基于用户意愿的电动汽车备用容量多目标优化

doi:10.16183/j.cnki.jsjtu.2022.131

上海交通大学学报 ›› 2023, Vol. 57 ›› Issue (11): 1501-1511.doi: 10.16183/j.cnki.jsjtu.2022.131

所属专题：《上海交通大学学报》2023年“新型电力系统与综合能源”专题

• 新型电力系统与综合能源 • 上一篇下一篇

基于用户意愿的电动汽车备用容量多目标优化

邵萍¹, 杨之乐²(), 李慷³, 朱晓东¹

1.郑州大学电气与信息工程学院, 郑州 450001
2.中国科学院深圳先进技术研究院,广东深圳 518000
3.利兹大学电子与电气工程学院,英国利兹 LS2 9JT

收稿日期:2022-04-27 修回日期:2022-07-18 接受日期:2022-07-22 出版日期:2023-11-28 发布日期:2023-12-01
通讯作者: 杨之乐,博士,副研究员;E-mail: zl.yang@siat.ac.cn.
作者简介:邵萍(1997-),硕士生,研究方向为EV入网及其优化调度.
基金资助:
国家自然基金委面上项目(52077213);国家自然科学基金青年科学基金项目(62003332)

Multi-Objective Optimization of Electric Vehicle Spare Capacity Based on User Wishes

SHAO Ping¹, YANG Zhile²(), LI Kang³, ZHU Xiaodong¹

1. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
2. Shenzhen Institute of Advanced Technology of the Chinese Academy of Sciences, Shenzhen 518000, Guangdong, China
3. School of Electronics and Electrical Engineering, University of Leeds, Leeds LS2 9JT, U.K.

Received:2022-04-27 Revised:2022-07-18 Accepted:2022-07-22 Online:2023-11-28 Published:2023-12-01

摘要/Abstract

摘要：

电动汽车(EV)保有量可观且具有储能的特性,使其参与电力系统运行调控提供备用服务成为可能.针对此建立基于EV用户意愿,以集电商经济收益、微电网功率波动和用户满意度为目标的多目标优化调度模型.考虑到负荷预测误差的影响,对模型进行日前阶段和日内实时修正阶段的多时间尺度优化调度分析.求解方法采用主流的多目标智能优化算法NSGA-III 算法,同时将NSGA-II 和MOEA/D算法作为对比算法,通过对比实验选出最优调度方案并分析EV提供备用容量的场景.仿真结果证明所提模型的有效性.

关键词: 备用容量, 用户意愿, 多目标, 多时间尺度, 负荷需求不确定性

Abstract:

Due to the considerable number and the characteristics of energy storage, it is possible for electric vehicles (EVs) to participate in the operation and regulation of power system to provide reserve service. In view of this, a multi-objective optimal scheduling model is established based on the wishes of electric vehicle users, with the objectives of the economic benefits of electricity collectors, microgrid power fluctuations and user satisfaction. Considering the uncertainty of load demand, the optimal scheduling analysis of multi-time scale scenes with the day-ahead time scale and the intra-day real-time correction time scale is conducted. The mainstream multi-objective intelligent optimization algorithm NSGA-III algorithm is adopted in the solution method, and the NSGA-II and MOEA/D algorithms are used for comparison. The optimal dispatching scheme is selected through comparative experiments and scenarios where EVs provide spare capacity are analyzed. The simulation results verify the feasibility and effectiveness of the proposed model.

Key words: spare capacity, user wishes, multi-objective, multi-time scale, load demand uncertainty

中图分类号:

TP731

邵萍, 杨之乐, 李慷, 朱晓东. 基于用户意愿的电动汽车备用容量多目标优化[J]. 上海交通大学学报, 2023, 57(11): 1501-1511.

SHAO Ping, YANG Zhile, LI Kang, ZHU Xiaodong. Multi-Objective Optimization of Electric Vehicle Spare Capacity Based on User Wishes[J]. Journal of Shanghai Jiao Tong University, 2023, 57(11): 1501-1511.

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时间	负荷/kW	发电功率/kW	时间	负荷/kW	发电功率/kW
0:00	7000	6500	12:00	14000	14000
1:00	7500	6000	13:00	13000	13000
2:00	8500	6600	14:00	12000	12000
3:00	9500	7700	15:00	10500	10300
4:00	10000	8300	16:00	10000	9500
5:00	11000	9500	17:00	11000	10000
6:00	11500	10500	18:00	12000	12000
7:00	12000	12000	19:00	14000	14000
8:00	13000	13000	20:00	13000	13000
9:00	14000	14000	21:00	11000	10800
10:00	14500	14500	22:00	9000	9000
11:00	15000	15000	23:00	8000	8000

模式	算法	F₁/美元	F₂/kW²	F₃
日前调度	NSGA-III	13362.74	6352108.49	11.53
	NSGA-II	13158.60	6419170.18	11.06
	MOEA/D	12756.56	6448278.59	10.44
日内调度	NSGA-III	8947.78	432816.70	16.59
	NSGA-II	8771.79	430594.38	15.38
	MOEA/D	8695.90	427959.96	15.15

基于用户意愿的电动汽车备用容量多目标优化

Multi-Objective Optimization of Electric Vehicle Spare Capacity Based on User Wishes

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