基于时空规律的电动汽车负荷建模及其自动需求响应

目前关于电动汽车负荷预测的研究大都未针对实际区域进行预测研究。本文提出将数据驱动与模型驱动相结合进行电动汽车负荷预测,在此基础上,提出了一种自动需求响应策略。首先,通过对滴滴数据进行采集,挖掘获得该区域功能区划分、电动汽车出行时间、空间转移矩阵等再生信息,考虑多日一充,构建了负荷预测模型。其次,以平抑净负荷波动为目标,协同考虑供需两侧效益制定自动需求响应策略,结合价格型、激励型需求响应机制为用户带来的收益,引入logistic函数刻画了用户参与的不确定性。最后以海口某片区为例,仿真结果验证了预测模型能有效针对实际区域进行负荷预测,所提策略能实现供需两侧的双赢,就地消纳新能源,平抑负荷波动。

Electric Vehicle Load Modeling and Automatic Demand Response Based on Space-Time Law

At present,most of the research on electric vehicle load forecasting does not focus on the actual area.In this paper,data driven and model driven are combined to predict the load of electric vehicles.On this basis,an automatic demand response strategy is proposed.Firstly,through the data collection of Didi,the regeneration information such as functional area division,electric vehicle travel time and space transfer matrix are obtained.Considering multi-day charging,the load forecasting model is constructed.Second-ly,in order to stabilize the fluctuation of net load,considering the benefits of both sides of supply and demand,the automatic demand response strategy is formulated.Combined with the benefits of price type and incentive type demand response mechanism,the uncer-tainty of user participation is described by introducing logistic function.Finally,taking a certain area in Haikou as an example,the simulation results verify that the prediction model can effectively predict the load of the actual area,and the proposed strategy can a-chieve a win-win situation between supply and demand,consume absorb new energy locally,and stabilize the load fluctuation.