基于强度Pareto平衡优化器的共享储能系统实时调度优化OA

The volatility,randomness,and intermittency of renewable energy sources,along with the variability of loads,exacerbate the uncertainties on both the supply and demand sides.This poses significant challenges to the in-tegration of renewable energy into the power grid and real-time scheduling.In this study,flexibility indicators and a multi-objective optimization algorithm are employed to implement the real-time scheduling strategy.Shared energy storage microgrid clusters and electric vehicles are regarded as flexible resources,and new flexibility indicators are de-fined to ensure the flexibility of the system during the day-ahead scheduling stage and enhance the system's ability to cope with uncertainties on both the supply and demand sides.The strength Pareto evolutionary optimization algorithm is proposed to solve the power system scheduling scheme with the objectives of minimizing costs and maximizing flexibil-ity,avoiding the results from falling into local optima.The results show that,on the premise of ensuring costs and flexi-bility,the day-ahead scheduling strategy developed by the proposed method enables the utilization rate of renewable energy and the energy self-sufficiency rate to reach 94.22％and 92.87％,respectively,verifying the reliability of the proposed method.