针对电动汽车(electric vehicle,EV)入网时长和荷电状态(state of charge,SOC)的不确定性,提出基于支持向量聚类(support vector clustering,SVC)的电动汽车集群并网鲁棒优化调度模型.以EV的充放电功率作为决策变量,用户最小充电成本为目标函数,建立集群EV调度模型.利用EV历史充电数据,以包含所有样本数据的最小超球体作为不确定集形状,将广义直方图交叉核作为核函数,计算EV入网时间和充电时长参数的不确定集,建立基于SVC的集群EV鲁棒优化调度模型.算例分析结果表明,所提方法能更准确地描述EV充电的不确定性参数,所提模型在保证经济性的同时能迅速响应分时电价,具有较好的实用性.
Robust Optimal Scheduling Model of Grid-connected Electric Vehicle Clusters Based on SVC
Aiming at the uncertainty of electric vehicle(EV)grid connection time and charging time,we proposed a robust optimal scheduling model for grid-connected electric vehicle cluster based on support vector clustering(SVC).Taking the charging and discharging power of EV as the decision variable and the minimum charging cost of the user as the objective function,we established a cluster EV scheduling model.Using the EV historical charging data,and taking the smallest hy-persphere containing all sample data as the shape of the uncertain set,and the generalized histogram cross-kernel as the kernel function,we calculated the uncertain sets of EV network access time and charging time parameters,and established.A rod optimization scheduling model of cluster EV based on SVC.The analysis of the final example shows that the method proposed in this paper can describe the uncertainty parameters of EV charging more accurately,and the model can quickly respond to the time-of-use electricity price while ensuring economy,and has good practicability.
electric vehicleuncertain setsnetwork access durationstate of chargesupport vector clusteringrobust optimization
万方数据
维普
