基于IFCM算法的电动汽车群聚合调峰方法

Peak-regulating Method of Aggregating Electric Vehicle Groups Based on IFCM Algorithm

通过规模迅速扩大的电动汽车缓解电网调峰压力成为一种可行措施,但电动汽车的无序充放电会带来反调峰现象和决策变量爆炸问题。针对上述问题,提出一种在不同出行链基础上、采用IFCM算法进行的聚类-调峰-任务分配模型,以提高调度模型精确度,解决大比例电动汽车并网参与调峰优化的问题。首先,采用改进模糊C均值(improved fuzzy C-means,IFCM)聚类算法对不同出行链下的电动汽车群时空特性进行聚类分析,减少决策变量数目;其次,以调控成本最小为目标,将聚类结果应用于电力系统的调峰,得到各个集群的充放电任务;最后建立个体任务分配模型,得到调度任务下最优的单个电动汽车充放电策略。算例分析表明,该模型使用IFCM算法提高聚类性能,有效降低了调峰的决策变量维数,在满足调峰需求的前提下保证了电网、用户的经济性。

The bidirectional regulation ability of EVs can be planned to relieve the peak-valley pressure.However,the disordered charging and discharging of EVs may bring problems such as reverse peak-regulation and variables explosion of decision-making.To solve these problems,we proposed a clustering-peak-task decomposition model based on different travel chains by using the IFCM algorithm,and solved the problem that a large proportion of electric vehicles participate in peak regulation optimization under the premise of improving the accuracy of the scheduling model.Firstly,the IFCM clustering algorithm was adopted to aggregate EVs.Secondly,to minimize the control cost,the clustering results were ap-plied to the power system peak regulation,and the charging and discharging tasks of each cluster were obtained.Finally,an individual task assignment model was established to obtain the optimal charging and discharging strategy for a single EV.Example analysis shows that,by using IFCM algorithm to improve clustering performance,the model effectively re-duces the dimension of decision-making variables for peak regulation,and ensures the economy of users on the premise of meeting the demands of peak regulation.