【摘要】 针对电动汽车无序充电对配电网的负面影响,该文设计了基于K-means聚类的有序充放电多目标调度模型。首先,以私家车为研究对象进行充电负荷的不确定性建模;其次,根据电动汽车充电桩的空间分布实现有效聚类,形成等效节点以及所对应的代理商;构建以减小峰谷差和代理商调度偏差为目标的第一阶段模型,第二阶段模型以用户充电成本最小为目标,每辆电动汽车的充电需求为决策量;然后将2个目标函数通过单一化处理达到综合最优;最后,在M ATLAB平台上采用粒子群优化算法进行求解,算例仿真表明该文提出的调度优化模型在削峰填谷与提高用户经济性方面效果突出。更多还原

【Abstract】 Aiming at the serious impact of the uncoordinated charging of electric vehicles on the distribution netw ork,this paper designs a multi-objective scheduling model for coordinated charging and discharging based on K-means clustering.Firstly,w e take private cars as research objects for the uncertainty modeling of charging load. Secondly,according to the spatial distribution of the electric vehicle charging pile,the effective clustering is achieved,and the equivalent node and the corresponding agent are formed. The first stage model is constructed to minimize the deviation betw een the peak-valley difference and the scheduling of agents. At the same time,the second stage model takes the minimum user charging and discharging cost as objective and each electric vehicle charging pow er as decision content. Then,tw o objective functions achieve comprehensive optimal through simplified handling. Finally,w e adopt particle sw arm optimization algorithm on the M ATLAB platform to solve the model. The example simulation results show that the proposed scheduling optimization model has remarkable effect in peak cutting and improving user economy.更多还原