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.

图/表 12

图1

图2

图3

表1

图4

图5

图6

表2

图7

图8

图9

图10

参考文献 27

[1]	何西, 涂春鸣, 王丽丽, 等. 考虑用户行车习惯的电动汽车充电双层控制策略[J]. 电力系统自动化, 2018, 42(3): 64-69.
	HE Xi, TU Chunming, WANG Lili, et al. Double-layer charging strategy for electric vehicles considering users’ driving patterns[J]. Automation of Electric Power Systems, 2018, 42(3): 64-69.
[2]	杨昆. 推动实现碳达峰、碳中和加快构建以新能源为主体的新型电力系统[J]. 中国电业, 2021(5): 8-11.
	YANG Kun. Promote the realization of peak carbon dioxide emissions and carbon neutrality and accelerate the construction of a new power system with new energy as the main body[J]. China Electric Power, 2021(5): 8-11.
[3]	武昭原, 周明, 王剑晓, 等. 双碳目标下提升电力系统灵活性的市场机制综述[J]. 中国电机工程学报, 2022, 42(21): 7746-7764.
	WU Zhaoyuan, ZHOU Ming, WANG Jianxiao, et al. Review on market mechanism to enhance the flexibility of power system under the dual-carbon target[J]. Proceedings of the CSEE, 2022, 42(21): 7746-7764.
[4]	张亚朋, 穆云飞, 贾宏杰, 等. 电动汽车虚拟电厂的多时间尺度响应能力评估模型[J]. 电力系统自动化, 2019, 43(12): 94-103.
	ZHANG Yapeng, MU Yunfei, JIA Hongjie, et al. Response capability evaluation model with multiple time scales for electric vehicle virtual power plant[J]. Automation of Electric Power Systems, 2019, 43(12): 94-103.
[5]	吕祥梅, 刘天琪, 刘绚, 等. 考虑高比例新能源消纳的多能源园区日前低碳经济调度[J]. 上海交通大学学报, 2021, 55(12): 1586-1597.
	LÜ Xiangmei, LIU Tianqi, LIU Xuan, et al. Low-carbon economic dispatch of multi-energy park considering high proportion of renewable energy[J]. Journal of Shanghai Jiao Tong University, 2021, 55(12): 1586-1597.
[6]	XUE Y S, YU X H. Beyond smart grid—Cyber-physical-social system in energy future[point of view][J]. Proceedings of the IEEE, 2017, 105(12): 2290-2292. doi: 10.1109/JPROC.2017.2768698 URL
[7]	张高, 王旭, 蒋传文. 基于主从博弈的含电动汽车虚拟电厂协调调度[J]. 电力系统自动化, 2018, 42(11): 48-55.
	ZHANG Gao, WANG Xu, JIANG Chuanwen. Stackelberg game based coordinated dispatch of virtual power plant considering electric vehicle management[J]. Automation of Electric Power Systems, 2018, 42(11): 48-55.
[8]	SHAFIEE S, FOTUHI-FIRUZABAD M, RASTEGAR M. Investigating the impacts of plug-in hybrid electric vehicles on power distribution systems[J]. IEEE Transactions on Smart Grid, 2013, 4(3): 1351-1360. doi: 10.1109/TSG.2013.2251483 URL
[9]	吴巨爱, 薛禹胜, 谢东亮, 等. 电动汽车参与运行备用的能力评估及其仿真分析[J]. 电力系统自动化, 2018, 42(13): 101-107.
	WU Juai, XUE Yusheng, XIE Dongliang, et al. Evaluation and simulation analysis of reserve capability for electric vehicles[J]. Automation of Electric Power Systems, 2018, 42(13): 101-107.
[10]	LIU J B, ZHUGE C X, TANG J H C G, et al. A spatial agent-based joint model of electric vehicle and vehicle-to-grid adoption: A case of Beijing[J]. Applied Energy, 2022, 310: 118581. doi: 10.1016/j.apenergy.2022.118581 URL
[11]	AI X, WU Z Y, HU J J, et al. Robust operation strategy enabling a combined wind/battery power plant for providing energy and frequency ancillary services[J]. International Journal of Electrical Power & Energy Systems, 2020, 118: 105736. doi: 10.1016/j.ijepes.2019.105736 URL
[12]	刘紫琦. 基于用户驾驶行为的电动汽车有序充电策略[D]. 北京: 北京交通大学, 2017.
	LIU Ziqi. The EV coordinated charging strategy based on users’ driving behavior[D]. Beijing: Beijing Jiaotong University, 2017.
[13]	杨文涛, 王蕾, 邹波, 等. 基于大数据服务平台的电动汽车有序充放电管理[J]. 电力建设, 2018, 39(6): 28-41. doi: 10.3969/j.issn.1000-7229.2018.06.005
	YANG Wentao, WANG Lei, ZOU Bo, et al. Coordinated charging and discharging management of electric vehicles based on a multi-function big data service platform[J]. Electric Power Construction, 2018, 39(6): 28-41. doi: 10.3969/j.issn.1000-7229.2018.06.005
[14]	王毅, 陈进, 麻秀, 等. 采用分群优化的电动汽车与电网互动调度策略[J]. 电力自动化设备, 2020, 40(5): 77-85.
	WANG Yi, CHEN Jin, MA Xiu, et al. Interactive scheduling strategy between electric vehicles and power grid based on group optimization[J]. Electric Power Automation Equipment, 2020, 40(5): 77-85.
[15]	SARKER M R, PANDŽIĆ H, SUN K W, et al. Optimal operation of aggregated electric vehicle charging stations coupled with energy storage[J]. IET Generation, Transmission & Distribution, 2018, 12(5): 1127-1136. doi: 10.1049/gtd2.v12.5 URL
[16]	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.
[17]	周华锋, 胡亚平, 聂涌泉, 等. 区域互联电网电能量与备用辅助服务联合优化模型研究[J]. 电网技术, 2020, 44(3): 991-1001.
	ZHOU Huafeng, HU Yaping, NIE Yongquan, et al. Co-optimization model of energy and reserve auxiliary service for regional interconnected power grid[J]. Power System Technology, 2020, 44(3): 991-1001.
[18]	吴洲洋, 艾欣, 胡俊杰. 电动汽车聚合商参与调频备用的调度方法与收益分成机制[J]. 电网技术, 2021, 45(3): 1041-1050.
	WU Zhouyang, AI Xin, HU Junjie. Dispatching and income distributing of electric vehicle aggregators’ participation in frequency regulation[J]. Power System Technology, 2021, 45(3): 1041-1050.
[19]	GAO S A, LI H L, JURASZ J, et al. Optimal charging of electric vehicle aggregations participating in energy and ancillary service markets[J]. IEEE Journal of Emerging & Selected Topics in Industrial Electronics, 2022, 3(2): 270-278.
[20]	李士动, 施泉生, 赵文会, 等. 计及电动汽车接入电网的备用服务多目标竞价优化[J]. 电力系统自动化, 2016, 40(2): 77-83.
	LI Shidong, SHI Quansheng, ZHAO Wenhui, et al. A multi-objective optimization based bidding model with vehicle-to-grid reserve provision considered[J]. Automation of Electric Power Systems, 2016, 40(2): 77-83.
[21]	陆建丽, 张晓峰, 张有兵, 等. 电动汽车参与经济调度的多目标模型[J]. 计算机系统应用, 2015, 24(3): 266-269.
	LU Jianli, ZHANG Xiaofeng, ZHANG Youbing, et al. Multi-objective model of economic dispatch incorporating electric vehicles[J]. Computer Systems & Applications, 2015, 24(3): 266-269.
[22]	薛禹胜, 谢东亮, 薛峰, 等. 支持信息-物理-社会系统研究的跨领域交互仿真平台[J]. 电力系统自动化, 2022, 46(10): 138-148.
	XUE Yusheng, XIE Dongliang, XUE Feng, et al. A cross-field interactive simulation platform for supporting research on cyber-physical-social systems[J]. Automation of Electric Power Systems, 2022, 46(10): 138-148.
[23]	ZHENG Y, SONG Y E, HILL D J, et al. Online distributed MPC-based optimal scheduling for EV charging stations in distribution systems[J]. IEEE Transactions on Industrial Informatics, 2019, 15(2): 638-649. doi: 10.1109/TII.2018.2812755 URL
[24]	CALEARO L, THINGVAD A, SUZUKI K, et al. Grid loading due to EV charging profiles based on pseudo-real driving pattern and user behavior[J]. IEEE Transactions on Transportation Electrification, 2019, 5(3): 683-694. doi: 10.1109/TTE.6687316 URL
[25]	TAO S, LIAO K Y, XIAO X N, et al. Charging demand for electric vehicle based on stochastic analysis of trip chain[J]. IET Generation, Transmission & Distribution, 2016, 10(11): 2689-2698. doi: 10.1049/gtd2.v10.11 URL
[26]	WU J A, XUE Y S, XIE D L, et al. Multi-agent modeling and analysis of EV users’ travel willingness based on an integrated causal/statistical/behavioral model[J]. Journal of Modern Power Systems & Clean Energy, 2018, 6(6): 1255-1263.
[27]	WHITE C D, ZHANG K M. Using vehicle-to-grid technology for frequency regulation and peak-load reduction[J]. Journal of Power Sources, 2011, 196(8): 3972-3980. doi: 10.1016/j.jpowsour.2010.11.010 URL

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

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 27

相关文章 15

编辑推荐

Metrics

本文评价

[1]	彭斌, 刘慧鑫, 陶耀辉. 基于变径基圆渐开线涡旋压缩机的几何模型及优化研究[J]. 上海交通大学学报, 2023, 57(8): 1046-1054.
[2]	朱海南, 王娟娟, 陈兵兵, 张厚望, 陈健, 吴秋伟. 考虑经济性与碳排放的电-气综合能源系统多目标规划[J]. 上海交通大学学报, 2023, 57(4): 422-431.
[3]	贾鲁生, 韦家耀, 艾尚茂. 缓波型钢悬链立管静态构型多目标粒子群优化分析[J]. 海洋工程装备与技术, 2023, 10(3): 132-137.
[4]	. 基于多视角道路相机的时空关联三维车辆检测与跟踪系统[J]. J Shanghai Jiaotong Univ Sci, 2023, 28(1): 52-60.
[5]	马洲俊, 王勇, 王杰, 陈少宇. 柔性控制器MMC子模块最优冗余数量双重协同优化方法[J]. 上海交通大学学报, 2022, 56(3): 325-332.
[6]	曾博, 穆宏伟, 董厚琦, 曾鸣. 考虑5G基站低碳赋能的主动配电网优化运行[J]. 上海交通大学学报, 2022, 56(3): 279-292.
[7]	何维, 孙宏磊, 陶袁钦, 蔡袁强. 开挖引起的隧道位移动态多目标优化反演预测[J]. 上海交通大学学报, 2022, 56(12): 1688-1699.
[8]	杨博, 王俊婷, 俞磊, 曹璞璘, 束洪春, 余涛. 基于孔雀优化算法的配电网储能系统双层多目标优化配置[J]. 上海交通大学学报, 2022, 56(10): 1294-1307.
[9]	何子述, 苏洋, 程婷. 基于共址MIMO雷达组网系统的机会协同目标跟踪方法[J]. 空天防御, 2022, 5(1): 6-11.
[10]	李玲芳, 陈占鹏, 胡炎, 邰能灵, 高孟平, 朱涛. 基于灵活性和经济性的可再生能源电力系统扩展规划[J]. 上海交通大学学报, 2021, 55(7): 791-801.
[11]	孙鸿强, 张占月, 方宇强. 基于NSGA-II算法的编队卫星重构策略[J]. 上海交通大学学报, 2021, 55(3): 320-330.
[12]	郑书坚, 赵文杰, 钟永建, 贺敏, 赵文龙. 面向多目标拦截问题的协同任务分配方法研究[J]. 空天防御, 2021, 4(3): 55-64.
[13]	何健, 袁双, 李芳, 杨贝尔, 李庆波, 何志敏. 基于GPU+FPGA异构计算资源的目标信息智能处理技术[J]. 空天防御, 2021, 4(3): 10-16.
[14]	齐东润, 陈刚. 无人驾驶机器人多目标模糊操纵策略[J]. 上海交通大学学报, 2021, 55(10): 1310-1319.
[15]	王运龙, 姜云博, 管官, 邢佳鹏, 于光亮. 基于知识工程的船舶机舱设备三维布局设计[J]. 上海交通大学学报, 2021, 55(10): 1219-1227.