基于动态分时电价引导的电动汽车需求侧响应
CSTR:
作者:
作者单位:

(1.国网湖南电力有限公司,湖南 长沙 410000;2.长沙理工大学电气与信息工程学院,湖南 长沙 410114)

通讯作者:

颜 勤(1988—),女,博士,讲师,主要从事电动汽车及新能源接入电力系统运行优化研究;E?mail:qin.yan@csust.edu.cn

中图分类号:

TM73

基金项目:

湖南省战略性新兴产业科技攻关与重大科技成果转化项目(2018GK4002);国网湖南省电力有限公司管理咨询项目


Demand response of electric vehicle based on dynamic time‑to‑use electricity price
Author:
Affiliation:

(1.State Grid Hunan Electric Power Co., Ltd., Changsha 410000, China; 2.School of Electrical &Information Engineering, Changsha University of Science & Technology, Changsha 410114, China )

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    摘要:

    为激励电动汽车(electric vehicles,EVs)参与需求侧响应以减小电网负荷峰谷差,同时提高电动汽车用电经济性,首先,利用蒙特卡罗法模拟出电动汽车无序充电负荷,再根据电动汽车是否受电网调控或者受价格信号引导,将电动汽车分为3类进行处理;然后,以电网峰谷差均方值最小和用户充放电费用最小为目标,搭建基于电价需求弹性矩阵的动态分时电价(time?of?use,TOU)需求响应模型;最后,根据湖南某地区历史负荷数据对一天的电价进行分段,再通过仿真分析,验证考虑实时负荷反馈的动态分时电价可以有效地应对负荷波动,并且对于减小峰谷差和降低用户用电成本的效果更明显。

    Abstract:

    To incentivize electric vehicles (EVs) to participate in demand-side response to reduce the peak-to-valley difference in grid load and enhance the economic viability of EV electricity usage, the Monte Carlo method is used to simulate the unordered charging load of EVs. EVs are then categorized into three types based on whether they are regulated by the grid or guided by price signals. Subsequently, a dynamic time-of-use (TOU) pricing demand response model is established on the basis of the electricity price demand elasticity matrix, with the objectives of minimizing the mean square value of the grid's peak-to-valley difference and minimizing user charging and discharging costs. Using historical load data from a region in Hunan and segmenting the electricity prices for a day, simulation analysis is conducted to verify that dynamic TOU pricing considering real-time load feedback can effectively manage load fluctuations, and it has a more pronounced effect on reducing the peak-to-valley difference and lowering user electricity costs.

    参考文献
    [1] 中华人民共和国国务院新闻办公室.新时代的中国能源发展[EB/OL].https://baike.so.com/doc/30070372-31689769.html,2020-12-21. The State Council Information Office of the People's Republic of China.China's energy development in the new era[EB/OL].https://baike.so.com/doc/30070372-31689769.html,2020-12-21.
    [2] 肖先勇,郑子萱.“双碳”目标下新能源为主体的新型电力系统:贡献、关键技术与挑战[J].工程科学与技术,2022,54(1):47-59. XIAO Xianyong,ZHENG Zixuan.New power systems dominated by renewable energy towards the goal of emission peak & carbon neutrality: contribution,key techniques,and challenges[J].Advanced Engineering Sciences.2022,54(1):47-59.
    [3] 李晖,刘栋,姚丹阳.面向碳达峰碳中和目标的我国电力系统发展研判[J].中国电机工程学报,2021,41(18):6245-6259. LI Hui,LIU Dong,YAO Danyang.Analysis and reflection on the development of power system towards the goal of carbon emission peak and carbon neutrality[J].Proceedings of the CSEE,2021,41(18):6245-6259.
    [4] 唐葆君,李茹.可再生能源成本下降对电力行业碳达峰与碳中和的影响[J].企业经济,2021,40(8):53-63. TANG Baojun,LI Ru.Impact of reduced renewable energycosts on carbon peak and carbon neutrality of power industry[J].Enterprise Economy,2021,40(8):53-63.
    [5] 韩文轩.2021年电荒:政策分析与选择[J].能源,2021(12):68-74. HAN Wenxuan.Power shortage in 2021: policy analysis and options[J].Energy,2021(12):68-74.
    [6] 张伊宁.考虑需求响应的能源互联网优化运行研究[D].杭州:浙江大学,2019. ZHANG Y N.Research on optimal operation for energy internet considering demand response[D].Hangzhou:Zhejiang University,2019.
    [7] CAO Y J,TANG S W,LI C B,et al.An optimized EV charging model considering TOU rPrice and SOC curve[J].IEEE Transactions on Smart Grid,2012,3(1):388-393.
    [8] 中华人民共和国公安部.全国新能源汽车保有量已突破1 000万辆[EB/OL].https://app.mps.gov.cn/gdnps/pc/content.jsp?id=8577652,2022-07-06. Ministry of Public Security of the People's Republic of China.The number of new energy vehicles in China has exceeded 10 million[EB/OL].https://app.mps.gov.cn/gdnps/pc/content.jsp?id=8577652,2022-07-06.
    [9] 洪奕,刘瑜俊,徐青山,等.基于积分制和分时电价的EV混合型精准需求响应策略[J].电力自动化设备,2020,40(11):106-116. HONG Yi,LIU Yujun,XU Qingshan,et al.EV hybrid precise demand response strategy based on points system and TOU price[J].Electric Power Automation Equipment,2020,40(11):106-116.
    [10] 胡澄,刘瑜俊,徐青山,等.面向含风电楼宇的EV优化调度策略[J].电网技术,2020,44(2):564-572. HU Cheng,LIU Yujun,XU Qingshan,et al.Optimal scheduling strategy for electric vehicles in buildings with wind power[J].Power System Technology,2020,44(2):564-572.
    [11] 朱超婷,杨玲君,崔一铂,等.考虑需求响应用户参与度的主动配电网优化调度[J].电测与仪表,2023,60(4):99-105. ZHU Chaoting,YANG Lingjun,CUI Yibo,et al.Optimal scheduling of active distribution network considering user participation in demand response[J].Electrical Measurement & Instrumentation,2023,60(4):99-105.
    [12] 方尚尚,王冰,胡庆燚,等.基于蒙特卡罗算法的电动汽车充电需求负荷研究[J].系统仿真技术,2020,16(3):150-161. FANG Shangshang,WANG Bin,HU Qingyan,et al. Research on electric vehicle charging demand load based on Monte Carlo algorithm[J].System Simulation Technology,2020,16(3):150-161.
    [13] 林健,熊军,孙明浩,等.一种住宅小区电动汽车有序充电控制系统的设计[J].电力科学与技术学报,2020,35(5):46-53. LIN Jian,XIONG Jun,SUN Minghao,et al.Design of coordinated charging control system for electric vehicles charging load in the residential area[J].Journal of Electric Power Science and Technology,2020,35(5):46-53.
    [14] 俞子聪,龚萍,王植,等.居民区电动汽车有序充放电控制策略[J].科学技术与工程,2021,21(1):380-386. YU Zicong,GONG Ping,WANG Zhi,et al.An orderly charging/diacharging control strategy for electric vehicles in residential areas[J].Science Technology and Engineering,2021,21(1):380-386.
    [15] ZHANG H,XIN A,GAO Z,et al.Study on orderly charging management of EVs based on demand response[C]//IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific),Beijing,China,2014.
    [16] 阮文骏,王蓓蓓,李扬,等.峰谷分时电价下的用户响应行为研究[J].电网技术,2012,36(7):86-93. RUAN Wenjun,WANG Beibei,LI Yang,et al.Customer response behavior in time-of-use price[J].Power System Technology,2012,36(7):86-93.
    [17] 魏步晗,鲍刚,李振华.基于支持向量回归预测模型考虑天气因素和分时电价因素的短期电力负荷预测[J].电网与清洁能源,2023,39(11):9-19. WEI Buhan,BAO Gang,LI Zhenhua.Short-term electricity load forecasting based on support vector regression forecasting model considering weather factors and time-of-use tariff factors[J].Power System and Clean Energy,2023,39(11):9-19.
    [18] 黄剑平,陈皓勇,林镇佳,等.需求侧响应背景下分时电价研究与实践综述[J].电力系统保护与控制,2021,49(9):178-187. HUANG Jianping,CHEN Haoyong,LIN Zhenjia,et al.A summary of time-of-use research and practice in a demand response environment[J].Power System Protection and Control,2012,36(7):86-93.
    [19] 王洪涛,邹斌.基于动态贝叶斯网络的电价区间预测[J].电力系统保护与控制,2022,50(5):117-127. WANG Hongtao,ZOU Bin.Prediction interval forecasts of electricity price based on dynamic Bayesian networks[J].Power System Protection and Control,2022,50(5):117-127.
    [20] 欧名勇,陈仲伟,谭玉东,等.基于峰谷分时电价引导下的电动汽车充电负荷优化[J].电力科学与技术学报,2020,35(5):54-59. OU Mingyong,CHEN Zhongwei,TAN Yudong,et al.Optimization electric vehicle charging load based on peak-to-valley time of use electricity price[J].Journal of Electric Power Science and Technology,2020,35(5):54-59.
    [21] 刘浩田,陈锦,朱熹,等.一种基于价格弹性矩阵的居民峰谷分时电价激励策略[J].电力系统保护与控制,2021,49(5):116-123. LIU Haotian,CHEN Jin,ZHU Xi,et al.An incentive strategy of residential peak-valley price based on price elasticity matrix of demand[J].Power System Protection and Control,2021,49(5):116-123.
    [22] 李长云,徐敏灵,蔡淑媛.计及电动汽车违约不确定性的微电网两段式优化调度策略[J].电工技术学报,2023,38(7):1838-1851. LI Changyun,XU Minling,CAI Shuyuan.Two-stage optimal scheduling strategy for micro-grid considering EV default uncertainty[J].Transactions of China Electrotechnical Society,2023,38(7):1838-1851.
    [23] 王守相,王瀚樟,赵倩宇,等.面向配电网光伏接纳能力提升的分时电价优化方法[J].电力系统自动化,2023,47(10):38-46. WANG Shouxiang,WANG Hanzhang,ZHAO Qianyu,et al.Optimization method of time-of-use electricity price for improving photovoltaic hosting capacity of distribution network[J].Automation of Electric Power Systems,2023,47(10) : 38-46.
    [24] 张西竹,刘洵源,杨文涛,等.动态分时电价机制下的电动汽车分层调度策略[J].电力建设,2018,39(12):73-80. ZHANG Xizhu,LIU Xunyuan,YANG Wentao,et al.A hierarchical scheduling strategy for electric vehicles under dynamic time-of-use tariff mechanism[J].Electric Power Construction,2018,39(12):73-80.
    [25] 赵玲霞,王兴贵,丁颖杰,等.考虑分时电价及光热电站参与的多能源虚拟电厂优化调度[J].电力建设,2022,43(4): 119-129. ZHAO Lingxia,WANG Xinggui,DING Yingjie,et al.Optimal dispatch of multi-energy virtual power plant considering time-of-use electricity price and CSP plant[J].Electric Power Construction,2022,43(4): 119-129.
    [26] ZHANG J,WANG Y,LI J,et al.Day-ahead operation of EV charging station in a logistics center considering charging behaviors of different types of EVs[C]//IEEE 11th Annual International Conference on CYBER Technology in Automation,Control,and Intelligent Systems (CYBER),Jiaxing,China,2021.
    [27] 赵琦玮,王昕,王鑫郎,等.含不同集群电动汽车的微电网优化调度[J].可再生能源,2019,37(3):379-385. ZHAO Qiwei,WANG Xin,WANG Xinlang,et al.Optimal scheduling of microgrid with different cluster electric vehicles[J].Renewable Energy Resources,2019,37(3):379-385.
    [28] Fedreal Highway Administration,US Department of Transportation. 2009 national household travel survey [EB/OL].http//nbts.ornl.gov/2009/pub/stt.pdf,2011-06-20.
    [29] 郭月新.考虑环境温度下电动汽车中长期负荷预测及充电负荷优化研究[D].南昌:南昌大学,2021. GUO Yuexin.Research on medium and long Term load prediction and load optimization of electric vehicle considering ambient temperature[D].Nanchang:Nanchang University,2021.
    [30] 蔡黎,葛棚丹,代妮娜,等.电动汽车入网负荷预测及其与电网互动研究进展综述[J].智慧电力,2022,50(7):96-103. CAI Li,GE Pengdan,DAI Nina,et al.Review of research progress on load prediction and grid interaction of electric vehicles[J].Smart Power,2022,50(7):96-103.
    [31] 王茜,张粒子.采用NSGA-II混合智能算法的风电场多目标电网规划[J].中国电机工程学报,2011,31(19):17-24. WANG Qian,ZHANG Lizi.Multi-objective transmission planning associated with wind farms applying hybrid intelligent algorithm[J].Proceedings of the CSEE,2011,31(19):17-24.
    [32] 向佳炜.基于NSGA-Ⅱ的多目标配电网重构[D].长沙:长沙理工大学,2014. XIANG Jiawei.Muiti-objevtive distribution network reconfiguration based on NSGA-II[D].Changsha: Changsha University of Science &Technology,2019.
    [33] 蒋猛.基于改进 NSGA-II 算法的电—气—热综合能源系统多目标优化[J].发电技术,2020,41(2):131-136. JIANG Meng.Multi-objective optimization of electricity-gas-heat based on improved NSGA-II algorithm integrated energy system[J].Power Generation Technology,2020,41(2):131-136.
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叶文浩,陈耀红,颜 勤,等.基于动态分时电价引导的电动汽车需求侧响应[J].电力科学与技术学报,2024,39(4):138-145.
YE Wenhao, CHEN Yaohong, YAN Qin, et al. Demand response of electric vehicle based on dynamic time‑to‑use electricity price[J]. Journal of Electric Power Science and Technology,2024,39(4):138-145.

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