多层复杂出行链的电动汽车充换电负荷时空预测分布

严婧; 程静

doi:10.19912/j.0254-0096.tynxb.2025-0651

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (9) : 437-447. DOI: 10.19912/j.0254-0096.tynxb.2025-0651

多层复杂出行链的电动汽车充换电负荷时空预测分布

严婧¹, 程静²

作者信息 +

SPATIOTEMPORAL PREDICTION AND DISTRIBUTION OF ELECTRIC VEHICLE CHARGING AND BATTERY-SWAPPING LOADS UNDER MULTI-LAYER COMPLEX TRAVEL CHAIN FRAMEWORK

Yan Jing¹, Cheng Jing²

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文章历史 +

摘要

针对电动汽车并入电网的无序充电行为影响电网稳定性的问题,在考虑新能源消纳条件下,提出一种多层复杂出行链的电动汽车充换电负荷时空预测模型,旨在揭示“充-换”交互模式下换电负荷对充电负荷波动的动态补偿机制。首先,在多层复杂出行链模型下确定电动汽车的出行特征,利用弗洛伊德算法寻找出行的最优路径,结合“流量-车速”关系确定单位里程耗电量;其次,分别构建充、换电的充电模型并在“充-换”交互模式下确定电动汽车选择的充电方式;再次,建立风光发电的数学模型;最后,对所充电负荷及新能源发电进行数据处理形成时空分布图,对比不同模式下的负荷数据。实验结果表明,电动汽车在行驶过程中产生的充电需求有明显的区域差异性,且在新能源消纳下换电负荷能够有效减缓充电负荷产生的波动性。

Abstract

Aiming at the problem that the disorderly charging behavior of electric vehicles （EVs） connected to the grid affects the stability of the grid, a spatio-temporal prediction model of EV charging and swapping loads based on multi-layer complex travel chains is proposed under the condition of considering the accommodation of new energy. The model aims to reveal the dynamic compensation mechanism of swapping loads for the fluctuations of charging loads under the “charging-swapping” interaction mode. Firstly, the travel characteristics of EVs are determined under the multi-layer complex travel chain model, and the Floyd algorithm is used to find the optimal travel path. The unit mileage power consumption is determined by combining the “flow-speed” relationship. Secondly, the charging models of charging and swapping are respectively constructed, and the charging mode selected by EVs is determined under the “charging-swapping” interaction mode. Thirdly, a mathematical model of wind and solar power generation is established. Finally, the spatio-temporal distribution maps of the charging loads and new energy generation are formed through data processing, and the load data under different modes are compared. The experimental results show that the charging demand generated by EVs during driving has obvious regional differences, and the swapping load can effectively mitigate the fluctuations of charging loads under the accommodation of new energy.

导出引用

严婧, 程静. 多层复杂出行链的电动汽车充换电负荷时空预测分布[J]. 太阳能学报. 2026, 47(9): 437-447 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0651

Yan Jing, Cheng Jing. SPATIOTEMPORAL PREDICTION AND DISTRIBUTION OF ELECTRIC VEHICLE CHARGING AND BATTERY-SWAPPING LOADS UNDER MULTI-LAYER COMPLEX TRAVEL CHAIN FRAMEWORK[J]. Acta Energiae Solaris Sinica. 2026, 47(9): 437-447 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0651

中图分类号： TM73

参考文献

[1] Liu J Z, Wang Z P, Zhang L.Integrated vehicle-following control for four-wheel-independent-drive electric vehicles against non-ideal V2X communication[J]. IEEE Transactions on Vehicular Technology, 2022, 71(4): 3648-3659.
[2] 刘宏伟, 胡永仕, 苏晓丽. 基于演化博弈的新能源汽车换电模式研究[J]. 武汉理工大学学报(交通科学与工程版), 2025, 49(5): 987-994.
Liu H W, Hu Y S, Su X L.Research on new energy vehicle battery-swapping mode based on evolutionary game[J]. Journal of Wuhan University of Technology (transportation science & engineering), 2025, 49(5): 987-994.
[3] 李练兵, 郭兴辰, 曾四鸣, 等. 基于GLDSC-ConvAutoformer模型的区域电动汽车短期充电负荷预测[J]. 太阳能学报, 2025, 46(2): 90-98.
Li L B, Guo X C, Zeng S M, et al.Short-term charging load prediction of regional electric vehicles based on GLDSC-ConvAutoformer model[J]. Acta Energiae Solaris Sinica, 2025, 46(2): 90-98.
[4] 马苗苗, 任智伟, 刘立成, 等. 考虑新能源消纳的电动汽车有序充电控制策略[J]. 太阳能学报, 2024, 45(8): 94-103.
Ma M M, Ren Z W, Liu L C, et al.Orderly charging control strategy for electric vehicles considering new energy accommodation[J]. Acta Energiae Solaris Sinica, 2024, 45(8): 94-103.
[5] Unterluggauer T, Rauma K, Järventausta P, et al.Short-term load forecasting at electric vehicle charging sites using a multivariate multi-step long short-term memory: a case study from Finland[J]. IET Electrical Systems in Transportation, 2021, 11(4): 405-419.
[6] Zhang X, Chan K W, Li H R, et al.Deep-learning-based probabilistic forecasting of electric vehicle charging load with a novel queuing model[J]. IEEE Transactions on Cybernetics, 2021, 51(6): 3157-3170.
[7] 张夏韦, 梁军, 王要强, 等. 电动汽车充电负荷时空分布预测研究综述[J]. 电力建设, 2023, 44(12): 161-173.
Zhang X W, Liang J, Wang Y Q, et al.Overview of research on spatiotemporal distribution prediction of electric vehicle charging[J]. Electric Power Construction, 2023, 44(12): 161-173.
[8] 任明远, 姜明军, 宋玉峰, 等. 基于蒙特卡洛法的城市电动汽车充电负荷预测[J]. 电气应用, 2023, 42(4): 18-23.
Ren M Y, Jiang M J, Song Y F, et al.Research on charging load forecasting of urban electric vehicle based on Monte Carlo method[J]. Electrotechnical Application, 2023, 42(4): 18-23.
[9] 张艳娟, 苏小林, 闫晓霞, 等. 基于电动汽车时空特性的充电负荷预测[J]. 电力建设, 2015, 36(7): 75-82.
Zhang Y J, Su X L, Yan X X, et al.A method of charging load forecast based on electric vehicle time-space characteristics[J]. Electric Power Construction, 2015, 36(7): 75-82.
[10] 欧阳静, 杨吕, 尹康, 等. 基于ALIF-LSTM多任务学习的综合能源系统短期负荷预测[J]. 太阳能学报, 2022, 43(9): 499-507.
Ouyang J, Yang L, Yin K, et al.Short-term load forecasting method for integrated energy system based on ALIF-LSTM and multi-task learning[J]. Acta Energiae Solaris Sinica, 2022, 43(9): 499-507.
[11] 曾成碧, 刘广, 苗虹, 等. 考虑城市交通流的电动汽车充电站规划[J]. 电力科学与技术学报, 2019, 34(4): 101-107.
Zeng C B, Liu G, Miao H, et al.Planning of electric vehicle charging station considering urban traffic flows[J]. Journal of Electric Power Science and Technology, 2019, 34(4): 101-107.
[12] 余洋, 陆文韬, 刘霡, 等. 考虑复杂出行链基于双链马尔科夫的电动汽车负荷建模方法[J]. 电网与清洁能源, 2024, 40(1): 109-118.
Yu Y, Lu W T, Liu M, et al.A load modeling method for electric vehicles based on double chain Markov considering complex travel chains[J]. Power System and Clean Energy, 2024, 40(1): 109-118.
[13] 张延宇, 张智铭, 刘春阳, 等. 基于动态自适应图神经网络的电动汽车充电负荷预测[J]. 电力系统自动化, 2024, 48(7): 86-93.
Zhang Y Y, Zhang Z M, Liu C Y, et al.Electric vehicle charging load prediction based on dynamic adaptive graph neural network[J]. Automation of Electric Power Systems, 2024, 48(7): 86-93.
[14] 刘志强, 张谦, 朱熠, 等. 计及车-路-站-网融合的电动汽车充电负荷时空分布预测[J]. 电力系统自动化, 2022, 46(12): 36-45.
Liu Z Q, Zhang Q, Zhu Y, et al.Spatial-temporal distribution prediction of charging loads for electric vehicles considering vehicle-road-station-grid integration[J]. Automation of Electric Power Systems, 2022, 46(12): 36-45.
[15] 丁乐言, 柯松, 张帆, 等. 考虑出行需求和引导策略的电动汽车充电负荷预测[J]. 电力建设, 2024, 45(6): 10-26.
Ding L Y, Ke S, Zhang F, et al.Forecasting of electric-vehicle charging load considering travel demand and guidance strategy[J]. Electric Power Construction, 2024, 45(6): 10-26.
[16] 刘丽军, 陈昌, 胡鑫, 等. 基于“车-路-站-网” 信息耦合的电动汽车有序充电策略[J]. 高电压技术, 2024, 50(2): 709-721.
Liu L J, Chen C, Hu X, et al.Ordered charging strategy for electric vehicles based on the information coupling of vehicle-road-station-grid[J]. High Voltage Engineering, 2024, 50(2): 709-721.
[17] 朱峻良, 武志刚, 刘嘉宁. 基于半动态交通均衡的电动汽车充电负荷概率分布建模[J]. 电网技术, 2024, 48(2): 640-649.
Zhu J L, Wu Z G, Liu J N.Electric vehicle charging load probability distribution modeling based on semi-dynamic traffic equilibrium[J]. Power System Technology, 2024, 48(2): 640-649.
[18] 胡号, 胡志坚, 李天格, 等. 基于“车-桩-站”交互的充换电站一体化实时调度策略[J]. 中国电机工程学报, 2025, 45(11): 4175-4186.
Hu H, Hu Z J, Li T G, et al.Integrated real-time dispatching strategy of charging and swapping stations based on “vehicle-pile-station” interaction[J]. Proceedings of the CSEE, 2025, 45(11): 4175-4186.
[19] 曹剑东, 张敖木翰, 李兵. 基于拓扑分层的路网简化模型及其应用[J]. 兵工自动化, 2015, 34(3): 41-44.
Cao J D, Zhang A, Li B.Simplified model of route network based on hierarchical topology and its application[J]. Ordnance Industry Automation, 2015, 34(3): 41-44.
[20] 张文昕, 栗然, 臧向迪, 等. 基于强化学习的电动汽车换电站实时调度策略优化[J]. 电力自动化设备, 2022, 42(10): 134-141.
Zhang W X, Li R, Zang X D, et al.Real-time scheduling strategy optimization for electric vehicle battery swapping station based on reinforcement learning[J]. Electric Power Automation Equipment, 2022, 42(10): 134-141.
[21] 刘一欣, 郭力, 王成山. 微电网两阶段鲁棒优化经济调度方法[J]. 中国电机工程学报, 2018, 38(14): 4013-4022.
Liu Y X, Guo L, Wang C S.Economic dispatch of microgrid based on two stage robust optimization[J]. Proceedings of the CSEE, 2018, 38(14): 4013-4022.
[22] 崔杨, 邓贵波, 曾鹏, 等. 计及碳捕集电厂低碳特性的含风电电力系统源-荷多时间尺度调度方法[J]. 中国电机工程学报, 2022, 42(16): 5869-5886.
Cui Y, Deng G B, Zeng P, et al.Multi-time scale source-load dispatch method of power system with wind power considering low-carbon characteristics of carbon capture power plant[J]. Proceedings of the CSEE, 2022, 42(16): 5869-5886.