As the current global environment is deteriorating,distributed renewable energy is gradually becoming an important member of the energy internet.Blockchain,as a decentralized distributed ledger with decentralization,t...As the current global environment is deteriorating,distributed renewable energy is gradually becoming an important member of the energy internet.Blockchain,as a decentralized distributed ledger with decentralization,traceability and tamper-proof features,is an importantway to achieve efficient consumption andmulti-party supply of new energy.In this article,we establish a blockchain-based mathematical model of multiple microgrids and microgrid aggregators’revenue,consider the degree of microgrid users’preference for electricity thus increasing users’reliance on the blockchainmarket,and apply the one-master-multiple-slave Stackelberg game theory to solve the energy dispatching strategy when each market entity pursues the maximum revenue.The simulation results show that the blockchain-based dynamic game of the multi-microgrid market can effectively increase the revenue of both microgrids and aggregators and improve the utilization of renewable energy.展开更多
Contingencies,such as behavior shifts of microgrid operators(MGOs)and abrupt weather fluctuations,significantly impact the economic operations of multi-microgrids(MMGs).To address these contingencies and enhance the e...Contingencies,such as behavior shifts of microgrid operators(MGOs)and abrupt weather fluctuations,significantly impact the economic operations of multi-microgrids(MMGs).To address these contingencies and enhance the economic and autonomous performance of MGOs,a self-organizing energy management modeling approach is proposed.A second-order stochastic dynamical equation(SDE)is developed to accurately characterize the self-organizing evolution of the operating cost of MGO incurred by contingencies.Firstly,an operating model of MMG relying on two random graph-driven information matrices is constructed and the order parameters are introduced to extract the probabilistic properties of variations in operating cost.Subsequently,these order parameters,which assist individuals in effectively capturing system correlations and updating state information,are incorporated as inputs into second-order SDE.The second-order SDE is then solved by using the finite difference method(FDM)within a loop-structured solution framework.Case studies conducted within a practical area in China validate that the proposed self-organizing energy management model(SEMM)demonstrates spontaneous improvements in economic performance compared with conventional models.展开更多
Uncertainties on both the source and demand side pose challenges for the day-to-day management of distributed power systems,such as microgrids(MGs).To harness the potential for flexible scheduling within MGs and to ex...Uncertainties on both the source and demand side pose challenges for the day-to-day management of distributed power systems,such as microgrids(MGs).To harness the potential for flexible scheduling within MGs and to explore the economic and environmental advantages of cooperative interactions between multiple MGs,this paper develops a two-stage power-sharing model for day-ahead and intraday operations across multi-MGs.The intraday stage,which takes into account the prediction errors caused by uncertainty,refines the day-ahead results by factoring in real-time fluctuations in renewable energy sources and loads,changes in equipment status,and the scheduling of various demand response resources.At the end of the intraday optimal scheduling process,the profits from MGs are allocated according to a generalized Nash equilibrium.A case study involving three MGs within a distribution network,examining both island and cooperative operation strategies,demonstrates the practicality and effectiveness of the proposed approach.展开更多
Consensus has been widely used in distributed control,where distributed individuals need to share their states with their neighbors through communication links to achieve a common goal.However,the objectives of existi...Consensus has been widely used in distributed control,where distributed individuals need to share their states with their neighbors through communication links to achieve a common goal.However,the objectives of existing consensus-based control strategies for energy systems seldom address battery degradation cost,which is an important performance indicator to assess the performance and sustainability of battery energy storage(BES)systems.In this paper,we propose a consensusbased optimal control strategy for multi-microgrid systems,aiming at multiple control objectives including minimizing battery degradation cost.Distributed consensus is used to synchronize the ratio of BES output power to BES state-of-charge(SoC)among all microgrids while each microgrid is trying to reach its individual optimality.In order to reduce the pressure of communication links and prevent excessive exposure of local information,this ratio is the only state variable shared between microgrids.Since our complex nonlinear problem might be difficult to solve by traditional methods,we design a compressive sensing-based gradient descent(CSGD)method to solve the control problem.Numerical simulation results show that our control strategy results in a 74.24%reduction in battery degradation cost on average compared to the control method without considering battery degradation.In addition,the compressive sensing method causes an 89.12%reduction in computation time cost compared to the traditional Monte Carlo(MC)method in solving the control problem.展开更多
With the new power system growth,cooperative scheduling among multiple microgrids(MMG)is emerging.Complex energy coupling relationship within the MMG system poses challenges for achieving energy complementarity.Tradit...With the new power system growth,cooperative scheduling among multiple microgrids(MMG)is emerging.Complex energy coupling relationship within the MMG system poses challenges for achieving energy complementarity.Tradition-alMMGscheduling faces limitations in achieving economic scheduling and privacy protection due to the extensive need for energy data.To address the MMG scheduling issue,this paper proposes a novel distributed intelligent cooperative scheduling model,named E-Hive,for optimal economic operation.We employ two techniques into E-Hive:1)We develop a multi-agent deep reinforcement learning module to achieve cooperative scheduling in MMG.2)We employ a distributed architecture for inter-microgrid communication while ensuring privacy protection of energy data.Evaluation results show that the E-Hive model enables cooperative scheduling relying solely on local microgrid data,preserving the privacy of each microgrid.Furthermore,the operating cost is reduced by up to 16.4%compared to state-of-the-art methods,enhancing the economic benefit.展开更多
传统多微网系统的集中式优化策略计算时间长,而以交替方向乘子法(alternating direction method of muitipiers,ADMM)为代表的分布式优化算法求解效率取决于目标函数的拉格朗日增广函数的求解难度,很难适用于复杂多微网系统。针对该问题...传统多微网系统的集中式优化策略计算时间长,而以交替方向乘子法(alternating direction method of muitipiers,ADMM)为代表的分布式优化算法求解效率取决于目标函数的拉格朗日增广函数的求解难度,很难适用于复杂多微网系统。针对该问题,提出了一种基于非精确广义不定邻近交替方向乘子法(the inexact generalized ADMM with indefinite proximal term,IGADMM-IPT)的多微网系统分布式协调优化方案。首先,构建多微网系统的分层优化架构和各可调节设备动态模型;然后,基于可再生能源出力、负荷需求的差值和可调节设备出力阈值确定各微网可共享发电量和储能容量;接着,基于多微网系统运行成本最低构建全局共享目标函数,利用IGADMM-IPT对该优化问题迭代求解;最后,在8个微网和一个直连设备群通过公共母线互联的场景进行仿真。结果显示,在一天内利用IGADMM-IPT获取多微网系统运行成本最低优化方案所需时间比ADMM少21.38%。展开更多
基金This research was funded by the NSFC under Grant No.61803279in part by the Qing Lan Project of Jiangsu,in part by the China Postdoctoral Science Foundation under Grant Nos.2020M671596 and 2021M692369+3 种基金in part by the Suzhou Science and Technology Development Plan Project(Key Industry Technology Innovation)under Grant No.SYG202114in part by the Open Project Funding from Anhui Province Key Laboratory of Intelligent Building and Building Energy Saving,Anhui Jianzhu University,under Grant No.IBES2021KF08in part by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.KYCX23_3320in part by the Postgraduate Research&Practice Innovation Program of Jiangsu Province under Grant No.SJCX22_1585.
文摘As the current global environment is deteriorating,distributed renewable energy is gradually becoming an important member of the energy internet.Blockchain,as a decentralized distributed ledger with decentralization,traceability and tamper-proof features,is an importantway to achieve efficient consumption andmulti-party supply of new energy.In this article,we establish a blockchain-based mathematical model of multiple microgrids and microgrid aggregators’revenue,consider the degree of microgrid users’preference for electricity thus increasing users’reliance on the blockchainmarket,and apply the one-master-multiple-slave Stackelberg game theory to solve the energy dispatching strategy when each market entity pursues the maximum revenue.The simulation results show that the blockchain-based dynamic game of the multi-microgrid market can effectively increase the revenue of both microgrids and aggregators and improve the utilization of renewable energy.
基金supported by the National Natural Science Foundation of China(No.52077035)the Liaoning Revitalization Talents Program of China(No.XLYC2007181).
文摘Contingencies,such as behavior shifts of microgrid operators(MGOs)and abrupt weather fluctuations,significantly impact the economic operations of multi-microgrids(MMGs).To address these contingencies and enhance the economic and autonomous performance of MGOs,a self-organizing energy management modeling approach is proposed.A second-order stochastic dynamical equation(SDE)is developed to accurately characterize the self-organizing evolution of the operating cost of MGO incurred by contingencies.Firstly,an operating model of MMG relying on two random graph-driven information matrices is constructed and the order parameters are introduced to extract the probabilistic properties of variations in operating cost.Subsequently,these order parameters,which assist individuals in effectively capturing system correlations and updating state information,are incorporated as inputs into second-order SDE.The second-order SDE is then solved by using the finite difference method(FDM)within a loop-structured solution framework.Case studies conducted within a practical area in China validate that the proposed self-organizing energy management model(SEMM)demonstrates spontaneous improvements in economic performance compared with conventional models.
基金supported in part by the National Natural Science Foundation of China(No.52307090)in part by the Major Research Special Project on Science and Technology of Jiangxi Province(No.20223AAE02011).
文摘Uncertainties on both the source and demand side pose challenges for the day-to-day management of distributed power systems,such as microgrids(MGs).To harness the potential for flexible scheduling within MGs and to explore the economic and environmental advantages of cooperative interactions between multiple MGs,this paper develops a two-stage power-sharing model for day-ahead and intraday operations across multi-MGs.The intraday stage,which takes into account the prediction errors caused by uncertainty,refines the day-ahead results by factoring in real-time fluctuations in renewable energy sources and loads,changes in equipment status,and the scheduling of various demand response resources.At the end of the intraday optimal scheduling process,the profits from MGs are allocated according to a generalized Nash equilibrium.A case study involving three MGs within a distribution network,examining both island and cooperative operation strategies,demonstrates the practicality and effectiveness of the proposed approach.
基金supported by the BNRist Program(No.BNR 2021TD01009)Fundamental Research Funds for the Central Universities of China(Grant No.B200201071).
文摘Consensus has been widely used in distributed control,where distributed individuals need to share their states with their neighbors through communication links to achieve a common goal.However,the objectives of existing consensus-based control strategies for energy systems seldom address battery degradation cost,which is an important performance indicator to assess the performance and sustainability of battery energy storage(BES)systems.In this paper,we propose a consensusbased optimal control strategy for multi-microgrid systems,aiming at multiple control objectives including minimizing battery degradation cost.Distributed consensus is used to synchronize the ratio of BES output power to BES state-of-charge(SoC)among all microgrids while each microgrid is trying to reach its individual optimality.In order to reduce the pressure of communication links and prevent excessive exposure of local information,this ratio is the only state variable shared between microgrids.Since our complex nonlinear problem might be difficult to solve by traditional methods,we design a compressive sensing-based gradient descent(CSGD)method to solve the control problem.Numerical simulation results show that our control strategy results in a 74.24%reduction in battery degradation cost on average compared to the control method without considering battery degradation.In addition,the compressive sensing method causes an 89.12%reduction in computation time cost compared to the traditional Monte Carlo(MC)method in solving the control problem.
文摘With the new power system growth,cooperative scheduling among multiple microgrids(MMG)is emerging.Complex energy coupling relationship within the MMG system poses challenges for achieving energy complementarity.Tradition-alMMGscheduling faces limitations in achieving economic scheduling and privacy protection due to the extensive need for energy data.To address the MMG scheduling issue,this paper proposes a novel distributed intelligent cooperative scheduling model,named E-Hive,for optimal economic operation.We employ two techniques into E-Hive:1)We develop a multi-agent deep reinforcement learning module to achieve cooperative scheduling in MMG.2)We employ a distributed architecture for inter-microgrid communication while ensuring privacy protection of energy data.Evaluation results show that the E-Hive model enables cooperative scheduling relying solely on local microgrid data,preserving the privacy of each microgrid.Furthermore,the operating cost is reduced by up to 16.4%compared to state-of-the-art methods,enhancing the economic benefit.
文摘传统多微网系统的集中式优化策略计算时间长,而以交替方向乘子法(alternating direction method of muitipiers,ADMM)为代表的分布式优化算法求解效率取决于目标函数的拉格朗日增广函数的求解难度,很难适用于复杂多微网系统。针对该问题,提出了一种基于非精确广义不定邻近交替方向乘子法(the inexact generalized ADMM with indefinite proximal term,IGADMM-IPT)的多微网系统分布式协调优化方案。首先,构建多微网系统的分层优化架构和各可调节设备动态模型;然后,基于可再生能源出力、负荷需求的差值和可调节设备出力阈值确定各微网可共享发电量和储能容量;接着,基于多微网系统运行成本最低构建全局共享目标函数,利用IGADMM-IPT对该优化问题迭代求解;最后,在8个微网和一个直连设备群通过公共母线互联的场景进行仿真。结果显示,在一天内利用IGADMM-IPT获取多微网系统运行成本最低优化方案所需时间比ADMM少21.38%。
