Optimal scheduling strategy based on genetic algorithm for grid-side energy storage system to participate in regional power trading

With the increasing global energy demand and environmental problems, energy storage technology is gradually playing an important role in modern power systems.In this study, an optimal scheduling strategy based on genetic algorithm for grid-side energy storage system to participate in regional power trading is proposed.The strategy optimizes the charging and discharging decisions of the energy storage system by genetic algorithm to maximize the revenue.First, an optimization model of the energy storage system is constructed, including power trading, charging and discharging constraints of the energy storage device and the prediction of market electricity price.Then, the multi-objective optimization problem is solved by genetic algorithm to optimize the charging and discharging decisions, and applied to the actual scheduling of grid-side energy storage system.The experimental results show that the energy storage system is able to realize high returns under specific electricity price and load demand conditions.In the specific arithmetic analysis, the improved NSGA-II algorithm is used to optimize the scheduling of the IEEE 33-node power system.The optimized grid-side energy storage system achieves better returns in several time periods, including renewable energy consumption and peak-valley arbitrage in the 14:00-22:00 time period.Under different tariff fluctuation conditions, the energy storage system is able to efficiently dispatch resources, reduce operating costs and enhance market competitiveness.Through further optimization, the scheduling performance of the system is significantly improved and has strong application prospects.