计及电动汽车有序充电的智能配电网协调优化控制方法

Coordinated Optimal Control Method for Smart Distribution Network Considering Orderly Charging of Electric Vehicles

随着高渗透率分布式电源与电动汽车的接入,智能配电网运行状态日趋复杂多变,呈现常态化的不确定性与波动性,对协调优化控制提出了更高的要求。针对分布式电源随机性出力制约其消纳与利用、电动汽车无序充电加剧尖峰负荷等问题,根据分布式电源出力与负荷预测结果,提出了一种计及电动汽车有序充电的智能配电网协调优化控制方法。该方法主要对响应分时电价的电动汽车的充电时间、充电顺序与充电位置进行动态优化,高效匹配分布式电源的随机性、波动性出力。综合考虑分布式电源消纳、负荷峰谷差与峰负荷、电动汽车用户的充电成本与充电满意度,构建了多目标协调优化控制模型,并采用粒子群-非支配排序遗传混合优化算法进行求解。算例分析结果表明,所提模型与方法能够有效减少EV用户充电成本、提升EV用户充电满意度、促进DG消纳、削峰填谷以及降低网损。

With the scale access of distributed generations and electric vehicles,the operating state of smart distribution networks is becoming more and more complex and changeable,presenting normalized uncertainty and volatility,and puts forward higher requirements for coordinated optimal control methods.Aiming at the problems of distributed generation output randomness restricting its consumption and utilization,and the disordered charging of electric vehicles exacerbates peak loads,a coordinated optimal control method for smart distribution network considering orderly charging of electric vehicles was proposed,based on distributed generation output and load forecasting results.The method mainly dynamically optimized the charging time,charging sequence and charging position of electric vehicles that responded to the time-of-use electricity price to efficiently match the randomness and volatility of distributed generation output.A multi-objective optimal control model was established,which considered the consumption of distributed generation,peak and valley difference of load and peak load,charging cost and satisfaction degree of electric vehicle users.The hybrid optimization algorithm of particle swarm and non-dominated sorting genetic algorithmⅡwas used to solve the model.Finally,the feasibility and effectiveness of the proposed model and method were verified by simulation results.