Multi‐agent deep reinforcement learning‐enabled voltage regulation approach for partitioned active distribution network using heterogeneous PV inverters Article Swipe

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Kang Xiong , Chenxing Ye , Bin Liu , Xun Suo ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1049/enc2.70026

To improve the adaptability of voltage regulation in active distribution networks (ADNs) with high photovoltaic (PV) penetration, this paper proposes a distributed Volt/Var control (VVC) strategy enabled by multi‐agent deep reinforcement learning and implemented through heterogeneous PV inverters. First, a distributed VVC framework is established by partitioning the ADN into multiple sub‐networks, each modelled as an agent, with the goal of minimizing voltage deviation. This control framework considers the heterogeneous operation modes of PV systems and utilizes both reactive power support and active power curtailment to maintain voltage within acceptable limits. Then, the VVC problem is formulated as a Markov game and solved using a multi‐agent soft actor–critic algorithm. Simulation studies conducted on the IEEE 33‐bus and 118‐bus test systems validated the effectiveness of the proposed method, demonstrating its superior performance in reducing voltage fluctuations compared to benchmark approaches.

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Type: article
Language: en
Landing Page: https://doi.org/10.1049/enc2.70026
OA Status: gold
References: 27
OpenAlex ID: https://openalex.org/W4417288782

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DOI: https://doi.org/10.1049/enc2.70026

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Title: Multi‐agent deep reinforcement learning‐enabled voltage regulation approach for partitioned active distribution network using heterogeneous PV inverters

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Type: article

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Language: en

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Publication year: 2025

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Publication date: 2025-12-12

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Authors: Kang Xiong, Chenxing Ye, Bin Liu, Xun Suo

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Landing page: https://doi.org/10.1049/enc2.70026

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OA URL: https://doi.org/10.1049/enc2.70026

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Cited by: 0

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References (count): 27

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