| 引用本文: | 张新城,刘志珍,侯延进,范书静.计及冬季预热需求的居民区电动汽车负荷调度策略[J].电力自动化设备,2020,40(11): |
| ZHANG Xincheng,LIU Zhizhen,HOU Yanjin,FAN Shujing.Scheduling strategy of electric vehicle load in residential community considering preheating demands in winter[J].Electric Power Automation Equipment,2020,40(11): |
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| 摘要: |
| 针对电动汽车(EV)在低温环境下的预热需求，通过研究各种车辆的预热技术，结合电动汽车入网(V2G)技术，提出一种计及车辆预热需求的EV负荷调度策略。首先，将温度因素引入传统的EV负荷模型，使之更能准确反映在低温环境下的EV负荷需求；然后，结合用户在冬季的车辆充电需求和预热需求，对不同荷电状态下的车辆在不同时段做针对性的充放电安排，并利用改进后的模糊自适应粒子群优化算法对调度模型进行求解。以北京市某小区配电网为对象进行算例设计，通过仿真验证了所提策略在满足车辆用电需求的同时，可以充分发挥其储能特性，为电网提供“削峰填谷”的辅助功能。最后，通过建立EV电池组的热模型以监测具体车辆的荷电状态和温度变化，结果表明所提策略在调节电网峰谷属性的同时，有效地改善了车载电池组的出行温度。 |
| 关键词: 电动汽车 预热需求 V2G 模糊自适应粒子群优化算法 削峰填谷 出行温度 调度策略 |
| DOI：10.16081/j.epae.202007024 |
| 分类号:U469.72 |
| 基金项目:山东省重点研发计划项目(2019GGX104080) |
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| Scheduling strategy of electric vehicle load in residential community considering preheating demands in winter |
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ZHANG Xincheng1, LIU Zhizhen1, HOU Yanjin2, FAN Shujing1
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1.School of Electrical Engineering, Shandong University, Jinan 250061, China;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 250014, China
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| Abstract: |
| 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. |
| Key words: electric vehicles preheating demands V2G fuzzy adaptive particle swarm optimization algorithm peak load shifting travel temperature scheduling strategy |
