Energies, Vol. 19, Pages 978: Multi-Agent Proximal Policy Optimization for Coordinated Adaptive Control of Photovoltaic Inverter Clusters in Active Distribution Networks

Energies doi: 10.3390/en19040978

Authors:
Gongrun Wang
Shumin Sun
Yan Cheng
Peng Yu
Shibo Wang
Xueshen Zhao

High penetration of distributed photovoltaic (PV) generation has transformed active distribution networks into inverter-dominated systems, where maintaining voltage stability, minimizing power losses, and maximizing renewable utilization under uncertainty remain significant challenges. Conventional centralized optimal power flow (OPF) and ADMM-based distributed optimization methods suffer from scalability limitations, high computational latency, and reliance on accurate system models, while single-agent reinforcement learning approaches such as PPO struggle with non-stationarity and lack of coordination in multi-inverter settings. To address these limitations, this paper proposes a coordinated control framework based on Multi-Agent Proximal Policy Optimization (MAPPO) for photovoltaic inverter clusters. By adopting centralized training with decentralized execution, the proposed approach enables effective coordination among heterogeneous inverter agents while preserving real-time autonomy. The framework explicitly incorporates network-level objectives, inverter operational constraints, and stochastic irradiance and load uncertainties, allowing agents to learn adaptive and robust control strategies. Simulation studies on a modified IEEE 33-bus active distribution network demonstrate that the proposed MAPPO-based method reduces voltage deviations by more than 40%, decreases network losses by approximately 25%, and lowers photovoltaic curtailment ratios by nearly 50% compared with centralized optimization approaches. In addition, MAPPO achieves significantly faster and more stable convergence than independent PPO under highly variable operating conditions.b These results indicate that MAPPO provides a scalable and resilient alternative to conventional optimization and single-agent learning methods, offering a practical pathway to enhance hosting capacity, operational robustness, and renewable integration in future active distribution networks.

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