Multi-Agent Deep Reinforcement Learning for Realistic Distribution System Voltage Control Using PV Inverters
Over the last few decades, the deployment of distributed solar photovoltaic (PV) systems has increased consistently. High PV penetration could cause adverse effects on the grid, such as voltage violations. This paper proposes a new distributed soft actor-critic based multi-agent deep reinforcement learning (SAC-MADRL) control solution to minimize the PV real power curtailment while keeping the grid voltage in an acceptable range. New reward functions have been designed to coordinate different agents during the learning process, yielding improved convergence. Comparison results with other control methods on a real feeder in western Colorado U.S. with 80% penetration of PVs demonstrate that the proposed method has better capability of effectively regulating voltage while minimizing the PV real power curtailment.
- Research Organization:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S); National Science Foundation (NSF)
- DOE Contract Number:
- AC36-08GO28308
- OSTI ID:
- 1909411
- Report Number(s):
- NREL/CP-5D00-84989; MainId:85762; UUID:2b7c8ab0-1d61-4c06-8208-8fb1b35caa14; MainAdminID:68451
- Country of Publication:
- United States
- Language:
- English
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