上海交通大学学报

上海交通大学学报 >

2024 , Vol. 58 >Issue 5: 636 - 646

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.348

新型电力系统与综合能源

考虑补偿激励的电动汽车多区域优化调度策略

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  • 1.华北电力大学 电气与电子工程学院,北京 102206
    2.国网河北省电力有限公司信息通信分公司,石家庄 050000
孙 毅(1972-),教授,博士生导师,从事能源互联网信息通信、需求侧管理相关研究.
葛明洋,硕士生;E-mail:gmy8242@163.com.

收稿日期: 2022-09-05

  修回日期: 2022-11-07

  录用日期: 2022-12-13

  网络出版日期: 2023-03-07

基金资助

国家电网有限公司科技项目(5204XA22000D)

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Multi-Region Optimal Scheduling Strategy for Electric Vehicles Considering Compensation Incentives

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  • 1. School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
    2. Information and Telecommunication Branch, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China

Received date: 2022-09-05

  Revised date: 2022-11-07

  Accepted date: 2022-12-13

  Online published: 2023-03-07

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摘要

针对充电区域内分布式新能源(DRE)与电动汽车负荷供需不均的问题,提出一种考虑补偿激励的电动汽车多区域优化调度策略,引导电动汽车前往不同充电区域充电,以促进DRE就地消纳.首先,基于需求价格弹性及用户时间焦虑建立电动汽车用户需求电量模型.其次,基于剩余电量可达原则和空闲时间冗余原则将电动汽车划分为可响应和不可响应集群,利用基于后悔理论的充电区域决策模型对可响应集群按区域作进一步划分.最后,建立电动汽车多区域优化调度模型,从最大化充电服务商经济效益及DRE消纳程度两个方面对充电价格进行优化.通过仿真验证证明所提优化策略能够充分考虑时间焦虑和价格弹性对电动汽车用户的影响,充分挖掘用户的响应潜力,在降低DRE消纳偏差量和提高充电服务商经济效益方面具有明显效果.

关键词： 电动汽车; 分布式新能源; 充电补偿; 时间焦虑

本文引用格式

孙毅, 葛明洋, 王献春, 鲍荟谕, 杨泓玥, 姚陶 . 考虑补偿激励的电动汽车多区域优化调度策略[J]. 上海交通大学学报, 2024 , 58(5) : 636 -646 . DOI: 10.16183/j.cnki.jsjtu.2022.348

Abstract

Aimed at the problem of uneven supply and distributed renewable energy (DRE) in charging regions and electric vehicle (EV) loads, a multi-region optimal scheduling strategy for EVs considering compensation incentives is proposed to guide EVs to choose different charging regions, so as to promote local consumption of distributed energy. First, an EV charging response model is established based on the price elasticity of demand and users’ time anxiety. Then, based on the principle of remaining power availability and idle time redundancy, EVs are divided into responsive cluster and non-responsive cluster. A charging area decision model based on regret theory is used to further divide the responsive cluster by region. Finally, a multi-region optimal scheduling model for EVs is established, and the charging price is optimized in terms of maximizing the economic benefits of charging service providers and the consumption of DRE. Simulation cases show that the proposed optimization strategy can fully consider the impact of time anxiety and price elasticity on EV users, fully tap the users’ response potential, and has obvious effects in reducing the deviation of distributed new energy consumption and improving the economic benefits of charging service providers.

Key words： electric vehicle (EV); distributed renewable energy (DRE); charging compensation; time anxiety

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