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
An effective modeling and optimization method,which takes into account source-load-storage coordination,and full-time collaborative optimization within and outside micro-grids,is introduced.Considering the operational...An effective modeling and optimization method,which takes into account source-load-storage coordination,and full-time collaborative optimization within and outside micro-grids,is introduced.Considering the operational conditions of various resources and their interactions,an energy management model for microgrids is proposed aiming at maximization of renewable energy utilization and minimization of overall system costs.The model is suitable for both real-time pricing and time-of-use mechanisms.In microgrids,demand response and economic energy storage dispatch are introduced to enhance selfcoordination and self-balancing ability among different resources.Depending on whether there is still an imbalance between supply and demand after coordination within a niicrogrid,trade between it and external microgrids are optimized in a orderly manner by considering different transaction prices and usage rights.Finally,three different schemes are designed,where the Lagrangian multiplier method as well as a co-evolution algorithm are used to solve and analyze different examples,verifying the reliability and validity of the method proposed in this paper.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
A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncer...A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.展开更多
With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through the deployment ...With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through the deployment of energy storage.To solve the problem of the interests of different subjects in the operation of the energy storage power stations(ESS)and the integrated energy multi-microgrid alliance(IEMA),this paper proposes the optimization operation method of the energy storage power station and the IEMA based on the Stackelberg game.In the upper layer,ESS optimizes charging and discharging decisions through a dynamic pricing mechanism.In the lower layer,IEMA optimizes the output of various energy conversion coupled devices within the IEMA,as well as energy interaction and demand response(DR),based on the energy interaction prices provided by ESS.The results demonstrate that the optimization strategy proposed in this paper not only effectively balances the benefits of the IEMA and ESS but also enhances energy consumption rates and reduces IEMA energy costs.展开更多
The emerging novel energy infrastructures,such as energy communities,smart building-based microgrids,electric vehicles enabled mobile energy storage units raise the requirements for a more interconnective and interope...The emerging novel energy infrastructures,such as energy communities,smart building-based microgrids,electric vehicles enabled mobile energy storage units raise the requirements for a more interconnective and interoperable energy system.It leads to a transition from simple and isolated microgrids to relatively large-scale and complex interconnected microgrid systems named multi-microgrid clusters.In order to efficiently,optimally,and flexibly control multi-microgrid clusters,cross-disciplinary technologies such as power electronics,control theory,optimization algorithms,information and communication technologies,cyber-physical,and big-data analysis are needed.This paper introduces an overview of the relevant aspects for multi-microgrids,including the out-standing features,architectures,typical applications,existing control mechanisms,as well as the challenges.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
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
Multi-microgrids have many new characteristics, such as bi-directional power flow, flexible operation and variable fault current consisting of the different control strategy of inverter interfaced distributed generati...Multi-microgrids have many new characteristics, such as bi-directional power flow, flexible operation and variable fault current consisting of the different control strategy of inverter interfaced distributed generations (IIDGs), which all present challenges in multi-microgrid protection. In this paper, the current and voltage characteristics of different feeders are analyzed considering faults at different locations of the multi-microgrid. Based on the voltage and current distribution characteristics of the line parameters, a new protection scheme for the internal faults of multi-microgrids is proposed, which takes the change of phase difference and amplitude of measured bus admittances as the criterion. This proposed scheme has high sensitivity and reliability, is based on a simple principle, and can be easily adjusted. Simulation results using PSCAD/EMTDC verify the correctness and effectiveness of the protection scheme.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
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 Shanghai Sailing Program(20YF1418800)outstanding Ph.D.graduate development scholarship of Shanghai Jiao Tong University.
文摘An effective modeling and optimization method,which takes into account source-load-storage coordination,and full-time collaborative optimization within and outside micro-grids,is introduced.Considering the operational conditions of various resources and their interactions,an energy management model for microgrids is proposed aiming at maximization of renewable energy utilization and minimization of overall system costs.The model is suitable for both real-time pricing and time-of-use mechanisms.In microgrids,demand response and economic energy storage dispatch are introduced to enhance selfcoordination and self-balancing ability among different resources.Depending on whether there is still an imbalance between supply and demand after coordination within a niicrogrid,trade between it and external microgrids are optimized in a orderly manner by considering different transaction prices and usage rights.Finally,three different schemes are designed,where the Lagrangian multiplier method as well as a co-evolution algorithm are used to solve and analyze different examples,verifying the reliability and validity of the method proposed in this paper.
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.U24B20156)the National Defense Basic Scientific Research Program of China(No.JCKY2021204B051)the National Laboratory of Space Intelligent Control of China(Nos.HTKJ2023KL502005 and HTKJ2024KL502007)。
文摘A chance-constrained energy dispatch model based on the distributed stochastic model predictive control(DSMPC)approach for an islanded multi-microgrid system is proposed.An ambiguity set considering the inherent uncertainties of renewable energy sources(RESs)is constructed without requiring the full distribution knowledge of the uncertainties.The power balance chance constraint is reformulated within the framework of the distributionally robust optimization(DRO)approach.With the exchange of information and energy flow,each microgrid can achieve its local supply-demand balance.Furthermore,the closed-loop stability and recursive feasibility of the proposed algorithm are proved.The comparative results with other DSMPC methods show that a trade-off between robustness and economy can be achieved.
基金supported by the Guangxi Science and Technology Major Special Project (Project Number GUIKEAA22067079-1).
文摘With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through the deployment of energy storage.To solve the problem of the interests of different subjects in the operation of the energy storage power stations(ESS)and the integrated energy multi-microgrid alliance(IEMA),this paper proposes the optimization operation method of the energy storage power station and the IEMA based on the Stackelberg game.In the upper layer,ESS optimizes charging and discharging decisions through a dynamic pricing mechanism.In the lower layer,IEMA optimizes the output of various energy conversion coupled devices within the IEMA,as well as energy interaction and demand response(DR),based on the energy interaction prices provided by ESS.The results demonstrate that the optimization strategy proposed in this paper not only effectively balances the benefits of the IEMA and ESS but also enhances energy consumption rates and reduces IEMA energy costs.
基金supported by VILLUM FONDEN under the VILLUM Investigator Grant(No.25920):Center for Research on Microgrids(CROM)www.crom.et.aau.dk。
文摘The emerging novel energy infrastructures,such as energy communities,smart building-based microgrids,electric vehicles enabled mobile energy storage units raise the requirements for a more interconnective and interoperable energy system.It leads to a transition from simple and isolated microgrids to relatively large-scale and complex interconnected microgrid systems named multi-microgrid clusters.In order to efficiently,optimally,and flexibly control multi-microgrid clusters,cross-disciplinary technologies such as power electronics,control theory,optimization algorithms,information and communication technologies,cyber-physical,and big-data analysis are needed.This paper introduces an overview of the relevant aspects for multi-microgrids,including the out-standing features,architectures,typical applications,existing control mechanisms,as well as the challenges.
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.
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.
文摘Multi-microgrids have many new characteristics, such as bi-directional power flow, flexible operation and variable fault current consisting of the different control strategy of inverter interfaced distributed generations (IIDGs), which all present challenges in multi-microgrid protection. In this paper, the current and voltage characteristics of different feeders are analyzed considering faults at different locations of the multi-microgrid. Based on the voltage and current distribution characteristics of the line parameters, a new protection scheme for the internal faults of multi-microgrids is proposed, which takes the change of phase difference and amplitude of measured bus admittances as the criterion. This proposed scheme has high sensitivity and reliability, is based on a simple principle, and can be easily adjusted. Simulation results using PSCAD/EMTDC verify the correctness and effectiveness of the protection scheme.
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.
