| 引用本文: | 杨隆,张沛超,赵建立,向佳霓.基于出行大数据的电动汽车集群灵活性推演方法[J].电力自动化设备,2025,45(4):84-91 |
| YANG Long,ZHANG Peichao,ZHAO Jianli,XIANG Jiani.Flexibility inference method for electric vehicle cluster based on travel big data[J].Electric Power Automation Equipment,2025,45(4):84-91 |
|
| 摘要: |
| 出行大数据能够提供丰富的车辆行程信息，但如何基于出行大数据推演电动汽车(EV)集群的调节能力鲜有研究。提出基于出行调查数据和高斯混合模型的车辆时空链推演方法；提出模型驱动的EV能量链推演方法，将EV的时空变迁转换为能量变化，从而推演出EV充电行为；根据上述推演结果，构建表示EV集群灵活性的充电可行域，并提出基于优化的EV集群上、下调节能力估计方法。基于公开的出行调查数据库进行仿真，验证了由所提方法生成的EV出行结果更符合原始数据分布。同时，在考虑充电偏好、响应时长等因素下，推演了各时段EV集群的削峰、填谷能力。 |
| 关键词: 电动汽车 出行调查数据 时空链 能量链 高斯混合模型 可行域 充电灵活性 |
| DOI：10.16081/j.epae.202412036 |
| 分类号:U469.72 |
| 基金项目:国家重点研发计划项目(2021YFB2401200)；国网上海市电力公司科技项目(52090D230004) |
|
| Flexibility inference method for electric vehicle cluster based on travel big data |
|
YANG Long1, ZHANG Peichao2, ZHAO Jianli3, XIANG Jiani3
|
|
1.School of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China;2.School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;3.State Grid Shanghai Municipal Electric Power Company, Shanghai 200122, China
|
| Abstract: |
| The travel big data can provide rich vehicle travel information, but there are few studies on how to deduce the adjustment ability of electric vehicle(EV) clusters based on travel big data. A spatio-temporal chain inference method of vehicles based on travel survey data and Gaussian mixture model is proposed. A model-driven EV energy chain inference method is proposed to transform the spatio-temporal transitions of EVs into energy changes, thus predicting the charging behavior of EVs. Based on the above inferred results, the charging feasible region representing the flexibility of EV clusters is constructed, and an optimization-based estimation method of EV cluster’s up and down regulation capability is proposed. Based on a public travel survey database, the simulative results verify that the EV travel results generated by the proposed method are more consistent with the original data distribution. Additionally, considering the factors such as charging preference and response time, the peak shaving and valley filling capabilities of EV cluster in each period are deduced. |
| Key words: electric vehicles travel survey data spatio-temporal chain energy chain Gaussian mixture model feasible region charging flexibility |
