Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of cha...Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of charge(SOC)information of the energy storage system,thereby reducing the system flexibility.In this study,we propose an adaptive coordinated control strategy that employs a two-layer fuzzy neural network controller(FNNC)to adapt to varying operating conditions in an IDCMG with multiple PV and battery energy storage system(BESS)units.The first-layer FNNC generates optimal operating mode commands for each DG,thereby avoiding the requirement for complex operating modes based on SOC segmentation.An optimal switching sequence logic prioritizes the most appropriate units during mode transitions.The second-layer FNNC dynamically adjusts the droop power to overcome power distribution challenges among DG groups.This helps in preventing the PV power from exceeding the limits and mitigating the risk of BESS overcharging or over-discharging.The simulation results indicate that the proposed strategy enhances the coordinated operation of multi-DG IDCMGs,thereby ensuring the efficient and safe utilization of PV and BESS.展开更多
With the proliferation of advanced communication technologies and the deepening interdependence between cyber and physical components,power distribution networks are subject to miscellaneous security risks induced by ...With the proliferation of advanced communication technologies and the deepening interdependence between cyber and physical components,power distribution networks are subject to miscellaneous security risks induced by malicious attackers.To address the issue,this paper proposes a security risk assessment method and a risk-oriented defense resource allocation strategy for cyber-physical distribution networks(CPDNs)against coordinated cyber attacks.First,an attack graph-based CPDN architecture is constructed,and representative cyber-attack paths are drawn considering the CPDN topology and the risk propagation process.The probability of a successful coordinated cyber attack and incurred security risks are quantitatively assessed based on the absorbing Markov chain model and National Institute of Standards and Technology(NIST)standard.Next,a risk-oriented defense resource allocation strategy is proposed for CPDNs in different attack scenarios.The tradeoff between security risk and limited resource budget is formulated as a multi-objective optimization(MOO)problem,which is solved by an efficient optimal Pareto solution generation approach.By employing a generational distance metric,the optimal solution is prioritized from the optimal Pareto set of the MOO and leveraged for subsequent atomic allocation of defense resources.Several case studies on a modified IEEE 123-node test feeder substantiate the efficacy of the proposed security risk assessment method and risk-oriented defense resource allocation strategy.展开更多
To optimize the placement of soft open points(SOPs)in active distribution networks(ADNs),many aspects should be considered,including the adjustment of transmission power,integration of distributed generations(DGs),coo...To optimize the placement of soft open points(SOPs)in active distribution networks(ADNs),many aspects should be considered,including the adjustment of transmission power,integration of distributed generations(DGs),coordination with conventional control methods,and maintenance of economic costs.To address this multi-objective planning problem,this study proposes a multi-stage coordinated robust optimization model for the SOP allocation in ADNs with photovoltaic(PV).First,two robust technical indices based on a robustness index are proposed to evaluate the operation conditions and robust optimality of the solutions.Second,the proposed coordinated allocation model aims to optimize the total cost,robust voltage offset index,robust utilization index,and voltage collapse proximity index.Third,the optimization methods of the multiand single-objective models are coordinated to solve the proposed multi-stage problem.Finally,the proposed model is implemented on an IEEE 33-node distribution system to verify its effectiveness.Numerical results show that the proposed index can better reveal voltage offset conditions as well as the SOP utilization,and the proposed model outperforms conventional ones in terms of robustness of placement plans and total cost.展开更多
Networked microgrids(NMGs)are critical in theaccommodation of distributed renewable energy.However,theexisting centralized state estimation(SE)cannot meet the demandsof NMGs in distributed energy management.The curren...Networked microgrids(NMGs)are critical in theaccommodation of distributed renewable energy.However,theexisting centralized state estimation(SE)cannot meet the demandsof NMGs in distributed energy management.The currentestimator is also not robust against bad data.This study introducesthe concepts of relative error to construct an improvedrobust SE(IRSE)optimization model with mixed-integer nonlinearprogramming(MINLP)that overcomes the disadvantage ofinaccurate results derived from different measurements whenthe same tolerance range is considered in the robust SE(RSE).To improve the computation efficiency of the IRSE optimizationmodel,the number of binary variables is reduced based on theprojection statistics and normalized residual methods,which effectivelyavoid the problem of slow convergence or divergenceof the algorithm caused by too many integer variables.Finally,an embedded consensus alternating direction of multiplier method(ADMM)distribution algorithm based on outer approximation(OA)is proposed to solve the IRSE optimization model.This algorithm can accurately detect bad data and obtain SE resultsthat communicate only the boundary coupling informationwith neighbors.Numerical tests show that the proposed algorithmeffectively detects bad data,obtains more accurate SE results,and ensures the protection of private information in all microgrids.展开更多
针对配电网与多微电网耦合系统调度中分布式电源出力不确定性以及运行效率低下等问题,提出基于深度学习方法集成的配电网与微电网互联系统分布鲁棒机会约束运行优化调度模型。构建了基于概率输出的支持向量机、贝叶斯神经网络、深度信...针对配电网与多微电网耦合系统调度中分布式电源出力不确定性以及运行效率低下等问题,提出基于深度学习方法集成的配电网与微电网互联系统分布鲁棒机会约束运行优化调度模型。构建了基于概率输出的支持向量机、贝叶斯神经网络、深度信念网络的微电网可再生能源和负荷不确定性概率集合;建立了D-S(dempster-shafer)证据理论信息集成框架,提出了基于Kappa系数与准确率权重的证据修正方法,进而得到更高精度不确定性概率集合,得出源荷功率的概率分布模糊集。接着,建立配电网与多微电网两阶段滚动调度优化模型,即第一阶段预调度模型和第二阶段实时调控模型,第一阶段以实现配电网与多微电网区域全局运行经济最优为优化目标进行能量预分配;阶段二为实时运行调控阶段,考虑微电网内新能源实时出力的不确定性对分布式电源进行实时调控;两阶段鲁棒经济调度模型采用改进列约束生成方法(column-and-constraint generation,C&CG)和交叉方向乘子(alternating direction multiplier method,ADMM)和结合的内外双环算法分布式求解。仿真结果表明,有效提高了源荷预测不确定性下配-微电网市场安全可靠运行,提高了互联系统的新能源消纳率及经济收益。展开更多
基金supported by National Key R&D Program of ChinaunderGrant,(2021YFB2601403).
文摘Conventional coordinated control strategies for DC bus voltage signal(DBS)in islanded DC microgrids(IDCMGs)struggle with coordinating multiple distributed generators(DGs)and cannot effectively incorporate state of charge(SOC)information of the energy storage system,thereby reducing the system flexibility.In this study,we propose an adaptive coordinated control strategy that employs a two-layer fuzzy neural network controller(FNNC)to adapt to varying operating conditions in an IDCMG with multiple PV and battery energy storage system(BESS)units.The first-layer FNNC generates optimal operating mode commands for each DG,thereby avoiding the requirement for complex operating modes based on SOC segmentation.An optimal switching sequence logic prioritizes the most appropriate units during mode transitions.The second-layer FNNC dynamically adjusts the droop power to overcome power distribution challenges among DG groups.This helps in preventing the PV power from exceeding the limits and mitigating the risk of BESS overcharging or over-discharging.The simulation results indicate that the proposed strategy enhances the coordinated operation of multi-DG IDCMGs,thereby ensuring the efficient and safe utilization of PV and BESS.
基金supported by the National Natural Science Foundation of China(No.52377086)the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.SJCX23_0063)。
文摘With the proliferation of advanced communication technologies and the deepening interdependence between cyber and physical components,power distribution networks are subject to miscellaneous security risks induced by malicious attackers.To address the issue,this paper proposes a security risk assessment method and a risk-oriented defense resource allocation strategy for cyber-physical distribution networks(CPDNs)against coordinated cyber attacks.First,an attack graph-based CPDN architecture is constructed,and representative cyber-attack paths are drawn considering the CPDN topology and the risk propagation process.The probability of a successful coordinated cyber attack and incurred security risks are quantitatively assessed based on the absorbing Markov chain model and National Institute of Standards and Technology(NIST)standard.Next,a risk-oriented defense resource allocation strategy is proposed for CPDNs in different attack scenarios.The tradeoff between security risk and limited resource budget is formulated as a multi-objective optimization(MOO)problem,which is solved by an efficient optimal Pareto solution generation approach.By employing a generational distance metric,the optimal solution is prioritized from the optimal Pareto set of the MOO and leveraged for subsequent atomic allocation of defense resources.Several case studies on a modified IEEE 123-node test feeder substantiate the efficacy of the proposed security risk assessment method and risk-oriented defense resource allocation strategy.
基金supported in part by the National Natural Science Foundation of China(General Program)(No.52077017)the International Postdoctoral Exchange Fellowship Program(Talent-Introduction Program)(No.YJ20210337)。
文摘To optimize the placement of soft open points(SOPs)in active distribution networks(ADNs),many aspects should be considered,including the adjustment of transmission power,integration of distributed generations(DGs),coordination with conventional control methods,and maintenance of economic costs.To address this multi-objective planning problem,this study proposes a multi-stage coordinated robust optimization model for the SOP allocation in ADNs with photovoltaic(PV).First,two robust technical indices based on a robustness index are proposed to evaluate the operation conditions and robust optimality of the solutions.Second,the proposed coordinated allocation model aims to optimize the total cost,robust voltage offset index,robust utilization index,and voltage collapse proximity index.Third,the optimization methods of the multiand single-objective models are coordinated to solve the proposed multi-stage problem.Finally,the proposed model is implemented on an IEEE 33-node distribution system to verify its effectiveness.Numerical results show that the proposed index can better reveal voltage offset conditions as well as the SOP utilization,and the proposed model outperforms conventional ones in terms of robustness of placement plans and total cost.
基金supported by the National Natural Science Foundation of China(No.5217070269).
文摘Networked microgrids(NMGs)are critical in theaccommodation of distributed renewable energy.However,theexisting centralized state estimation(SE)cannot meet the demandsof NMGs in distributed energy management.The currentestimator is also not robust against bad data.This study introducesthe concepts of relative error to construct an improvedrobust SE(IRSE)optimization model with mixed-integer nonlinearprogramming(MINLP)that overcomes the disadvantage ofinaccurate results derived from different measurements whenthe same tolerance range is considered in the robust SE(RSE).To improve the computation efficiency of the IRSE optimizationmodel,the number of binary variables is reduced based on theprojection statistics and normalized residual methods,which effectivelyavoid the problem of slow convergence or divergenceof the algorithm caused by too many integer variables.Finally,an embedded consensus alternating direction of multiplier method(ADMM)distribution algorithm based on outer approximation(OA)is proposed to solve the IRSE optimization model.This algorithm can accurately detect bad data and obtain SE resultsthat communicate only the boundary coupling informationwith neighbors.Numerical tests show that the proposed algorithmeffectively detects bad data,obtains more accurate SE results,and ensures the protection of private information in all microgrids.
文摘针对配电网与多微电网耦合系统调度中分布式电源出力不确定性以及运行效率低下等问题,提出基于深度学习方法集成的配电网与微电网互联系统分布鲁棒机会约束运行优化调度模型。构建了基于概率输出的支持向量机、贝叶斯神经网络、深度信念网络的微电网可再生能源和负荷不确定性概率集合;建立了D-S(dempster-shafer)证据理论信息集成框架,提出了基于Kappa系数与准确率权重的证据修正方法,进而得到更高精度不确定性概率集合,得出源荷功率的概率分布模糊集。接着,建立配电网与多微电网两阶段滚动调度优化模型,即第一阶段预调度模型和第二阶段实时调控模型,第一阶段以实现配电网与多微电网区域全局运行经济最优为优化目标进行能量预分配;阶段二为实时运行调控阶段,考虑微电网内新能源实时出力的不确定性对分布式电源进行实时调控;两阶段鲁棒经济调度模型采用改进列约束生成方法(column-and-constraint generation,C&CG)和交叉方向乘子(alternating direction multiplier method,ADMM)和结合的内外双环算法分布式求解。仿真结果表明,有效提高了源荷预测不确定性下配-微电网市场安全可靠运行,提高了互联系统的新能源消纳率及经济收益。
