In recent times,numerous Machine Learning(ML)-enabled load predictive techniques have been developed,while most of the existing studies did not consider its implicit features,optimal parameter selection,and prediction stability.In order to overcome fulfill this research gap,the current research paper presents a new Multi-Objective Grasshopper Optimisation Algorithm(MOGOA)with Deep Extreme Learning Machine(DELM)-based short-term load predictive technique i.e.,MOGOA-DELM model for P2P Energy Trading(ET)in SGs.The proposed MOGOA-DELM model involves four distinct stages of operations namely,data cleaning,Feature Selection(FS),prediction,and parameter optimization.In addition,MOGOA-based FS technique is utilized in the selection of optimum subset of features.Besides,DELM-based predictive model is also applied in forecasting the load requirements.The proposed MOGOA model is also applied in FS and the selection of optimalDELM parameters to improve the predictive outcome.To inspect the effectual outcome of the proposed MOGOA-DELM model,a series of simulations was performed using UK Smart Meter dataset.In the experimentation procedure,the proposed model achieved the highest accuracy of 85.80%and the results established the superiority of the proposed model in predicting the testing data.
基金funded in part by the Grant No.RG-15-135-43 from the Deanship of Scientific Research(DSR)at King Abdulaziz University in Saudi Arabia.
文摘The proliferation of distributed energy resources(DERs)and the large-scale electrification of transportation are driving forces behind the ongoing evolution for transforming traditionally passive consumers into prosumers(both consumers and producers)in coordinated power distribution network(PDN)and urban transportation network(UTN).In this new paradigm,peer-to-peer(P2P)energy trading is a promising energy management strategy for dynamically balancing the supply and demand in elec-tricity markets.In this paper,we propose the application of Blockchain(BC)to electric vehicle charging station(EVCS)op-erations to optimally transact energy in a hierarchical P2P framework.In the proposed framework,a decentralised privacy-preserving clearing mechanism is implemented in the transactive energy market(TEM)in which BC's smart contracts are applied in a coordinated PDN and UTN operation.The effectiveness of the proposed TEM and its solution approach are validated via numerical simulations which are performed on a modified IEEE 123-bus PDN and a modified Sioux Falls UTN.
基金supported by the National Natural Science Foundation of China(No.52107199)the International Corporation Project of Shanghai Science and Technology Commission(No.21190780300).
文摘Peer-to-peer(P2P)energy trading provides a promising solution for integrating distributed microgrids(MGs).However,most existing research works on P2P energy trading among MGs ignore the influence of the dynamic network usage fees imposed by the distribution system operator(DSO).Therefore,a method of P2P energy trading among MGs based on the optimal dynamic network usage fees is proposed in this paper to balance the benefits of DSO.The interaction between DSO and MG is formulated as a Stackelberg game,in which the existence and uniqueness of optimal dynamic network usage fees are proven.Additionally,the optimal dynamic network usage fees are obtained by transforming the bi-level problem into single-level mixed-integer quadratic programming using Karush-Kuhn-Tucker conditions.Furthermore,the underlying relationship among optimal dynamic network usage fees,electrical distance,and power flow is revealed,and the mechanism of the optimal dynamic network usage fee can further enhance P2P energy trading among MGs.Finally,simulation results on an enhanced IEEE 33-bus system demonstrate that the proposed mechanism achieves a 17.08%reduction in operation costs for MG while increasing DSO revenue by 15.36%.
基金supported by the National Key R&D Program of China(No.2022YFE0196100)the National Natural Science Foundation of China(Nos.12071460 and 72401205)+1 种基金the Special Innovation Projects of Ordinary Colleges and Universities in Guangdong Province(No.2024KTSCX258)Shenzhen Fundamental Research Program Stability Support Program for Higher Education Institution(No.20231127142912001).
文摘The rapid evolution of distributed energy resources,particularly photovoltaic systems,poses a formidable challenge in maintaining a delicate balance between energy supply and demand while minimizing costs.The integrated nature of distributed markets,blending centralized and decentralized elements,holds the promise of maximizing social welfare and significantly reducing overall costs,including computational and communication expenses.However,achieving this balance requires careful consideration of various hyperparameter sets,encompassing factors such as the number of communities,community detection methods,and trading mechanisms employed among nodes.To address this challenge,we introduce a groundbreaking neural network-based framework,the Energy Trading-based Artificial Neural Network(ET-ANN),which excels in performance compared to existing algorithms.Our experiments underscore the superiority of ET-ANN in minimizing total energy transaction costs while maximizing social welfare within the realm of photovoltaic networks.
基金supported in part by the National Natural Science Foundation of China(No.52107100)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX24-0657)。
文摘Peer-to-peer(P2P)energy trading enables an efficient regulation of distributed renewable energy among prosumers,implicitly promoting low-carbon operation.This study proposes a novel P2P energy trading scheme with coupled electricity-carbon(E/C)market that co-optimizes both power and carbon emission flows.To facilitate the low-carbon operations in the market,we introduce a prosumer-driven carbon-aware distribution locational marginal price(PDC-DLMP)to serve as a pricing signal for the distribution system operator(DSO).To efficiently determine the optimal trading solutions,we adopt a two-layer data-driven approach.The first layer employs a reinforcement learning algorithm named multi-agent twin-delayed deep deterministic policy gradient(MATD3);the second layer uses a deep neural network(DNN)driven surrogate model,which is designed to map the PDC-DLMP signals,thereby eliminating the need for direct DSO intervention during market operation.This approach protects the physical model parameters of the distribution network and ensures multi-level privacy protection.Simulation results validate the effectiveness of the proposed P2P energy trading scheme with coupled E/C market,demonstrating its ability to achieve both reduced carbon emissions and lower operational costs for microgrid prosumers.
基金supported by the National Natural Science Foundation of China(No.52177085).
文摘Peer-to-peer(P2P)energy trading in active distribution networks(ADNs)plays a pivotal role in promoting the efficient consumption of renewable energy sources.However,it is challenging to effectively coordinate the power dispatch of ADNs and P2P energy trading while preserving the privacy of different physical interests.Hence,this paper proposes a soft actor-critic algorithm incorporating distributed trading control(SAC-DTC)to tackle the optimal power dispatch of ADNs and the P2P energy trading considering privacy preservation among prosumers.First,the soft actor-critic(SAC)algorithm is used to optimize the control strategy of device in ADNs to minimize the operation cost,and the primary environmental information of the ADN at this point is published to prosumers.Then,a distributed generalized fast dual ascent method is used to iterate the trading process of prosumers and maximize their revenues.Subsequently,the results of trading are encrypted based on the differential privacy technique and returned to the ADN.Finally,the social welfare value consisting of ADN operation cost and P2P market revenue is utilized as a reward value to update network parameters and control strategies of the deep reinforcement learning.Simulation results show that the proposed SAC-DTC algorithm reduces the ADN operation cost,boosts the P2P market revenue,maximizes the social welfare,and exhibits high computational accuracy,demonstrating its practical application to the operation of power systems and power markets.
基金supported by the UK ARIA Safeguarded AI TA3 project‘SAINTES-Safe and scalable AI decisioN support Tools for Energy Systems’.
文摘The rapid development of distributed energy resources has led to an increasing number of prosumers enhancing their energy utilization,thereby raising the demands on energy management technologies.As a result,the development of future smart grids is becoming increasingly important,with a particular emphasis on integrating demand-side flexibility into electricity market.To facilitate distributed interaction among prosumers,the double-side auction market enables peer-to-peer(P2P)energy trading,maximizing the social welfare within the dynamic local electricity market.In this setup,prosumers can set their own bidding prices and optimize their operations and trading strategies.However,trading in double-side auction market faces limitations due to the complexity of the market clearing algorithm and the difficulty of predicting other participants’bidding behaviors.To address these challenges,this paper models the P2P energy trading problem in the double-side auction market as a multi-agent reinforcement learning(MARL)task.The concept of federated learning is introduced to enhance scalability among market participants while protecting the private information of individual prosumers.Additionally,the parameter-sharing framework is proposed to accelerate the learning process.To further improve the stability of MARL training,the global information of P2P energy trading price is integrated into the critic network.The proposed federated MARL algorithm is evaluated using a real-world open-source dataset from an European residential community of 250 households with a 15-minute resolution.The evaluation assesses both the training performance of the algorithm as well as the economic and operational benefits of the P2P energy trading market compared to a traditional electricity retail market.
基金This work was supported in part by the Peer-to-peer Energy Trading and Sharing-3M(multi-times,multi-scales,multi-qualities)project funded by EPSRC(No.EP/N03466X/1)in part,by ENERGY-IQ,a UK-Canada Power Forward Smart Grid Demonstrator project funded by The Department for Business,Energy and Industrial Strategy(BEIS)(No.7454460).
文摘Utility maximization is a major priority of prosumers participating in peer-to-peer energy trading and sharing(P2P-ETS).However,as more distributed energy resources integrate into the distribution network,the impact of the communication link becomes significant.We present a multi-commodity formulation that allows the dual-optimization of energy and communication resources in P2P-ETS.On one hand,the proposed algorithm minimizes the cost of energy generation and communication delay.On the other hand,it also maximizes the global utility of prosumers with fair resource allocation.We evaluate the algorithm in a variety of realistic conditions including a time-varying communication network with signal delay signal loss.The results show that the convergence is achieved in a fewer number of time steps than the previously proposed algorithms.It is further observed that the entities with a higher willingness to trade the energy acquire more satisfactions than others.
基金This work was supported in part by Shanghai Science and Technology Plan:Research and application for key technologies of public building virtual power plant based on distributed resource aggregation control,China(No.20dz1206200).
文摘Peer-to-peer(P2P)transactive energy trading offers a promising solution for facilitating the efficient and secure operation of a distribution system consisting of multiple prosumers.One critical but challenging task is how to avoid system network constraints to be violated for the distribution system integrated with extensive P2P transactive energy trades.This paper proposes a security constrained decentralized P2P transactive energy trading framework,which allows direct energy trades among neighboring prosumers in the distribution system with enhanced system efficiency and security in which no conventional intermediary is required.The P2P transactive energy trading problem is formulated based on the Nash Bargaining theory and decomposed into two subproblems,i.e.,an OPF problem(P1)and a payment bargaining problem(P2).A distributed optimization method based on the alternating direction method of multiplier(ADMM)is adopted as a privacy-preserving solution to the formulated security constrained P2P transactive energy trading model with ensured accuracy.Extensive case studies based on a modified 33-bus distribution system are presented to validate the effectiveness of the proposed security constrained decentralized P2P transactive energy trading framework in terms of efficiency improvement,loss reduction,and voltage security enhancement.
基金supported by the National Natural Science Foundation of China(No.52207108),and by the Science and Technology Project of State Grid Corporation of China(No.1400202099523 A0000).
文摘This paper investigates a double auction-based peer-to-peer(P2P)energy trading market for a community of renewable prosumers with private information on reservation price and quantity of energy to be traded.A novel competition padding auction(CPA)mechanism for P2P energy trading is proposed to address the budget deficit problem while holding the advantages of the widely-used Vickrey-Clarke-Groves mechanism.To illustrate the theoretical properties of the CPA mechanism,the sufficient conditions are identified for a truth-telling equilibrium with a budget surplus to exist,while further proving its asymptotical economic efficiency.In addition,the CPA mechanism is implemented through consortium blockchain smart contracts to create safer,faster,and larger P2P energy trading markets.The proposed mechanism is embedded into blockchain consensus protocols for high consensus efficiency,and the budget surplus of the CPA mechanism motivates the prosumers to manage the blockchain.Case studies are carried out to show the effectiveness of the proposed method.
