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A Novel Data-Driven Multi-Agent Reinforcement Learning Approach for Voltage Control Under Weak Grid Support.

Jiaxin Wu1, Ziqi Wang1, Ji Han2

  • 1State Grid Henan Electric Power Company, Zhengzhou 450052, China.

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Summary
This summary is machine-generated.

This study introduces a multi-agent deep reinforcement learning (MADRL) method for active voltage control in weak photovoltaic (PV) grids. The approach ensures voltage compliance and reduces network losses, outperforming traditional methods.

Keywords:
barrier functiondistributed controldistributed photovoltaicsdistribution networkmulti-agent deep reinforcement learning

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Area of Science:

  • Electrical Engineering
  • Artificial Intelligence
  • Power Systems

Background:

  • Weak voltage support in distribution networks with high PV integration poses challenges.
  • Active voltage control is crucial for grid stability and efficiency.

Purpose of the Study:

  • To develop a multi-agent deep reinforcement learning (MADRL)-based coordinated control for PV clusters.
  • To enhance voltage compliance and energy efficiency in weak grids.

Main Methods:

  • Formulated voltage control as a decentralized partially observable Markov decision process (Dec-POMDP).
  • Employed a centralized training with decentralized execution (CTDE) framework.
  • Designed barrier functions for reward shaping to balance voltage and efficiency.

Main Results:

  • The MADRL framework ensured voltage compliance and reduced network losses.
  • The MADDPG algorithm achieved a 91.9% Controllability Ratio (CR) with low power loss (0.0695 p.u.).
  • Demonstrated superior performance compared to optimal power flow (OPF) and droop control.

Conclusions:

  • The proposed MADRL approach effectively improves voltage stability and energy efficiency.
  • The method is robust under model-free and communication-constrained weak grid conditions.