期刊
IEEE TRANSACTIONS ON POWER SYSTEMS
卷 35, 期 5, 页码 4120-4123出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TPWRS.2020.3000652
关键词
Voltage control; Training; Inverters; Markov processes; Games; Artificial neural networks; Real-time systems; Voltage regulation; multi-agent deep reinforcem-ent learning; coordinated control; distribution system
资金
- National Key Research and Development Program of China [2018YFB0905200]
- Sichuan Distinguished Young Scholars [20JCQN0213]
This paper proposes a multi-agent deep reinforcement learning-based approach for distribution system voltage regulation with high penetration of photovoltaics (PVs). The designed agents can learn the coordinated control strategies from historical data through the counter-training of local policy networks and centric critic networks. The learned strategies allow us to perform online coordinated control. Comparative results with other methods show the enhanced control capability of the proposed method under various conditions.
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