Journal of Modern Power Systems and Clean Energy

ISSN 2196-5625 CN 32-1884/TK

Graph Attention Network Based Deep Reinforcement Learning for Voltage/var Control of Topologically Variable Power System
Author:
Affiliation:

1.School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China;2.School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China;3.Yunnan Electric Power Dispatching and Control Center, Kunming 650011, China

Fund Project:

This work was supported by China Southern Power Grid Co., Ltd. Yunnan Electric Power Dispatching Control Center (Cyber-physical-based “Source-network-load-storage” Coordinated Dispatch and Control Technologies and Application System Development, sub-project YNKJXM20222463). Many thanks to revision suggestions provided by Zhenhuan Ding from Anhui University, Hefei, China.

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    Abstract:

    The high proportion of renewable energy integration and the dynamic changes in grid topology necessitate the enhancement of voltage/var control (VVC) to manage voltage fluctuations more rapidly. Traditional model-based control algorithms are becoming increasingly incompetent for VVC due to their high model dependence and slow online computation speed. To alleviate these issues, this paper introduces a graph attention network (GAT) based deep reinforcement learning for VVC of topologically variable power system. Firstly, combining the physical information of the actual power grid, a physics-informed GAT is proposed and embedded into the proximal policy optimization (PPO) algorithm. The GAT-PPO algorithm can capture topological and spatial correlations among the node features to tackle topology changes. To address the slow training, the ReliefF-S algorithm identifies critical state variables, significantly reducing the dimensionality of state space. Then, the training samples retained in the experience buffer are designed to mitigate the sparse reward issue. Finally, the validation on the modified IEEE 39-bus system and an actual power grid demonstrates superior performance of the proposed algorithm compared with state-of-the-art algorithms, including PPO algorithm and twin delayed deep deterministic policy gradient (TD3) algorithm. The proposed algorithm exhibits enhanced convergence during training, faster solution speed, and improved VVC performance, even in scenarios involving grid topology changes and increased renewable energy integration. Meanwhile, in the adopted cases, the network loss is reduced by 6.9%, 10.8%, and 7.7%, respectively, demonstrating favorable economic outcomes.

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History
  • Received:September 27,2023
  • Revised:March 19,2024
  • Adopted:
  • Online: January 24,2025
  • Published: