Research on the Coordinated Optimization Configuration of Wind-Solar-Energy Storage Based on Goal Programming and Genetic Algorithm

This paper presents a comprehensive study on the coordinated optimization configuration of wind-solar-energy storage systems, leveraging goal programming and genetic algorithms to enhance the economic and operational efficiency of microgrids in parks A, B, and C. The research initially involves the collection and preprocessing of typical daily load and wind-photovoltaic power generation data, addressing missing and abnormal values to ensure data integrity. An economic analysis is conducted assuming an initial electricity installation of zero, with subsequent calculations of electricity purchase volumes, curtailed power, and total supply costs for each park, revealing distinct operational and economic profiles. The goal programming model is established to minimize the total electricity purchase cost, incorporating constraints and operational lifespan considerations of the energy storage system. We define the cost function in terms of the power rating, energy capacity, and lifespan of the storage system. The genetic algorithm model is then introduced, employing fundamental genetic operators—selection, crossover, and mutation—to explore the solution space for optimal energy storage configurations, transcending the limitations of a 50 kW/100 kWh plan.