Research on online topology optimization strategy of distribution network based on deep reinforcement learning
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(1. School of Electrical Engineering, Southeast University, Nanjing 210096, China;2. Nanjing Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China)

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TM712;TM734

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    Abstract:

    The increasing use of distributed generation(DG)in power systems can result in frequent online voltage problems.In case of large prediction error of DG, the requirement of online volt-age regulation can not be met by limited voltage regulation devices.In order to solve this problem, a flexible topology control method is proposed, and a deep reinforcement learning algorithm is used to model and solve it. The action mechanism in this algorithm combines the branch exchange concept in graph theory, and effectively simplifies the action dimension and action space. The analysis of the calculation examples on the standard IEEE 14 node system shows that the algorithm has excellent generalization ability. Compared with the previous methods, the proposed algorithm can obtain the closest optimal solution, improve the performance of the calculation significantly and meet the needs of online voltage regulation.

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胥鹏,张悦,王蓓蓓,朱红,刘少君,许洪华.基于深度强化学习的配电网在线拓扑优化策略研究[J].电力需求侧管理英文版,2022,24(3):09-14.

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History
  • Received:January 20,2022
  • Revised:March 28,2022
  • Adopted:
  • Online: May 24,2022
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