Abstract
An essential component of modern microgrid operation is the energy management system. This work proposes a stochastic programming–based proximal policy optimisation (PPO) technique for a typical wind–solar–diesel–storage microgrid, accounting for the significant uncertainty associated with wind energy. Using Latin Hypercube Sampling, scenario sets for wind power with different probabilities are generated by simulating potential errors in predicted wind power. A Markov decision process with high‐dimensional state and action spaces is used to model the energy management optimisation problem. In this manner, day‐ahead power scheduling for each generation/storage unit is optimised using PPO, which maximises economic gain while satisfying security constraints. The proposed method’s day‐ahead scheduling, evaluated on a real‐world microgrid, demonstrates the energy management system’s high efficiency and robustness. The results show that the proposed framework is competitive in solution quality and constraint satisfaction, and it achieves a positive balance among computational efficiency, robustness, operational feasibility and long‐term system reliability under renewable uncertainty.