Real-time Decomposition Strategy of Aggregate Power for Large-scale Electric Vehicle Charging and Battery Swapping Facilities

CHEN Zhuoxu; HU Zechun; BAO Zhiyuan; LIU Xiaonan; ZHANG Junwei; XIE Yan

doi:10.7500/AEPS20250319001

您当前的位置：

首页 >

文章列表页 >

Real-time Decomposition Strategy of Aggregate Power for Large-scale Electric Vehicle Charging and Battery Swapping Facilities

Key Technologies and Practical Applications of Vehicle\-to\-Grid in New Power System | 更新时间：2026-08-21

- Real-time Decomposition Strategy of Aggregate Power for Large-scale Electric Vehicle Charging and Battery Swapping Facilities
- Automation of Electric Power Systems Vol. 50, Issue 15, Pages: 158-168(2026)
- 作者机构：
  
  1.清华大学电机工程与应用电子技术系，北京市 100084
  2.国网天津市电力公司，天津市 300010
- 作者简介：
- 基金信息：
  
  the National Key R&D Program of China(2022YFB2403900)
- DOI：10.7500/AEPS20250319001
  CLC：
- Received：19 March 2025，
  
  Revised：2025-06-09，
  
  Online First：11 October 2025，
  
  Published：10 August 2026
- 稿件说明：
移动端阅览
陈卓旭,胡泽春,鲍志远等.大规模电动汽车充换电设施集总功率实时分解策略[J].电力系统自动化,2026,50(15):158-168.

CHEN Zhuoxu,HU Zechun,BAO Zhiyuan,et al.Real-time Decomposition Strategy of Aggregate Power for Large-scale Electric Vehicle Charging and Battery Swapping Facilities[J].Automation of Electric Power Systems,2026,50(15):158-168.
陈卓旭,胡泽春,鲍志远等.大规模电动汽车充换电设施集总功率实时分解策略[J].电力系统自动化,2026,50(15):158-168. DOI： 10.7500/AEPS20250319001.

CHEN Zhuoxu,HU Zechun,BAO Zhiyuan,et al.Real-time Decomposition Strategy of Aggregate Power for Large-scale Electric Vehicle Charging and Battery Swapping Facilities[J].Automation of Electric Power Systems,2026,50(15):158-168. DOI： 10.7500/AEPS20250319001.

摘要

为发挥大规模电动汽车充换电设施的调节能力，运营商通常将其聚合参与车网互动，再将集总功率目标曲线分解为个体充放电计划。然而，现有研究主要聚焦于充电设施，且分解方法易引起个体功率波动。针对上述问题，在更小松弛度、更长剩余时间优先级准则的基础上，提出一种考虑前瞻和再分配机制的集总功率分解方法，通过前瞻周期自适应调整提高计算效率；构建了基于动态电池储备的换电站实时运行策略，采用XGBoost算法预测动力电池储备规模；进而，形成充换电设施联合的集总功率实时分解方法。基于实际数据的仿真结果表明，该方法在10万辆电动汽车接入场景下单步运行时间仅为16.65 s，满足在线控制要求，同时，能够有效降低集总功率分解误差和个体功率波动。

Abstract

To exploit the adjustable capacity of large-scale electric vehicle (EV) charging and battery swapping facilities

operators typically aggregate them for vehicle-to-grid

and then decompose the aggregate power target curve into individual charging and discharging plans. However

existing research mainly focuses on charging facilities

and the decomposition methods tend to cause fluctuations in individual power. To address the above issues

based on the priority criteria of less laxity and longer remaining processing time

an aggregate power decomposition method considering look-ahead and reassignment mechanisms is proposed

which improves computational efficiency through look-ahead cycle adaptive adjustment. The real-time operation strategies based on dynamic battery reserve are constructed for battery swapping stations

and the XGBoost algorithm is used to predict the scale of power battery reserves. Furthermore

a real-time decomposition method of aggregate power is formed for combined charging and battery swapping facilities. The simulation results based on actual data indicate that the method only takes 16.65 seconds to operate in a single step in the scenario of 100 000 EVs

which meets online control requirements and can effectively reduce the aggregate power decomposition error and individual power fluctuations.

关键词

Keywords

references

IEA . Global EV outlook 2024 ［EB/OL］. ［ 2025-02-05 ］. https：//www.iea.org/reports/global-ev-outlook-2024 https://www.iea.org/reports/global-ev-outlook-2024 .

新华社 . 我国新能源汽车保有量高速增长［EB/OL］.［ 2025-02-05 ］. https：//www.gov.cn/zhengce/jiedu/tujie/202501/content_6999527.htm https://www.gov.cn/zhengce/jiedu/tujie/202501/content_6999527.htm .

Xinhua News Agency . The number of renewable energy vehicles in China is growing rapidly ［EB/OL］. ［ 2025-02-05 ］. https：//www.gov.cn/zhengce/jiedu/tujie/202501/content_6999527.htm https://www.gov.cn/zhengce/jiedu/tujie/202501/content_6999527.htm .

中国充电联盟 . 2024年全国电动汽车充换电基础设施运行情况［EB/OL］.［ 2025-02-05 ］. http：//auto.ce.cn/auto/gundong/202501/24/t20250124_39276695.shtml http://auto.ce.cn/auto/gundong/202501/24/t20250124_39276695.shtml .

China Charging Alliance . Operation of national electric vehicle charging and swapping infrastructure in 2024 ［EB/OL］. ［ 2025-02-05 ］. http：//auto.ce.cn/auto/gundong/202501/24/t20250 124_39276695.shtml http://auto.ce.cn/auto/gundong/202501/24/t20250124_39276695.shtml .

裴振坤，王学梅，康龙云 . 电动汽车参与电网辅助服务的控制策略综述［J］. 电力系统自动化， 2023 ， 47 （ 18 ）： 17 - 32 .

PEI Zhenkun ， WANG Xuemei ， KANG Longyun . Review on control strategies for electric vehicles participating in ancillary services of power grid ［J］. Automation of Electric Power Systems ， 2023 ， 47 （ 18 ）： 17 - 32 .

DUAN X Y ， HU Z C ， SONG Y H . Bidding strategies in energy and reserve markets for an aggregator of multiple EV fast charging stations with battery storage ［J］. IEEE Transactions on Intelligent Transportation Systems ， 2021 ， 22 （ 1 ）： 471 - 482 .

YAO W F ， ZHAO J H ， WEN F S ， et al . A hierarchical decomposition approach for coordinated dispatch of plug-in electric vehicles ［J］. IEEE Transactions on Power Systems ， 2013 ， 28 （ 3 ）： 2768 - 2778 .

WENZEL G ， NEGRETE-PINCETIC M ， OLIVARES D E ， et al . Real-time charging strategies for an electric vehicle aggregator to provide ancillary services ［J］. IEEE Transactions on Smart Grid ， 2018 ， 9 （ 5 ）： 5141 - 5151 .

CAO Y S ， WANG H ， LI D M ， et al . Smart online charging algorithm for electric vehicles via customized actor-critic learning ［J］. IEEE Internet of Things Journal ， 2022 ， 9 （ 1 ）： 684 - 694 .

CHEN X ， DALL’ANESE E ， ZHAO C H ， et al . Aggregate power flexibility in unbalanced distribution systems ［J］. IEEE Transactions on Smart Grid ， 2020 ， 11 （ 1 ）： 258 - 269 .

胡俊杰，陆家悦，马文帅，等 . 面向电网调峰的电动汽车聚合商多层级实时控制策略［J］. 电力系统自动化， 2024 ， 48 （ 22 ）： 84 - 95 .

HU Junjie ， LU Jiayue ， MA Wenshuai ， et al . Multi-layer real-time control strategy of electric vehicle aggregators for peak regulation of power grid ［J］. Automation of Electric Power Systems ， 2024 ， 48 （ 22 ）： 84 - 95 .

YAN D X ， HUANG S H ， CHEN Y . Real-time feedback based online aggregate EV power flexibility characterization ［J］. IEEE Transactions on Sustainable Energy ， 2023 ， 15 （ 1 ）： 658 - 673 .

SUBRAMANIAN A ， GARCIA M J ， CALLAWAY D S ， et al . Real-time scheduling of distributed resources ［J］. IEEE Transactions on Smart Grid ， 2013 ， 4 （ 4 ）： 2122 - 2130 .

ZHANG H C ， HU Z C ， XU Z W ， et al . Evaluation of achievable vehicle-to-grid capacity using aggregate PEV model ［J］. IEEE Transactions on Power Systems ， 2017 ， 32 （ 1 ）： 784 - 794 .

MAURICETTE L ， DONG Z H ， ZHANG L N ， et al . Resilience enhancement of urban energy systems via coordinated vehicle-to-grid control strategies ［J］. CSEE Journal of Power and Energy Systems ， 2022 ， 9 （ 2 ）： 433 - 443 .

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 Hao ， HU Zhijian ， LI Tiange ， 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 .

XU Y J ， PAN F ， TONG L . Dynamic scheduling for charging electric vehicles： a priority rule ［J］. IEEE Transactions on Automatic Control ， 2016 ， 61 （ 12 ）： 4094 - 4099 .

LONG T ， JIA Q S ， WANG G M ， et al . Efficient real-time EV charging scheduling via ordinal optimization ［J］. IEEE Transactions on Smart Grid ， 2021 ， 12 （ 5 ）： 4029 - 4038 .

ZHANG M Y ， YANG H R ， XU Y L ， et al . Learning-based real-time aggregate flexibility provision and scheduling of electric vehicles ［J］. IEEE Transactions on Smart Grid ， 2024 ， 15 （ 6 ）： 5840 - 5852 .

SHALABY A A ， SHAABAN M F ， MOKHTAR M ， et al . A dynamic optimal battery swapping mechanism for electric vehicles using an LSTM-based rolling horizon approach ［J］. IEEE Transactions on Intelligent Transportation Systems ， 2022 ， 23 （ 9 ）： 15218 - 15232 .

ZHOU Y Q ， YAU D K Y ， YOU P C ， et al . Optimal-cost scheduling of electrical vehicle charging under uncertainty ［J］. IEEE Transactions on Smart Grid ， 2017 ， 9 （ 5 ）： 4547 - 4554 .

CHEN T Q ， GUESTRIN C . XGBoost： a scalable tree boosting system ［C］// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ， August 13-17 ， 2016 ， San Francisco， USA ： 785 - 794 .

UTKARSH K ， DING F ， JIN X ， et al . A network-aware distributed energy resource aggregation framework for flexible， cost-optimal， and resilient operation ［J］. IEEE Transactions on Smart Grid ， 2022 ， 13 （ 2 ）： 1213 - 1224 .

鲍志远，胡泽春 . 大规模电动汽车充换电设施可调能力聚合评估与预测［J］. 中国电机工程学报， 2025 ， 45 （ 11 ）： 4103 - 4116 .

BAO Zhiyuan ， HU Zechun . Aggregated assessment and prediction of adjustable capacity for large-scale electric vehicle charging and battery swapping facilities ［J］. Proceedings of the CSEE ， 2025 ， 45 （ 11 ）： 4103 - 4116 .

ZHANG M Z ， YU S S ， YU H L ， et al . Dispatchable capacity optimization strategy for battery swapping and charging station aggregators to participate in grid operations ［J］. Energy Reports ， 2023 ， 10 ： 734 - 743 .

WANG Z Q ， HOU S Z ， LI B K ， et al . A day-ahead bidding strategy for battery swapping and charging system participating in the regulation market ［J］. IET Generation ， Transmission & Distribution， 2023 ， 17 （ 5 ）： 1135 - 1147 .

邓艺璇，黄玉萍，黄周春 . 基于随机森林算法的电动汽车充放电容量预测［J］. 电力系统自动化， 2021 ， 45 （ 21 ）： 181 - 188 .

DENG Yixuan ， HUANG Yuping ， HUANG Zhouchun . Charging and discharging capacity forecasting of electric vehicles based on random forest algorithm ［J］. Automation of Electric Power Systems ， 2021 ， 45 （ 21 ）： 181 - 188 .

YAN D X ， MA C B ， CHEN Y . Distributed coordination of charging stations considering aggregate EV power flexibility ［J］. IEEE Transactions on Sustainable Energy ， 2023 ， 14 （ 1 ）： 356 - 370 .

BAO Z Y ， HU Z C ， MUJEEB A . A novel electric vehicle aggregator bidding method in electricity markets considering the coupling of cross-day charging flexibility ［J］. IEEE Transactions on Transportation Electrification ， 2024 ， 10 （ 4 ）： 8790 - 8805 .

张沛，周钰朋，崔晖，等 . 基于可解释机制的极限传输能力计算知识表达模型［J］. 电力系统自动化， 2023 ， 47 （ 15 ）： 179 - 187 .

ZHANG Pei ， ZHOU Yupeng ， CUI Hui ， et al . Interpretable mechanism based knowledge expression model for total transfer capability computation ［J］. Automation of Electric Power Systems ， 2023 ， 47 （ 15 ）： 179 - 187 .

张波，高远，李铁成，等 . 考虑光伏电源可靠性的新能源配电网数据驱动无功电压优化控制［J］. 中国电机工程学报， 2024 ， 44 （ 15 ）： 5934 - 5947 .

ZHANG Bo ， GAO Yuan ， LI Tiecheng ， et al . Data-driven voltage/var optimization control of active distribution network considering the reliability of photovoltaic power supply ［J］. Proceedings of the CSEE ， 2024 ， 44 （ 15 ）： 5934 - 5947 .

FHWA . 2017 national household travel survey ［EB/OL］. ［ 2025-02-05 ］. https：//nhts.ornl.gov https://nhts.ornl.gov .

北京市发展和改革委员会 . 关于调整本市一般工商业销售电价有关问题的通知［EB/OL］.［ 2025-02-05 ］. https：//www.beijing.gov.cn/zhengce/gfxwj/201905/t20190531_82985.html https://www.beijing.gov.cn/zhengce/gfxwj/201905/t20190531_82985.html .

Beijing Municipal Development and Reform Commission . Notice on adjusting general industrial and commercial sales electricity prices in this city ［EB/OL］. ［ 2025-02-05 ］. https：//www.beijing.gov.cn/zhengce/gfxwj/201905/t20190531_82985.html https://www.beijing.gov.cn/zhengce/gfxwj/201905/t20190531_82985.html .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Pilot Trap Under Triple Constraints: Dilemmas of V2G Large-scale Application in North America and Lessons for China

Vehicle-to-Grid for Large-scale Applications: Practical Progress, Key Bottlenecks, and Technical Pathways

Optimal Low-carbon Scheduling with Vehicle-Grid Spatio-temporal Coordination Considering Carbon Quotas for Life Cycle of Electric Vehicles

Convexification of Charging and Discharging Model and Constraint Set Contraction Method for Accurate Electric Vehicle Aggregation

Review of Key Vehicle-side Technologies for Vehicle-to-Grid

Related Author

HUANG Yuxiang

XU Xingyuan

CHEN Haoyong

ZHANG Xiaoping

HUANG Xueliang

LIU Yongdong

ZHANG Ziqi

GONG Chengming

Related Institution

Guangzhou Development Group Incorporated

School of Electric Power Engineering, South China University of Technology

Guangzhou Development Group Research Institute Co., Ltd.

Department of Electronic, Electrical and Systems Engineering, University of Birmingham

School of Electrical Engineering, Southeast University

⁰