| 引用本文: | 王誉博,龚庆武,乔卉,刘栋,张劲弦.考虑电动汽车用户意愿的滚动优化调度[J].电力自动化设备,2022,42(10): |
| WANG Yubo,GONG Qingwu,QIAO Hui,LIU Dong,ZHANG Jinxian.Rolling optimal scheduling considering electric vehicle users’ willingness[J].Electric Power Automation Equipment,2022,42(10): |
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| 摘要: |
| 大规模电动汽车(EV)接入电网后，其无序充放电行为会给电力系统带来极坏的影响，因此对EV进行聚合分类并引导其有序充放电变得非常重要。然而，不同EV用户的需求具有明显的个性化、多样化，为了给不同EV用户提供精准的服务，需要对EV用户的意愿进行响应。首先，提出了行为倾向函数，其中包含了EV的剩余电量、未来出行距离、上一时段的充放电状态、停车时长等信息，以评价EV用户对主动参与调度的意愿；然后，根据EV用户的行为倾向对EV进行分类；最后，建立考虑配电网网损、风电与负荷的匹配度以及负荷方差的多目标优化函数对分类后的EV进行滚动调度，以确定最优调度策略。以改进的IEEE 33节点系统为算例进行仿真分析，仿真结果表明，所提优化调度方法准确响应了EV用户的多样化需求，既满足了EV的用车需求，又提高了EV充放电的整体效益，同时有效降低了配电网网损及负荷方差，提高了风电与负荷的匹配度。 |
| 关键词: 电动汽车 有序充放电 行为倾向 网损 匹配度 负荷方差 滚动调度 |
| DOI：10.16081/j.epae.202203028 |
| 分类号:U469.72;TM734 |
| 基金项目:国家重点研发计划项目(2020YFB0905905) |
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| Rolling optimal scheduling considering electric vehicle users’ willingness |
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WANG Yubo, GONG Qingwu, QIAO Hui, LIU Dong, ZHANG Jinxian
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School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, China
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| Abstract: |
| After the grid-connection of large-scale EVs(Electric Vehicles),their disordered charging and discharging behaviors will bring extremely bad effects on the power system. Therefore, it is very important to aggregate and classify EVs and guide their ordered charging and discharging. However, the demands of different EV users are obviously personalized and diversified. In order to provide accurate services to different EV users, it is necessary to respond to EV users’ willingness. Firstly, the behavioral tendency function is proposed, which contains the information of EVs’ remaining capacity, future travel distance, charging and discharging status in the previous period, stop time, and so on, so as to evaluate the willingness of EV users to actively participate in scheduling. Then, EVs are classified according to the behavioral tendencies of EV users. Finally, the multi-objective optimization function considering the distribution network loss, the matching degree between wind power and load as well as load variance is established to carry out rolling scheduling for classified EVs, so as to determine the optimal scheduling strategy. The improved IEEE 33-bus system is taken as the example for simulation analysis, and the simulative results show that the proposed method can accurately response to EV users’ diversified demands, not only meets the transport needs of EVs, but also improves the whole efficiency of EV charging and discharging, and effectively reduces the distribution network loss and load variance, and improves the matching degree between wind power and load. |
| Key words: electric vehicles ordered charging and discharging behavior tendency network loss matching degree load variance rolling scheduling |
