Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management. - Yuanzheng Li; Shangyang He; Yang Li 011; Yang Shi 001; Zhigang Zeng (CoRR, 2023)

Probabilistic Charging Power Forecast of EVCS: Reinforcement Learning Assisted Deep Learning Approach. - Yuanzheng Li; Shangyang He; Yang Li 011; Leijiao Ge; Suhua Lou; Zhigang Zeng (IEEE Trans. Intell. Veh., 2023)

Renewable Energy Absorption Oriented Many-Objective Probabilistic Optimal Power Flow. - Yuanzheng Li; Shangyang He; Yang Li 011; Qiang Ding; Zhigang Zeng (IEEE Trans. Netw. Sci. Eng., 2024)

Federated Multiagent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multimicrogrid Energy Management. - Yuanzheng Li; Shangyang He; Yang Li 011; Yang Shi 001; Zhigang Zeng (IEEE Trans. Neural Networks Learn. Syst., 2024)

Graph Learning for Power Flow Analysis: A Global-Receptive Graph Iteration Network Method. - Junyan Huang; Yuanzheng Li; Shangyang He; Guokai Hao; Chunjie Zhou; Zhigang Zeng (IEEE Trans. Netw. Sci. Eng., 2025)

Boosting Communication Efficiency in Federated Learning for Multiagent-Based Multimicrogrid Energy Management. - Shangyang He; Yuanzheng Li; Yang Li 011; Yang Shi 001; C. Y. Chung 001; Zhigang Zeng (IEEE Trans. Neural Networks Learn. Syst., 2025)

Two-Timescale Online Optimization of Behind-the-Meter Battery Storage for Stacked Revenue by Providing Multi-Services. - Shibo Chen 002; Suhan Zhang; Shangyang He; Haosen Yang 001 (IEEE Trans. Smart Grid, 2025)