Optimization of Hybrid Renewable Microgrids: A Source-Load Uncertainty-Aware Framework for Wind-Photovoltaic-Storage Systems

A two-stage robust optimization model for capacity configuration optimization operation was established to address the uncertainty of renewable energy and load in microgrids, and the optimal capacity and operation strategy under uncertain conditions were obtained. The model considers the operational constraints and coordinated control of energy storage, demand side loads, and controllable distributed power sources, and introduces uncertainty adjustment parameters to configure and optimize microgrid systems from multiple perspectives. Based on the Benders algorithm, the original problem is decomposed into a main problem and sub problems with mixed integer linear characteristics for solving, in order to obtain the optimal solution of the original problem. Finally, the effectiveness of the established model and solving algorithm was verified through simulation analysis. After comparative verification, robust optimization was compared with deterministic optimization, resulting in a 33% reduction in power grid operating costs.