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

Pei, Yansong; Yao, Yiyun; Zhao, Junbo; Ding, Fei; Wang, Jiyu

doi:10.1109/PESGM52003.2023.10252567

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

Conference · 25 September 2023

DOI:https://doi.org/10.1109/PESGM52003.2023.10252567· OSTI ID:2229089

Pei, Yansong; ; Zhao, Junbo; Ding, Fei; Wang, Jiyu

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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Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 2229089

Report Number(s):: NREL/CP-5D00-88236; MainId:89011; UUID:feb645b9-59af-4332-bf8d-f0d034170e2b; MainAdminID:71246

Resource Relation:: Conference: Presented at the the 2023 IEEE Power & Energy Society General Meeting (PESGM), 16-20 July 2023, Orlando, Florida; Related Information: 84637

Country of Publication:: United States

Language:: English

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