Reinforcement learning-assisted distributionally robust energy management for multi-microgrid networks

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Chinese explanation / 中文解读

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Original abstract

This paper proposes a hybrid reinforcement learning-assisted distributionally robust optimization (RL-DRO) framework for robust and economically efficient energy management in interconnected multi-microgrid systems under renewable, demand, and price uncertainty. The framework integrates deep reinforcement learning to generate adaptive scheduling policies with a Wasserstein-metric distributionally robust optimization formulation that enhances robustness against probability distribution shifts and non-stationary uncertainty. The upper level maximizes cumulative rewards of reinforcement learning agents representing individual microgrids, while the lower level optimizes power dispatch and energy exchange decisions subject to operational and network constraints. A five-microgrid test system equipped with photovoltaic generation, battery storage, and flexible loads is evaluated using 300 stochastic scenarios derived from historical data. Simulation results demonstrate that the proposed RL-DRO framework achieves a superior trade-off between cost efficiency and operational robustness when compared with deterministic, stochastic, and standalone reinforcement learning benchmarks. Specifically, the framework reduces expected operational cost by 14.8%, improves operational feasibility and service continuity as reflected by a proxy-based resilience indicator from 84.5% to 96.1%, and decreases the loss-of-load probability from 4.8% to 2.1%. Furthermore, the proposed approach maintains near-optimal performance as the Wasserstein ambiguity radius increases to 0.25, highlighting its robustness to distributional shifts and adverse uncertainty realizations. Rather than modeling explicit physical disturbances or fault-driven contingencies, the proposed framework focuses on sustaining feasible, adaptive, and cost-effective operation under severe uncertainty and stressed operating conditions. The hybrid learning-optimization paradigm thus unifies data-driven adaptability with theoretical robustness, providing a scalable and uncertainty-aware pathway for autonomous operation of future distribution networks.

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