Research on online topology optimization strategy of distribution network based on deep reinforcement learning

胥鹏; 张悦; 王蓓蓓; 朱红; 刘少君; 许洪华

doi:10.3969/j.issn.1009-1831.2022.03.002

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Research on online topology optimization strategy of distribution network based on deep reinforcement learning

更新时间：2026-03-05

- Research on online topology optimization strategy of distribution network based on deep reinforcement learning
- Vol. 24, Issue 3, (2022)
- 作者机构：
  
  1. 东南大学电气工程学院,南京,210096
  2. 国网江苏省电力有限公司南京供电分公司,南京,210000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1009-1831.2022.03.002
  CLC：
- Published：2022
- 稿件说明：
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胥鹏, 张悦, 王蓓蓓, et al. Research on online topology optimization strategy of distribution network based on deep reinforcement learning[J]. 2022, 24(3).
DOI：

胥鹏, 张悦, 王蓓蓓, et al. Research on online topology optimization strategy of distribution network based on deep reinforcement learning[J]. 2022, 24(3). DOI： 10.3969/j.issn.1009-1831.2022.03.002.

摘要

分布式发电（distributed generation，DG）在电力系统中的应用日益广泛，会频繁地导致在线电压问题。在分布式发电产生较大预测误差时，依靠有限的电压调节装置逐渐不能满足在线电压调节的要求。针对这一问题，提出了一种灵活的拓扑控制方法，并采用一种深度强化学习算法对其进行建模与求解。该算法中的动作机制与图论中的分支交换概念相融合，有效简化了智能体的动作维度和动作空间。在标准IEEE 14节点系统上的算例表明，该算法具备较优秀的泛化能力；与传统算法相比，该算法在获得接近最优解的同时，显著提高了计算性能，满足在线电压调节的需求。

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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