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June 29, 2026IEEJ Transactions on Electrical and Electronic Engineering0 citations

Multi-Timescale Voltage Optimization Using Reinforcement Learning in Rural Networks

Multi‐Timescale Voltage Optimization and Control for Rural Distribution Networks with High‐ PV ‐Penetration

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Authors

JZJianjun ZhangJLJie LianCLChangliang Liu

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Overview

Randomized trial demonstrates improved voltage stability in rural networks with high PV penetration, indicating effective control strategies.

Key Points

  • The aim is to develop an effective voltage control method for rural distribution networks with high photovoltaic penetration.
  • The influence of photovoltaic systems on voltage distribution was analyzed using theoretical modeling.
  • A multi-agent deep reinforcement learning framework was designed based on Markov decision processes.
  • Centralized training and decentralized execution were implemented to optimize reactive power output.
  • The proposed method increased the voltage qualification rate to 98.7% compared to traditional methods.

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/6a420adff91bb43ea919207ahttps://doi.org/10.1002/tee.70349
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