This paper investigates the issues of topology design and capacity configuration in multi-microgrid(MMG)systems.Firstly,we analyze the limitations of current researches about MMG planning,which mainly focus on either ...This paper investigates the issues of topology design and capacity configuration in multi-microgrid(MMG)systems.Firstly,we analyze the limitations of current researches about MMG planning,which mainly focus on either topology design or capacity configuration separately,and propose the idea of joint planning simultaneously considering both aspects.Secondly,we present a two-stage stochastic optimization model aimed at minimizing the planning and operational costs to derive the solutions for topology design and capacity configuration.Moreover,various constraints are taken into account,including maximum equipment capacity,load shedding penalty cost,wind and solar power curtailment costs,and energy coordination among microgrids.Finally,simulation experiments are conducted using the islanded multi-microgrid(IMMG)as the research object,and additional comparative experiments are included to demonstrate the effectiveness and feasibility of the proposed model.Experimental results demonstrate that our method can significantly improve the economic benefits of MMG systems and enhance their penetration of renewable energy,which helps promote the economic development and stable operation of MMG systems.展开更多
The multi-directional flow of energy in a multi-microgrid(MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We pr...The multi-directional flow of energy in a multi-microgrid(MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We propose an approach to centrally train all the agents to achieve coordinated control through an individual attention mechanism with a deep dense neural network for reinforcement learning. The attention mechanism and novel deep dense neural network allow each agent to attend to the specific information that is most relevant to its reward. When training is complete, the proposed approach can construct decisions to manage multiple energy sources within the MMG system in a fully decentralized manner. Using only local information, the proposed approach can coordinate multiple internal energy allocations within individual microgrids and external multilateral multi-energy interactions among interconnected microgrids to enhance the operational economy and voltage stability. Comparative results demonstrate that the cost achieved by the proposed approach is at most 71.1% lower than that obtained by other multi-agent deep reinforcement learning approaches.展开更多
Power sharing can improve the benefit of the multi-microgrid(MMG)system.However,the information disclosure may appear during the sharing process,which would bring privacy risk to a local microgrid.Actually,the risk an...Power sharing can improve the benefit of the multi-microgrid(MMG)system.However,the information disclosure may appear during the sharing process,which would bring privacy risk to a local microgrid.Actually,the risk and coordination cost are different in different sharing modes.Therefore,this paper develops a decision-making method to decide the most suitable one of three mostly used sharing modes(i.e.,cooperative game with complete information,cooperative game with incomplete information,and noncooperative game).Firstly,power sharing paradigms and coordination mechanisms in the three modes are formulated in detail.Particularly,different economic operation models of MMG system are included to analyze the economic benefit from different sharing modes.Based on the different disclosed information,the risk cost is evaluated by using the simplified fuzzy analytic hierarchy process(FAHP).And the coordination cost for different sharing modes is expressed in different functions.In addition,a hierarchical evaluation system including three decision-making factors(e.g.,economics,risk,and coordination)is set up.Meanwhile,a combination weighting method(e.g.,the simplified FAHP combined with the anti-entropy weight method)is applied to obtain the weight of each factor for comprehensive evaluation.Finally,the optimal sharing solution of MMG system is decided by comparing and analyzing the difference among the three sharing modes.Numerical results validate that the proposed method can provide a reference to deciding a suitable sharing mode.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
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
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support provided by the Major Open Project of Xiangiiang Laboratory(No.22XJ02003)the National Natural Science Foundation of China(Nos.62122093,72301287,and 72071205)+3 种基金the Science and Technology Project for Young and Middle-aged Talents of Hunan(No.2023T-Z03)the Science and Technology Innovation Program of Hunan Province(No.2023RC1002)the Hunan Provincial Natural Science Foundation of China(No.2022JJ10069)the Scientific Proiect of National University of Defense Technology(Nos.ZK21-07 and 23-ZZCX-JDZ-28).
文摘This paper investigates the issues of topology design and capacity configuration in multi-microgrid(MMG)systems.Firstly,we analyze the limitations of current researches about MMG planning,which mainly focus on either topology design or capacity configuration separately,and propose the idea of joint planning simultaneously considering both aspects.Secondly,we present a two-stage stochastic optimization model aimed at minimizing the planning and operational costs to derive the solutions for topology design and capacity configuration.Moreover,various constraints are taken into account,including maximum equipment capacity,load shedding penalty cost,wind and solar power curtailment costs,and energy coordination among microgrids.Finally,simulation experiments are conducted using the islanded multi-microgrid(IMMG)as the research object,and additional comparative experiments are included to demonstrate the effectiveness and feasibility of the proposed model.Experimental results demonstrate that our method can significantly improve the economic benefits of MMG systems and enhance their penetration of renewable energy,which helps promote the economic development and stable operation of MMG systems.
基金supported by Sichuan Province Innovative Talent Funding Project for Postdoctoral Fellows (No. BX202210)。
文摘The multi-directional flow of energy in a multi-microgrid(MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We propose an approach to centrally train all the agents to achieve coordinated control through an individual attention mechanism with a deep dense neural network for reinforcement learning. The attention mechanism and novel deep dense neural network allow each agent to attend to the specific information that is most relevant to its reward. When training is complete, the proposed approach can construct decisions to manage multiple energy sources within the MMG system in a fully decentralized manner. Using only local information, the proposed approach can coordinate multiple internal energy allocations within individual microgrids and external multilateral multi-energy interactions among interconnected microgrids to enhance the operational economy and voltage stability. Comparative results demonstrate that the cost achieved by the proposed approach is at most 71.1% lower than that obtained by other multi-agent deep reinforcement learning approaches.
基金supported by the National Key R&D Program of China(No.2019YFE0123600)the National Natural Science Foundation of China(No.52077146)the Sichuan Science and Technology Program(Grant No.2021YFSY0052).
文摘Power sharing can improve the benefit of the multi-microgrid(MMG)system.However,the information disclosure may appear during the sharing process,which would bring privacy risk to a local microgrid.Actually,the risk and coordination cost are different in different sharing modes.Therefore,this paper develops a decision-making method to decide the most suitable one of three mostly used sharing modes(i.e.,cooperative game with complete information,cooperative game with incomplete information,and noncooperative game).Firstly,power sharing paradigms and coordination mechanisms in the three modes are formulated in detail.Particularly,different economic operation models of MMG system are included to analyze the economic benefit from different sharing modes.Based on the different disclosed information,the risk cost is evaluated by using the simplified fuzzy analytic hierarchy process(FAHP).And the coordination cost for different sharing modes is expressed in different functions.In addition,a hierarchical evaluation system including three decision-making factors(e.g.,economics,risk,and coordination)is set up.Meanwhile,a combination weighting method(e.g.,the simplified FAHP combined with the anti-entropy weight method)is applied to obtain the weight of each factor for comprehensive evaluation.Finally,the optimal sharing solution of MMG system is decided by comparing and analyzing the difference among the three sharing modes.Numerical results validate that the proposed method can provide a reference to deciding a suitable sharing mode.
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.
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.
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%。
