Research on the Optimization of Wind-Solar Energy Storage Systems in Park Microgrids Based on Mixed Integer Linear Programming and Monte Carlo Simulation

This paper delves into the optimization and economic benefits of wind-solar energy storage systems in park microgrids. By constructing and refining multiple mathematical models, the study provides scientific decision support for system configuration, aiming to meet the increasing demand for load and enhance overall economic benefits. Firstly, the paper proposes the Photovoltaic and Energy Storage Coordination Optimization Model (PCSO-Model), which combines Mixed Integer Linear Programming (MILP) with Monte Carlo simulation, effectively reducing the total supply cost of the park. Subsequently, considering the aging effect of energy storage systems, load forecasting errors at different time scales, and the impact of electricity price fluctuations on economic viability, the PCSO model is improved and solved using an improved genetic algorithm. Simulation and optimization results demonstrate that the introduction of energy storage systems reduces costs by approximately 10^% on average, decreases the curtailment of wind and solar power by about 15%, and increases the utilization rate of wind and solar power generation by 20%.