上海交通大学学报

上海交通大学学报 >

2023 , Vol. 57 >Issue 11: 1501 - 1511

DOI: https://doi.org/10.16183/j.cnki.jsjtu.2022.131

新型电力系统与综合能源

基于用户意愿的电动汽车备用容量多目标优化

展开
  • 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
邵 萍(1997-),硕士生,研究方向为EV入网及其优化调度.

收稿日期: 2022-04-27

  修回日期: 2022-07-18

  录用日期: 2022-07-22

  网络出版日期: 2023-07-05

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)

收起

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

Expand
  • 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 date: 2022-04-27

  Revised date: 2022-07-18

  Accepted date: 2022-07-22

  Online published: 2023-07-05

Fold

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

本文引用格式

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 . DOI: 10.16183/j.cnki.jsjtu.2022.131

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

参考文献

[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.
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[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.
[7] 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.
[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.
[9] 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.
[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.
[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.
[12] 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.
[13] 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.
[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.
[14] 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, PAND?I? 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.
[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.
[16] 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.
[17] 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.
[18] 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.
[20] 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.
[21] 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.
[22] 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.
[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.
[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.
[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.
Options
文章导航

/

〈 〉