Self-Healing Power Supply Method Based on Topology Reconfiguration for Active Distribution System with Photovoltaic Generation Penetration

Bin Yu, Li-Guo Weng, Guozheng Zhou, Da-Hui Hong, Man Luo, Hao-Han Ying
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Abstract

In recent years, the reliability enhancement of distribution networks has attracted increasing attention due to the occurrence of a natural large-scale power blackout. This paper proposed a self-healing power supply method based on topology reconfiguration for the active distribution system with photovoltaic (PV) generation penetration. The aim for this self-healing method is to realize the power system self-healing and improve the network reliability. A multi-objective optimization model is established to maximize power supply restoration and minimize the number of switch actions. The genetic algorithm (GA) is used to solve the optimization problem and the graph theory is used to optimize each new solution to improve the efficiency of finding the optimal solution. An IEEE 33-node test network is used to verify the efficiency of the proposed self-healing power supply method through the single-node fault and multi-node fault scenarios. The numerical results confirm that the proposed method can improve network reliability.
光伏发电渗透有源配电系统基于拓扑重构的自愈供电方法
近年来,由于大规模自然停电事故的发生,配电网可靠性的提高日益受到人们的重视。针对具有光伏发电渗透的有源配电系统,提出了一种基于拓扑重构的自愈供电方法。这种自愈方法的目的是实现电力系统的自愈,提高电网的可靠性。建立了以最大限度恢复供电和最小开关动作次数为目标的多目标优化模型。利用遗传算法求解优化问题,并利用图论对每个新解进行优化,提高了寻找最优解的效率。采用IEEE 33节点测试网络,通过单节点故障和多节点故障场景验证了所提出的自愈供电方法的有效性。数值结果表明,该方法可以提高网络的可靠性。
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