In this paper

a source-grid-load-storage (SGLS) cooperative scheduling strategy based on a double-layer optimization model is proposed with purpose of minimizing the electricity cost for loads in parks while extending the life time of energy storage battery. The upper-payer optimization model schedules energy supply through time-of-use electricity pricing

coordinates the energy flow among wind power

photovoltaic generation

energy storage batteries and the grid. The lower-layer optimization model is based on a genetic algorithm

schedules the charging and discharging behavior of each energy storage battery to minimize life degradation of battery. The experimental results show that the proposed double-layer optimization model exhibits superior performance in both electricity purchasing cost and battery lifespan management. The model

compared to traditional single-objective optimization methods or rule-based single-layer scheduling strategies

can effectively enhance system economic efficiency and system stability. This research has provided an efficient scheduling framework for the application of energy management systems in practical parks

offering significant theoretical and practical value.