An On-Demand Compensation Function for an EV as a Reactive Power Service Provider

被引:41
|
作者
Mojdehi, Mohammad Nikkhah [1 ,2 ]
Ghosh, Prasanta [1 ]
机构
[1] Syracuse Univ, Dept Elect Engn & Comp Sci, Syracuse, NY 13244 USA
[2] OBrien & Gere, Syracuse, NY 13202 USA
关键词
Battery degradation; electric vehicle (EV); on-demand; reactive power; reactive power supply function; VEHICLE;
D O I
10.1109/TVT.2015.2504264
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Rapid growth in the deployment of electric vehicles ( EVs) is creating opportunities for system operators to improve the reliability and sustainability of electricity delivery system, while reducing the operating cost. EVs can respond quickly, improve system stability, and provide reactive power support without battery wear. Therefore, EVs could be a promising source of reactive power when they are connected to smart charging equipment. However, under certain scenarios, reactive power support may raise the vehicle charging cost by shifting part of charging power to times of the day when the price of electricity is higher. Participation of EVs in an ancillary service market for reactive power will thus require some level of compensation for EV owners. In this paper, we present a framework for the calculation of reactive power supply function for EVs. We define an objective function representing charging/discharging cost of EVs and then minimize the cost under realistic constraints. An algorithm is then introduced that extracts the EV's supply function for providing on-demand reactive power service at minimum cost. Thus, the system operator can include EVs, as reactive power service providers ( RPSPs) along with other service providers, to meet the on-demand need of the reactive power. Simulation results indicate the EV's ability to provide reactive power service during on-peak periods, when the system operator's need for reactive power is most likely high, at almost no cost to the EV owner.
引用
收藏
页码:4572 / 4583
页数:12
相关论文
共 50 条
  • [31] Short-Term Demand Forecasting for on-Demand Mobility Service
    Qian, Xinwu
    Ukkusuri, Satish V.
    Yang, Chao
    Yan, Fenfan
    [J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 23 (02) : 1019 - 1029
  • [32] Simultaneous Charger Placement and Power Scheduling for On-Demand Provisioning of RF Wireless Charging Service
    Jiang, Huatong
    Li, Yanjun
    Gao, Meihui
    [J]. ALGORITHMS AND ARCHITECTURES FOR PARALLEL PROCESSING, ICA3PP 2021, PT II, 2022, 13156 : 632 - 646
  • [33] On-Demand Service Deployment Strategies for Fog-as-a-Service Scenarios
    Bozorgchenani, Arash
    Tarchi, Daniele
    Cerroni, Walter
    [J]. IEEE COMMUNICATIONS LETTERS, 2021, 25 (05) : 1500 - 1504
  • [34] Pricing EV charging service with demand charge
    Lee, Zachary J.
    Pang, John Z. F.
    Low, Steven H.
    [J]. ELECTRIC POWER SYSTEMS RESEARCH, 2020, 189
  • [35] Research on the On-Demand Service Mode in Cloud Manufacturing
    Jin Xinjuan
    Liu Quan
    [J]. PROCEEDINGS OF 2016 IEEE 7TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING AND SERVICE SCIENCE (ICSESS 2016), 2016, : 285 - 288
  • [36] A Service System with Randomly Behaving On-demand Agents
    Nguyen, Lam M.
    Stolyar, Alexander L.
    [J]. SIGMETRICS/PERFORMANCE 2016: PROCEEDINGS OF THE SIGMETRICS/PERFORMANCE JOINT INTERNATIONAL CONFERENCE ON MEASUREMENT AND MODELING OF COMPUTER SCIENCE, 2016, : 365 - 366
  • [37] Investigating the on-demand service characteristics: an empirical study
    van der Burg, Robbert-Jan
    Ahaus, Kees
    Wortmann, Hans
    Huitema, George B.
    [J]. JOURNAL OF SERVICE MANAGEMENT, 2019, 30 (06) : 739 - 765
  • [38] An on-demand service aggregation and service recommendation method based on RGPS
    Zhao, Yi
    Guo, Junfei
    He, Keqing
    [J]. INTELLIGENT DATA ANALYSIS, 2019, 23 : S3 - S23
  • [39] On-demand ride service platform with differentiated services
    Ma, Lina
    Tao, Zhijie
    Wei, Qiang
    [J]. PLOS ONE, 2024, 19 (01):
  • [40] On-demand service hosting on production grid infrastructures
    Lizhe Wang
    Tobias Kurze
    Jie Tao
    Marcel Kunze
    Gregor von Laszewski
    [J]. The Journal of Supercomputing, 2013, 66 : 1178 - 1193