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
网络首发:2026-03-16,
纸质出版:2026-03-16
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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, et al. Bidding operation strategies for charging stations considering various vehicle-grid interactive scenarios[J]. 2026, 59(3): 64-73.
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. DOI: 10.11930/j.issn.1004-9649.202501061.
WANG Shiqian, HUA Yuanpeng, LI Qiuyan, et al. Bidding operation strategies for charging stations considering various vehicle-grid interactive scenarios[J]. 2026, 59(3): 64-73. DOI: 10.11930/j.issn.1004-9649.202501061.
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。
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.
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