考虑多场景车网互动的充电站投标运营策略

doi:10.11930/j.issn.1004-9649.202501061

中国电力 ›› 2026, Vol. 59 ›› Issue (3): 64-73.DOI: 10.11930/j.issn.1004-9649.202501061

• 电-碳协同下分布式能源系统运营关键技术 • 上一篇下一篇

考虑多场景车网互动的充电站投标运营策略

王世谦¹(), 华远鹏¹, 李秋燕¹, 刘博², 杨建平³(), 向月³

1. 国网河南省电力公司经济技术研究院，河南郑州　450052
2. 国网河南省电力公司，河南郑州　450052
3. 四川大学电气工程学院，四川成都　610065

收稿日期:2025-01-20 修回日期:2025-11-30 发布日期:2026-03-16 出版日期:2026-03-28
作者简介:
王世谦（1988），男，硕士，高级工程师，从事能源经济及电网规划研究。E-mail：wangshiqian@qq.com
杨建平（1998），男，通信作者，博士研究生，从事车网互动、配电网运行规划研究。E-mail：jping_y@163.com
基金资助:
国网河南省电力公司科技项目（5217L0240003）。

Bidding operation strategies for charging stations considering various vehicle-grid interactive scenarios

WANG Shiqian¹(), HUA Yuanpeng¹, LI Qiuyan¹, LIU Bo², YANG Jianping³(), XIANG Yue³

1. State Grid Henan Electric Power Company Economic and Technology Research Institute, Zhengzhou 450052, China
2. State Grid Henan Electric Power Company, Zhengzhou 450052, China
3. College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Received:2025-01-20 Revised:2025-11-30 Online:2026-03-16 Published:2026-03-28
Supported by:
This work is supported by Science and Technology Project of State Grid Henan Electric Power Company (No.5217L0240003).

摘要/Abstract

摘要：

随着电动汽车的规模化发展，充电负荷逐渐成为社会最大的分布式可调节资源，研究充电站参与车网互动的运营优化策略对电动汽车与电网的协同发展具有重要价值和意义。对此，首先考虑电动汽车用户差异化的充电需求以及用能特性，构建了基于订单数据的充电负荷分类预测模型，提供充电站参与响应能量管理依据。然后结合当前分级补贴和基线差额等工程因素进行了物理建模，提出了更贴合实际工程场景的充电站投标优化策略，以适应邀约型、市场型不同模式下的车网互动场景。最后基于实际订单数据进行了不同场景下充电站参与需求响应模拟分析，实验结果表明该模型在积极引导电动汽车响应电网调节需求的同时，能有效保证充电站的互动收益。

关键词: 电动汽车, 需求响应, 分级补贴, 订单数据, 负荷分类预测, 投标策略

Abstract:

With the large-scale development of electric vehicles, charging load has gradually become the largest distributed adjustable resource in the power system. Investigating operational optimization strategies for electric vehicle charging stations' participation in vehicle-grid integration has crucial implications for the synergistic development of both the electric vehicle industry and the power grids. Firstly, considering the differentiated charging demand and energy consumption characteristics of EV users, a charging load prediction model based on order data is constructed to provide the basis for charging stations' participation in responsive energy management. And then, based on engineering factors such as current tiered subsidies and baseline differentials, a physical model is developed, and a more practically-oriented bidding optimization strategy for charging stations is proposed, which is designed to adapt to different vehicle-grid interaction scenarios, including both invitation-based and market-based modes. Finally, simulation analyses of charging stations participating in demand response under various scenarios are conducted based on actual order data. The experimental results demonstrate that the proposed model can effectively ensure the interactive revenue for charging stations while actively guiding electric vehicles to respond to grid regulation demands.

Key words: electric vehicles, demand response, tiered subsidies, order data, load forecasting, bidding strategy

王世谦, 华远鹏, 李秋燕, 刘博, 杨建平, 向月. 考虑多场景车网互动的充电站投标运营策略[J]. 中国电力, 2026, 59(3): 64-73.

WANG Shiqian, HUA Yuanpeng, LI Qiuyan, LIU Bo, YANG Jianping, XIANG Yue. Bidding operation strategies for charging stations considering various vehicle-grid interactive scenarios[J]. Electric Power, 2026, 59(3): 64-73.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202501061

https://www.electricpower.com.cn/CN/Y2026/V59/I3/64

图/表 7

表 1 响应补贴标准

Table 1 Demand response subsidy standards

响应时长T/min	补贴价格/(元·(kW·次)^–1)
60≤T≤120	6
T＞120	9

图 1 订单充电功率展示

Fig.1 Display of charging power based on order

图 2 充电负荷分类

Fig.2 Charging load classification

图 3 充电负荷分类预测误差

Fig.3 Prediction error of charging load

图 4 充电站邀约型投标响应方案

Fig.4 Bidding strategy for invitation-based demand response

图 5 市场型削峰响应方案

Fig.5 Bidding strategy for market-oriented peak-shaving response

图 6 市场型填谷响应方案

Fig.6 Bidding strategy for market-oriented valley-filling response

参考文献 30

1	刘志文, 李岩, 邵冲, 等. 考虑柔性资源协同互动的配电网灵活性评估方法[J]. 中国电力, 2024, 57 (10): 158- 165.
	LIU Zhiwen, LI Yan, SHAO Chong, et al. Distribution network flexibility evaluation method considering collaborative interaction of flexible resources[J]. Electric Power, 2024, 57 (10): 158- 165.
2	JIANG T, WU C H, ZHANG R F, et al. Flexibility clearing in joint energy and flexibility markets considering TSO-DSO coordination[J]. IEEE Transactions on Smart Grid, 2023, 14 (2): 1376- 1387.
3	孙东磊, 王宪, 孙毅, 等. 基于多面体不确定集合的电力系统灵活性量化评估方法[J]. 中国电力, 2024, 57 (9): 146- 155.
	SUN Donglei, WANG Xian, SUN Yi, et al. Polyhedral uncertainty set based power system flexibility quantitative assessment[J]. Electric Power, 2024, 57 (9): 146- 155.
4	刘志伟, 马悦, 沙志成, 等. 考虑新能源多重相关性的柔性配电网分布鲁棒优化策略[J]. 中国电力, 2024, 57 (12): 97- 108.
	LIU Zhiwei, MA Yue, SHA Zhicheng, et al. Distributionally robust operation for flexible distribution networks considering multi-correlation of renewable power generation[J]. Electric Power, 2024, 57 (12): 97- 108.
5	朱润泽, 王德军. 基于LSTM神经网络的光伏系统功率预测[J]. 电力科技与环保, 2023, 39 (3): 201- 206.
	ZHU Runze, WANG Dejun. Power prediction of photovoltaic system based on LSTM neural network[J]. Electric Power Technology and Environmental Protection, 2023, 39 (3): 201- 206.
6	汪繁荣, 梅涛, 卢璐. 基于相似日聚类和VMD-LTWDBO-BiLSTM的短期光伏功率预测[J]. 智慧电力, 2024, 52 (10): 56- 63, 111.
	WANG Fanrong, MEI Tao, LU Lu. Short-term PV power prediction based on similar day clustering with VMD-LTWDBO-BiLSTM[J]. Smart Power, 2024, 52 (10): 56- 63, 111.
7	唐竹, 肖宇航, 郭淳, 等. 基于CEEMDAN模态分解和TCN-BiGRU的短期电力负荷预测[J]. 智慧电力, 2024, 52 (12): 59- 64, 72.
	TANG Zhu, XIAO Yuhang, GUO Chun, et al. Short-term electricity load forecasting based on CEEMDAN decomposition and TCN-BIGRU model[J]. Smart Power, 2024, 52 (12): 59- 64, 72.
8	臧海祥, 舒宇心, 傅雨婷, 等. 考虑多需求场景的城市电动汽车充电站多目标规划[J]. 电力系统保护与控制, 2021, 49 (5): 67- 80.
	ZANG Haixiang, SHU Yuxin, FU Yuting, et al. Multi-objective planning of an urban electric vehicle charging station considering multi demand scenarios[J]. Power System Protection and Control, 2021, 49 (5): 67- 80.
9	栗然, 臧向迪, 张文昕, 等. 共享电动汽车混合充换电站选址优化[J]. 电力自动化设备, 2021, 41 (10): 67- 74.
	LI Ran, ZANG Xiangdi, ZHANG Wenxin, et al. Location optimization of hybrid charging and changing station for shared electric vehicles[J]. Electric Power Automation Equipment, 2021, 41 (10): 67- 74.
10	姜欣, 冯永涛, 熊虎, 等. 基于出行概率矩阵的电动汽车充电站规划[J]. 电工技术学报, 2019, 34 (S1): 272- 281.
11	田梦瑶, 汤波, 杨秀, 等. 综合考虑充电需求和配电网接纳能力的电动汽车充电站规划[J]. 电网技术, 2021, 45 (2): 498- 509.
	TIAN Mengyao, TANG Bo, YANG Xiu, et al. Planning of electric vehicle charging stations considering charging demands and acceptance capacity of distribution network[J]. Power System Technology, 2021, 45 (2): 498- 509.
12	王超, 麻秀范. 基于电动汽车用户电价响应的充电负荷优化模型建立[J]. 内蒙古电力技术, 2017, 35 (1): 1- 7.
	WANG Chao, MA Xiufan. Charging load optimization model based on electric vehicle users' price response[J]. Inner Mongolia Electric Power, 2017, 35 (1): 1- 7.
13	陈璐, 徐青山, 杨永标, 等. 促进削峰填谷的居民实时积分套餐优化设计[J]. 电力自动化设备, 2020, 40 (7): 134- 142.
	CHEN Lu, XU Qingshan, YANG Yongbiao, et al. Optimization design of residents' real-time score plan for promoting peak-load shifting[J]. Electric Power Automation Equipment, 2020, 40 (7): 134- 142.
14	赵梓潼, 顾兵. 需求响应下基于电动汽车负荷聚合商的充放电电价与时段研究[J]. 东北电力大学学报, 2023, 43 (6): 79- 86.
	ZHAO Zitong, GU Bing. Research on charging and discharging price and time period basedon electric vehicle load aggregator under demand response[J]. Journal of Northeast Electric Power University, 2023, 43 (6): 79- 86.
15	范晋衡, 刘琦颖, 曲大鹏, 等. 考虑EV用户响应特性的激励型DR的削峰效果和各方需求分析[J]. 电力科学与技术学报, 2022, 37 (6): 138- 149.
	FAN Jinheng, LIU Qiying, QU Dapeng, et al. Analysis on peak shaving effect and needs of all parties based on incentive demand response considering response characteristics of EV users[J]. Journal of Electric Power Science and Technology, 2022, 37 (6): 138- 149.
16	刘加国, 单来支, 袁飞, 等. 考虑需求响应的产消群边缘自平衡交易机制研究[J]. 华北电力大学学报(自然科学版), 2021, 48 (2): 47- 54.
	LIU Jiaguo, SHAN Laizhi, YUAN Fei, et al. Research on edge self-balancing trading mechanism of production and sales group considering demand response[J]. Journal of North China Electric Power University (Natural Science Edition), 2021, 48 (2): 47- 54.
17	刘怀东, 刘豪, 冯志强, 等. 基于新型需求响应模型的风电消纳市场机制[J]. 电力系统及其自动化学报, 2021, 33 (3): 43- 48, 58.
	LIU Huaidong, LIU Hao, FENG Zhiqiang, et al. Market mechanism of wind power consumption based on novel demand-side response model[J]. Proceedings of the CSU-EPSA, 2021, 33 (3): 43- 48, 58.
18	王蓓蓓, 王骐鑫, 李雅超, 等. 区块链环境下考虑信用的需求响应资源交易机制[J]. 电力系统自动化, 2021, 45 (5): 30- 38.
	WANG Beibei, WANG Qixin, LI Yachao, et al. Transaction mechanism of demand response resource considering credit in blockchain environment[J]. Automation of Electric Power Systems, 2021, 45 (5): 30- 38.
19	SHARMA S, JAIN P. Integrated TOU price-based demand response and dynamic grid-to-vehicle charge scheduling of electric vehicle aggregator to support grid stability[J]. International Transactions on Electrical Energy Systems, 2020, 30 (1): e12160.
20	王俊杰, 贾雨龙, 米增强, 等. 基于双重激励机制的电动汽车备用服务策略[J]. 电力系统自动化, 2020, 44 (10): 68- 76.
	WANG Junjie, JIA Yulong, MI Zengqiang, et al. Reserve service strategy of electric vehicles based on double-incentive mechanism[J]. Automation of Electric Power Systems, 2020, 44 (10): 68- 76.
21	HUANG Z, FANG B L, DENG J. Multi-objective optimization strategy for distribution network considering V2G-enabled electric vehicles in building integrated energy system[J]. Protection and Control of Modern Power Systems, 2020, 5, 7.
22	詹祥澎, 杨军, 韩思宁, 等. 考虑电动汽车可调度潜力的充电站两阶段市场投标策略[J]. 电力系统自动化, 2021, 45 (10): 86- 96.
	ZHAN Xiangpeng, YANG Jun, HAN Sining, et al. Two-stage market bidding strategy of charging station considering schedulable potential capacity of electric vehicle[J]. Automation of Electric Power Systems, 2021, 45 (10): 86- 96.
23	文艺林, 胡泽春, 宁剑, 等. 基于分布鲁棒机会约束的充电运营商参与调峰市场投标策略[J]. 电力系统自动化, 2022, 46 (7): 23- 32.
	WEN Yilin, HU Zechun, NING Jian, et al. Bidding strategy of charging operator participating in peak regulation market based on distributionally robust chance constraint[J]. Automation of Electric Power Systems, 2022, 46 (7): 23- 32.
24	陈卓, 江周熹, 陈康, 等. 考虑充电节点过载的电动汽车公共充电网络规划方法[J]. 电测与仪表, 2025, 62 (9): 116- 124.
	CHEN Zhuo, JIANG Zhouxi, CHEN Kang, et al. Planning method of electric vehicle public charging network with overloaded charging nodes[J]. Electrical Measurement & Instrumentation, 2025, 62 (9): 116- 124.
25	周卓, 芦翔, 刘海涛, 等. 含新能源发电的电动汽车充电站充电功率在线优化策略研究[J]. 电测与仪表, 2024, 61 (2): 101- 107.
	ZHOU Zhuo, LU Xiang, LIU Haitao, et al. Research on on-line charging power optimization strategy for EV charging station with renewable energy generation[J]. Electrical Measurement & Instrumentation, 2024, 61 (2): 101- 107.
26	刘祺, 王承民, 谢宁, 等. 新型配电系统中考虑电动汽车差异化行为特性的充换电站规划方法[J]. 智慧电力, 2024, 52 (9): 18- 24, 64.
	LIU Qi, WANG Chengmin, XIE Ning, et al. Charging and swapping stations planning method considering differentiated behavior characteristics of electric vehicles in new distribution system[J]. Smart Power, 2024, 52 (9): 18- 24, 64.
27	梁云龙, 罗朝旭, 刘泽宇, 等. 基于V2G技术的电动汽车充电器分层控制策略[J]. 智慧电力, 2024, 52 (10): 64- 69, 78.
	LIANG Yunlong, LUO Zhaoxu, LIU Zeyu, et al. Hierarchical control strategy for electric vehicle charging station based on V2G technology[J]. Smart Power, 2024, 52 (10): 64- 69, 78.
28	张勇军, 林靖淳, 林衍扬. 基于交能耦合的电动汽车有序充馈电策略[J]. 浙江电力, 2025, 44 (10): 112- 120.
	ZHANG Yongjun, LIN Jingchun, LIN Yanyang. An orderly charging/discharging strategy for electric vehicles based on power-traffic coupling[J]. Zhejiang Electric Power, 2025, 44 (10): 112- 120.
29	李振坤, 胡焜, 姚一聪. 考虑用户意愿的电动汽车集群可调度容量评估方法[J]. 浙江电力, 2026, 45 (1): 34- 47.
	LI Zhenkun, HU Kun, YAO Yicong. A dispatch capacity assessment method for electric vehicle clusters considering user willingness[J]. Zhejiang Electric Power, 2026, 45 (1): 34- 47.
30	HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9 (8): 1735- 1780.

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考虑多场景车网互动的充电站投标运营策略

Bidding operation strategies for charging stations considering various vehicle-grid interactive scenarios

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考虑多场景车网互动的充电站投标运营策略

Bidding operation strategies for charging stations considering various vehicle-grid interactive scenarios

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