Voltage Control in Distributed Photovoltaic Power Distribution Networks Using Multi-Agent Deep Reinforcement Learning

Abstract

Abstract This study introduces a unique method employing the Multi-Agent Deep Deterministic Policy Gradient (MADDPG), a sophisticated deep reinforcement learning algorithm, for efficient voltage control in photovoltaic (PV) power distribution networks. The algorithm’s design emphasizes minimizing the need for communication and effectively managing delays, which are pivotal in ensuring consistent and reliable control in environments with fluctuating renewable energy sources. In simulations using the IEEE-33 power distribution system, the research demonstrates the algorithm’s ability to ensure stable and efficient network functioning, even under varying environmental and load conditions. This highlights its potential as a robust solution for modern and renewable energy-integrated power systems.