Scheduling strategy of electric vehicle load in residential community considering preheating demands in winter

被引：0
作者：
Zhang X. [1 ]
Liu Z. [1 ]
Hou Y. [2 ]
Fan S. [1 ]
机构：
[1] School of Electrical Engineering, Shandong University, Jinan
[2] Shandong Provincial Key Laboratory of Biomass Gasification Technology, Energy Institute of Shandong Academy of Sciences, Qilu University of Technology(Shandong Academy of Sciences), Jinan
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2020年 / 40卷 / 11期
关键词：
Electric vehicles; Fuzzy adaptive particle swarm optimization algorithm; Peak load shifting; Preheating demands; Scheduling strategy; Travel temperature; V2G;
D O I：
10.16081/j.epae.202007024
中图分类号：
学科分类号：
摘要：
Aiming at the preheating demands of EVs(Electric Vehicles) in low temperature environment, a scheduling strategy of EV load considering preheating demands is proposed by studying the preheating technologies of various vehicles and combining the V2G(Vehicle-to-Grid) technology. Firstly, the temperature factor is introduced into the traditional EV load model to reflect the EV load demand at low temperature more accurately. Then, according to users' charging and preheating demands in winter, specific charging and discharging arrangements are made for EVs under different SOC(State Of Charge) in different time periods, and the scheduling model is solved by using the improved fuzzy adaptive particle swarm optimization algorithm. Taking the distribution network of a residential area in Beijing as an example, the simulation verifies that the proposed strategy can give full play to EVs' energy storage characteristics while meeting the electricity demand, and provide auxiliary function of peak load shifting for the power grid. Finally, the thermal model of EV battery packs is established to monitor the SOC and temperature change of specific EVs, and results show that the proposed strategy can effectively improve the travel temperature of vehicle-mounted battery packs while adjusting the peak and valley properties of power grid. © 2020, Electric Power Automation Equipment Press. All right reserved.
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页码：115 / 121and137
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