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DOE OSTI · 2229089

Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Load Management

Pei, Yansong·Yao, Yiyun (ORCID:000000033847815X)·Zhao, Junbo·Ding, Fei·Wang, Jiyu
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Abstract

The growing integration of distributed solar photovoltaic (PV) in distribution systems could result in adverse effects during grid operation. This paper develops a two-agent soft actor critic-based deep reinforcement learning (SAC-DRL) solution to simultaneously control PV inverters and battery energy storage systems for voltage regulation and peak demand reduction. The novel two-stage framework, featured with two different control agents, is applied for daytime and nighttime operations to enhance control performance. Comparison results with other control methods on a real feeder in Western Colorado demonstrate that the proposed method can provide advanced voltage regulation with modest active power curtailment and reduce peak load demand from feeder's head.

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deep reinforcement learningdistribution systempeak load managementPOWER TRANSMISSION AND DISTRIBUTIONvoltage regulation

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Pei, Yansong, Yao, Yiyun (ORCID:000000033847815X), Zhao, Junbo, Ding, Fei, Wang, Jiyu. 2023-09-25. Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Load Management. https://doi.org/10.1109/pesgm52003.2023.10252567

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