基于用户偏好的电动汽车储能V2G策略优化

doi:10.19799/j.cnki.2095-4239.2023.0173

摘要/Abstract

摘要：

近年来我国电动汽车行业飞速发展，其中电动汽车入网技术(vehicle-to-grid，V2G)在中国新电力系统和能源互联网中是成本较低、规模较大、安全性能较好的一项新兴技术，成为未来发展的趋势。由于当前V2G技术不够成熟，试点项目较少，用户参与V2G的放电行为特征数据和V2G参与电力市场的案例分析较少。为了更加精准模拟电动汽车放电行为和评估V2G的经济效益和社会效益，以电动汽车用户的空闲时间和对放电截止容量的接受度为分类标准对电动汽车用户群进一步划分，建立了不同偏好的电动汽车用户V2G充放电负荷曲线，以聚合商收益为目标建立了优化函数模型。通过算例分析可知，不同的调度计划对不同类型电动汽车的需求不尽相同；对不同用户群进行分类调度的收益明显高于对单一群体调度的收益，并且不同车型组合优化调度比单一车型优化调度效果更优。对优化前后参与V2G的电动汽车的碳排放进行评估，评估结果显示优化后减碳率达到20%。由此可得优化后的策略在经济效益和社会效益上相比于原始模型都有所提升。

关键词: 电动汽车, V2G, 碳排放, 效益评估

Abstract:

In recent years, the electric vehicle industry in our country has developed rapidly, with emerging technologies such as vehicle-to-grid (V2G) gaining prominence in China's new electric power system and energy internet. V2G presents a future development trend owing to its low cost, scalability, and promising safety performance. Currently, the V2G technology is not fully developed, leading to a shortage of pilot projects, limited user engagement in V2G discharge behavior data, and insufficient analysis of V2G participation in the power market. To simulate electric vehicle discharge behavior more accurately and evaluate the economic and social benefits of V2G, electric vehicle (EV) users are further divided based on their idle time and willingness to accept discharge cutoff capacity. This allows us to establish discharge load curves for EV users with different preferences. Aiming at aggregator income, an optimization function model is established. Through the analysis of examples, we observed that different scheduling plans have varying demands for different types of EVs. Classification scheduling for different user groups is more beneficial than single-group modeling scheduling. Additionally, the optimization effect of different combinations of EV types is better than that of a single type. The carbon emission of EVs participating in V2G was evaluated and achieved a carbon reduction rate of 20% through optimization. Consequently, the optimized strategy enhances economic and social benefits compared with the original model.

Key words: electric vehicles, V2G, carbon emission, benefit evaluation

中图分类号:

TM 73

洪睿洁, 顾丹珍, 莫阮清, 蔡思楠, 张超林. 基于用户偏好的电动汽车储能V2G策略优化[J]. 储能科学与技术, 2023, 12(8): 2659-2667.

Ruijie HONG, Danzhen GU, Ruanqing MO, Sinan CAI, Chaolin ZHANG. Research on optimization of EV energy storage V2G strategy based on user preference[J]. Energy Storage Science and Technology, 2023, 12(8): 2659-2667.

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项目	A	B	C	D	E	F
对放电截止电池容量需求	20%	20%	30%	30%	40%	40%
电动汽车空闲时间	3～5 h	>5 h	3～5 h	>5 h	3～5 h	>5 h

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