Research on Multi-time Scale Optimization Scheduling Strategy of Microgrid Based on Improved Generative Adversarial Network for Wind and PV Power Scenario Generation

Aiming to address the impact of renewable energy output uncertainty on optimal scheduling of microgrid, a multi-time scale optimal scheduling strategy for wind and PV power scenario generation in microgrid based on C-WGAN (conditional Wasserstein generative adversarial networks) is proposed. Firstly, the specified wind and PV power scenarios are generated through C-WGAN. Then, during the day-ahead optimization scheduling stage, a comprehensive consideration is given to the cost of the connecting line and the battery degradation, while introducing an optimized coefficient to optimize the interactive power of the connecting line. The primary objective during the intraday optimization scheduling phase is to track the results of day-ahead optimization scheduling, so as to effectively mitigate the impact of inaccuracies in day-ahead renewable energy and load prediction data. The results indicate that the proposed method not only exhibits strong robustness, butalso effectively guarantees the economic operation of microgrid.