Improving the restoration efficiency of a distribution system is essential to enhance the ability of power systems to deal with extreme events.The distribution system restoration(DSR)depends on the interaction among t...Improving the restoration efficiency of a distribution system is essential to enhance the ability of power systems to deal with extreme events.The distribution system restoration(DSR)depends on the interaction among the electric network(EN),cyber network(CN),and traffic network(TN).However,the coordination of these three networks and codispatching of multiple recovery resources have been mostly neglected.This paper proposes a novel DSR framework,which is formulated as a mixed-integer linear programming(MILP)problem.The failures in cyber lines result in cyber blind areas,which restrict the normal operation of remote-controlled switches.To accelerate the recovery process,multiple recovery resources are utilized including electric maintenance crews(EMCs),cyber maintenance crews(CMCs),and emergency communication vehicles(ECVs).Specifically,CMCs and ECVs restore the cyber function of switches in cooperation,and EMCs repair damaged electric lines.The travel time of these three dispatchable resources is determined by TN.The effectiveness and superiority of the proposed framework are verified on the modified IEEE 33-node and 123-node test systems.展开更多
With the participation of large quantities of renewable energy in power system operations,their volatility and intermittence increases the difficulties and challenges of power system economic scheduling.Considering th...With the participation of large quantities of renewable energy in power system operations,their volatility and intermittence increases the difficulties and challenges of power system economic scheduling.Considering the uncertainty of renewable energy generation,based on the distributionally robust optimization method,a two-stage economic dispatch model is proposed to minimize the total operation costs.In this paper,it is assumed that the fluctuating of renewable power generation follows the unknown probability distribution that is restricted in an ambiguity set,which is established by utilizing the first-order moment information of available historical data.Furthermore,the theory of conditional value-at-risk is introduced to transform the model into a tractable model,which we call robust counterpart formulation.Based on the stochastic dual dynamic programming method,an improved iterative algorithm is proposed to solve the robust counterpart problem.Specifically,the convergence optimum can be obtained by the improved iterative algorithm,which performs a forward pass and backward pass repeatedly in each iterative process.Finally,by comparing with other methods,the results on the modified IEEE 6-bus,118-bus,and 300-bus system show the effectiveness and advantages of the proposed model and method.展开更多
基金supported by the National Natural Science Foundation of China(No.52007016).
文摘Improving the restoration efficiency of a distribution system is essential to enhance the ability of power systems to deal with extreme events.The distribution system restoration(DSR)depends on the interaction among the electric network(EN),cyber network(CN),and traffic network(TN).However,the coordination of these three networks and codispatching of multiple recovery resources have been mostly neglected.This paper proposes a novel DSR framework,which is formulated as a mixed-integer linear programming(MILP)problem.The failures in cyber lines result in cyber blind areas,which restrict the normal operation of remote-controlled switches.To accelerate the recovery process,multiple recovery resources are utilized including electric maintenance crews(EMCs),cyber maintenance crews(CMCs),and emergency communication vehicles(ECVs).Specifically,CMCs and ECVs restore the cyber function of switches in cooperation,and EMCs repair damaged electric lines.The travel time of these three dispatchable resources is determined by TN.The effectiveness and superiority of the proposed framework are verified on the modified IEEE 33-node and 123-node test systems.
基金supported by the National Natural Science Foundation of China(No.51777126)。
文摘With the participation of large quantities of renewable energy in power system operations,their volatility and intermittence increases the difficulties and challenges of power system economic scheduling.Considering the uncertainty of renewable energy generation,based on the distributionally robust optimization method,a two-stage economic dispatch model is proposed to minimize the total operation costs.In this paper,it is assumed that the fluctuating of renewable power generation follows the unknown probability distribution that is restricted in an ambiguity set,which is established by utilizing the first-order moment information of available historical data.Furthermore,the theory of conditional value-at-risk is introduced to transform the model into a tractable model,which we call robust counterpart formulation.Based on the stochastic dual dynamic programming method,an improved iterative algorithm is proposed to solve the robust counterpart problem.Specifically,the convergence optimum can be obtained by the improved iterative algorithm,which performs a forward pass and backward pass repeatedly in each iterative process.Finally,by comparing with other methods,the results on the modified IEEE 6-bus,118-bus,and 300-bus system show the effectiveness and advantages of the proposed model and method.
