天气影响下基于风险评估的电动汽车光伏充电站需求响应
作者:
作者单位:

长沙理工大学 电气与信息工程学院,长沙 410114

作者简介:

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。

基金项目:

卡塔尔国家研究基金资助项目(NPRP 8-241-2-095)。


Demand response of photovoltaic electric vehicle charging stations based on weather-impact risk assessment
Author:
Affiliation:

School of Electrical and Information Engineering, Changsha University of Science and Technology, Changsha 410114, P. R. China

Fund Project:

Supported by the Qatar National Research Fund (NPRP 8-241-2-095).

  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [18]
  • |
  • 相似文献
  • | | |
  • 文章评论
    摘要:

    在“碳达峰、碳中和”背景下,新能源发电逐渐占主导地位,电动汽车数量增长显著,电动汽车光伏充电站将在需求响应方面有重要作用。该文提出一种天气影响下基于风险评估的电动汽车光伏充电站的需求响应方案,根据构建的“预测—预防—响应”三阶段流程图,结合电网及天气的地理信息系统(geographic information system, GIS)数据进行多层时空数据整合分析,作出风险地图;并据此进行天气对用户影响的风险评估,对装备有光伏发电的电动汽车充电站的运行成本进行建模,优化充电站资源在日前储备市场的参与方案;在用户参与下分别进行需求侧管理(demand side management, DSM)和停电应急管理(outage management, OM),并进行案例研究。该文研究能有效预测天气对电力用户影响并将其可视化,并验证了带有光伏发电的电动汽车充电站,有助于减轻天气对电力供应造成负面影响的作用。

    Abstract:

    In the context of achieving “carbon peak and carbon neutrality” target in China, renewable energy is gradually taking the dominant role and the number of electric vehicle (EV) is growing significantly. The photovoltaic (PV) EV charging stations will play an important role in demand response of EV. This paper proposes a demand response plan of PV charging stations for EVs under the influence of weather based on risk assessment. According to the “predictive-preventive-corrective” framework, firstly, a risk map is produced by integrating and analyzing the multi-layer spatial-temporal data combing the GIS data of power grid and weather. Next, risk assessment considering the impact of the predicted weather on the customers is performed, the operating cost of PV charging station including the charging/discharging expense is modeled, and the day-ahead reserve market participation strategy of the aggregated resources is optimized. Finally, demand side management and outage management involving PV and charging/discharging of EVs are discussed and case studies are carried out. This paper effectively predicts and visualizes the weather impact on the electricity users, and verifies the roles of PV EV charging stations on mitigating the negative impact of weather on the power supply.

    参考文献
    [1] 国家发展改革委, 国家能源局. 关于推进电力源网荷储一体化和多能互补发展的指导意见[R].2021.National Development and Reform Commission, National Energy Administration. Guidance about boosting the integration of source-net-load-storage and development of multi-energy complement[R]. 2021. (in Chinese)
    [2] 卓振宇, 张宁, 谢小荣, 等. 高比例可再生能源电力系统关键技术及发展挑战[J]. 电力系统自动化, 2021, 45(9): 171-191.Zhuo Z Y, Zhang N, Xie X R, et al. Key technologies and developing challenges of power system with high proportion of renewable energy[J]. Automation of Electric Power Systems, 2021, 45(9): 171-191.(in Chinese)
    [3] 邵成成, 李徐亮, 钱涛, 等. 基于交通均衡的电动汽车快速充电负荷模拟[J]. 中国电机工程学报, 2021, 41(4): 1368-1376, 1543.Shao C C, Li X L, Qian T, et al. Simulation of EV fast charging load based on traffic equilibrium[J]. Proceedings of the CSEE, 2021, 41(4): 1368-1376, 1543.(in Chinese)
    [4] Li S Q, Gu C H, Li J W, et al. Boosting grid efficiency and resiliency by releasing V2G potentiality through a novel rolling prediction-decision framework and deep-LSTM algorithm[J]. IEEE Systems Journal, 2021, 15(2): 2562-2570.
    [5] 余苏敏, 杜洋, 史一炜, 等. 考虑V2B智慧充电桩群的低碳楼宇优化调度[J]. 电力自动化设备, 2021, 41(9): 95-101.Yu S M, Du Y, Shi Y W, et al. Optimal scheduling of low-carbon building considering V2B smart charging pile groups[J]. Electric Power Automation Equipment, 2021, 41(9): 95-101.(in Chinese)
    [6] 杨梓俊, 荆江平, 邓星, 等. 虚拟电厂参与江苏电网辅助服务市场的探讨[J]. 电力需求侧管理, 2021, 23(4): 90-95.Yang Z J, Jing J P, Deng X, et al. Discussion on virtual power plant participating in ancillary service market of Jiangsu power grid[J]. Power Demand Side Management, 2021, 23(4): 90-95.(in Chinese)
    [7] 曹敏, 徐杰彦, 巨健, 等. 用户侧储能设备参与电网辅助服务的技术经济性分析[J]. 电力需求侧管理, 2019, 21(1): 52-55.Cao M, Xu J Y, Ju J, et al. Technical and economic analysis of user side energy storage equipment participating in power grid ancillary services[J]. Power Demand Side Management, 2019, 21(1): 52-55.(in Chinese)
    [8] Trakas D N, Hatziargyriou N D. Resilience constrained day-ahead unit commitment under extreme weather events[J]. IEEE Transactions on Power Systems, 2020, 35(2): 1242-1253.
    [9] 李雪, 孙霆锴, 侯恺, 等. 极端天气下电力系统大范围随机设备故障的N-k安全分析及筛选方法[J]. 中国电机工程学报, 2020, 40(16): 5113-5126.Li X, Sun T K, Hou K, et al. N-k security assessment and screening for large-scale random equipment faults in bulk power grid under extreme weather[J]. Proceedings of the CSEE, 2020, 40(16): 5113-5126.(in Chinese)
    [10] 文福拴, 林鸿基, 胡嘉骅. 需求响应的商业机制与市场框架初探[J]. 电力需求侧管理, 2019, 21(1): 4-9.Wen F S, Lin H J, Hu J H. A preliminary investigation on commercial mechanism and market framework for demand response[J]. Power Demand Side Management, 2019, 21(1): 4-9.(in Chinese)
    [11] Sun Y Y, Yue H, Zhang J F, et al. Minimization of residential energy cost considering energy storage system and EV with driving usage probabilities[J]. IEEE Transactions on Sustainable Energy, 2019, 10(4): 1752-1763.
    [12] Kikusato H, Fujimoto Y, Hanada S I, et al. Electric vehicle charging management using auction mechanism for reducing PV curtailment in distribution systems[J]. IEEE Transactions on Sustainable Energy, 2019, 11(3): 1394-1403.
    [13] 张淼, 陈栩杰, 康家熙, 等. 考虑电动汽车与共享私人车位交易的微电网需求侧管理[J]. 智慧电力, 2020, 48(5): 34-40, 46.Zhang M, Chen X J, Kang J X, et al. Demand side management of microgrid considering the transaction between electric vehicles and shared private parkings[J]. Smart Power, 2020, 48(5): 34-40, 46.(in Chinese)
    [14] Hussain A, Kim H M. EV prioritization and power allocation during outages: a lexicographic method-based multiobjective optimization approach[J]. IEEE Transactions on Transportation Electrification, 2021, 7(4): 2474-2487.
    [15] 姜思坤. 考虑电动汽车并网的主动配电网需求侧管理机制综合规划研究[D]. 青岛: 青岛大学, 2020.Jiang S K. Comprehensive planning of the demand side management mechanism considering the grid connection of electric vehicles[D]. Qingdao: Qingdao University, 2020. (in Chinese)
    [16] 周贤正, 郭创新, 董树锋, 等. 考虑配电网重构的城市多能源配电/气/热网扩展规划[J]. 电力系统自动化, 2019, 43(7): 23-33.Zhou X Z, Guo C X, Dong S F, et al. Expansion planning of urban multi-energy electricity-gas-heating distribution network incorporating electrical reconfiguration[J]. Automation of Electric Power Systems, 2019, 43(7): 23-33.(in Chinese)
    [17] Yan Q, Dokic T, Kezunovic M. GIS-based risk assessment for electric power consumers under severe weather conditions[C]//2016 18th Mediterranean Electrotechnical Conference (MELECON). April 18-20, 2016, Lemesos, Cyprus. IEEE, 2016: 1-6.
    [18] Esri. Storm vulnerability assessment[EB/OL]. (2020-01-01)[2021-11-28]. https://www.esri.com/en-us/arcgis/products/arcgis-solutions/resources.
    相似文献
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

颜勤,涂晓帆.天气影响下基于风险评估的电动汽车光伏充电站需求响应[J].重庆大学学报,2023,46(4):37-45.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2022-02-14
  • 在线发布日期: 2023-05-12
文章二维码