【摘要】 电动汽车(electric vehicles, EV)的大规模接入,给综合能源系统调度带来了机遇和挑战。文章考虑电-热-气混合潮流和电动汽车的调度灵活性,建立了含电动汽车的综合能源系统两层嵌套调度模型,对电动汽车进行分群分层调度,合理制定每辆汽车的充放电策略。调度计划层以调度方案成本最小、能量波动最小和环保性最优为目标函数,采用改进的多目标粒子群优化(multi-objective particle swarm optimization, MOPSO)算法求解日前调度计划。EV调度层以用户满意度为目标,采用粒子群(particle swarm optimization, PSO)算法制定出各集群的充放电计划,集群内根据动态优先级制定每辆EV的充放电策略。算例分析表明,所建立的调度模型可有效求解含电动汽车的综合能源系统调度问题,且计算维度小、速度快,具有实用性。更多还原

【Abstract】 Large-scale electric vehicles(EV) accessing to the system brings opportunities and challenges to the scheduling of integrated energy system. With the power-heat-gas multi-energy flow and the scheduling flexibility of electric vehicles taken into consideration, this paper establishes a two-level scheduling model for integrated energy system with electric vehicles, performing grouped and hierarchical scheduling to make a rational charge-discharge strategy for each EV. Minimum scheduling plan cost, the smallest energy fluctuation and the best environmental protection are taken as objective functions in the scheduling plan level. Improved multi-objective particle swarm optimization(MOPSO) algorithm is adopted to solve the day-ahead scheduling plan. The EV scheduling level uses particle swarm optimization(PSO) algorithm to make the charge-discharge plan of each EV cluster with the goal of user satisfaction. According to the dynamic priority, the charge-discharge strategy of each EV in the cluster is solved. The analysis of case shows that the established scheduling model can effectively solve the scheduling problem of integrated energy system with electric vehicles, which has less calculation dimension, shorter simulation time and better practicality.更多还原