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 l...

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Bibliographic Details
Main Authors: Pei, Yansong, Yao, Yiyun, Zhao, Junbo, Ding, Fei, Wang, Jiyu
Format: Conference Proceeding
Language:English
Subjects:
Convergence
Distribution system
Inverters
multi-agent deep reinforcement learning
optimal power flow
Photovoltaic systems
PVs
Regulation
Reinforcement learning
Voltage control
voltage regulation
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Summary: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.
ISSN:1944-9933
DOI:10.1109/PESGM48719.2022.9917152