Data-Driven Distribution System Coordinated PV Inverter Control Using Deep Reinforcement Learning

Yansong Pei, Yiyun Yao, Junbo Zhao, Fei Ding, Ketian Ye

Research output: Contribution to conferencePaperpeer-review

2 Scopus Citations

Abstract

The deployment of distributed solar photovoltaic (PV) systems has increased consistently over the past decades. High penetrations of PVs could cause a series of adverse grid impacts, such as voltage violations. The recent development of smart inverter technologies rises the incentives of developing PV control solutions that regulate the inverter output power and seeking the optimization on system operational objectives. This paper proposes a data-driven control solution based on deep reinforcement learning (DRL) to optimize PV inverters for voltage regulation. The proposed solution can minimize PV real power curtailment while maintaining network voltage at an acceptable range. Comparison results between the proposed DRL control algorithms with deep deterministic policy gradient (DDPG) and volt-var control on a real feeder in west Colorado highlight the advantage of the proposed framework in controlling the system voltage while minimizing the PV real power curtailment.

Original languageAmerican English
Pages781-786
Number of pages6
DOIs
StatePublished - 2021
Event2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021 - Nanjing, China
Duration: 22 Dec 202124 Dec 2021

Conference

Conference2021 IEEE Sustainable Power and Energy Conference, iSPEC 2021
Country/TerritoryChina
CityNanjing
Period22/12/2124/12/21

Bibliographical note

Publisher Copyright:
© 2021 IEEE.

NREL Publication Number

  • NREL/CP-5D00-81188

Keywords

  • deep reinforcement learning
  • Distributed PV
  • distribution system
  • optimal power flow
  • voltage regulation

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