Open Access

ARTICLE

Research on Volt/Var Control of Distribution Networks Based on PPO Algorithm

Chao Zhu¹, Lei Wang¹, Dai Pan¹, Zifei Wang², Tao Wang², Licheng Wang^2,*, Chengjin Ye³
1 State Grid Zhejiang Economic and Technological Research Institute, Hangzhou, 310008, China
2 College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China
3 College of Electrical Engineering, Zhejiang University, Hangzhou, 310058, China
* Corresponding Author: Licheng Wang. Email:
(This article belongs to this Special Issue: Artificial Intelligence in Renewable Energy and Storage Systems)

Computer Modeling in Engineering & Sciences 2023, 134(1), 599-609. https://doi.org/10.32604/cmes.2022.021052

Received 24 December 2021; Accepted 16 February 2022; Issue published 24 August 2022

Abstract

In this paper, a model free volt/var control (VVC) algorithm is developed by using deep reinforcement learning (DRL). We transform the VVC problem of distribution networks into the network framework of PPO algorithm, in order to avoid directly solving a large-scale nonlinear optimization problem. We select photovoltaic inverters as agents to adjust system voltage in a distribution network, taking the reactive power output of inverters as action variables. An appropriate reward function is designed to guide the interaction between photovoltaic inverters and the distribution network environment. OPENDSS is used to output system node voltage and network loss. This method realizes the goal of optimal VVC in distribution network. The IEEE 13-bus three phase unbalanced distribution system is used to verify the effectiveness of the proposed algorithm. Simulation results demonstrate that the proposed method has excellent performance in voltage and reactive power regulation of a distribution network.

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Keywords

Deep reinforcement learning; voltage regulation; unbalance distribution systems; high photovoltaic permeability; photovoltaic inverter; volt/var control

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