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

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

Research output: Contribution to conferencePaper

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.
Original languageAmerican English
Number of pages5
DOIs
StatePublished - 2023

Bibliographical note

See NREL/CP-5D00-84637 for preprint

NREL Publication Number

  • NREL/CP-5D00-88236

Keywords

  • deep reinforcement learning
  • distribution system
  • peak load management
  • voltage regulation

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