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- Title
基于价值分解深度强化学习的分布式光伏 主动电压控制方法.
- Authors
刘 硕; 郭创新; 冯 斌; 张 勇; 王艺博
- Abstract
Aiming at the problem of active voltage control, deep reinforcement learning can effectively solve the shortcomings of mathematical optimization methods in accuracy and real-time performance. However, the traditional multi-agent deep reinforcement learning method has some problems, such as credit assignment, over-generalization, and so on, so it is difficult to learn the global optimal coordination strategy and the control effect is poor. Therefore, an active voltage control method of distributed photovoltaic based on value decomposition deep reinforcement learning is proposed. The active voltage control problem is modeled as a decentralized partially observable Markov decision process, and then based on the centralized training with decentralized execution framework, two improvement measures are proposed, including decomposed value network and centralized policy gradient. The global value network is decomposed into individual value networks and a mixing network, and the current policies of all agents are used for centralized parameter updating. The numerical results of the improved IEEE 33-bus distribution network system show that, the proposed method shows superior voltage stabilization and loss reduction performance, and has certain advantages in training speed and scene robustness.
- Subjects
DEEP reinforcement learning; PARTIALLY observable Markov decision processes; REINFORCEMENT learning; VOLTAGE control; MATHEMATICAL optimization; GLOBAL method of teaching
- Publication
Electric Power Automation Equipment / Dianli Zidonghua Shebei, 2023, Vol 43, Issue 10, p152
- ISSN
1006-6047
- Publication type
Article
- DOI
10.16081/j.epae.202309001