Demand Forecasting Associated with Electric Vehicle Penetration on Distribution Systems

被引:0
|
作者
Botero, Andres F. [1 ]
Rios, Mario A. [1 ]
机构
[1] Univ Los Andes, Dept Elect Engn & Elect, Bogota, Colombia
关键词
Battery Charging Infrastructure; Demand Forecasting; Electric Vehicle; Electric Vehicle Market Penetration; Spatial and Temporal Analysis;
D O I
暂无
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
This paper proposes a methodology for forecasting the electrical power demand associated to the penetration of electric vehicles on a distribution system. The simulation model is framed in a temporal and spatial base that considers a more detailed analysis of some demographical aspects and technical characteristics of the electric vehicles. This approach allows the assessment of the impact of such penetration in the different areas of a city, considering that each part of the network will have several consumption behaviors. Additionally, it gives the advantage to easily develop multiple case studies by adjusting driver pattern probabilities, electric vehicle characteristics, geographic and demographic parameters of the city and even charging scenarios.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation
    Sumanasena, Vidura
    Gunasekara, Lakshitha
    Kahawala, Sachin
    Mills, Nishan
    De Silva, Daswin
    Jalili, Mahdi
    Sierla, Seppo
    Jennings, Andrew
    ENERGIES, 2023, 16 (05)
  • [32] Forecasting the Demand for Electric Energy
    Gates, J. E.
    JOURNAL OF LAND AND PUBLIC UTILITY ECONOMICS, 1942, 18 (01): : 77 - 81
  • [33] Active demand response with electric heating systems: Impact of market penetration
    Arteconi, Alessia
    Patteeuw, Dieter
    Bruninx, Kenneth
    Delarue, Erik
    D'haeseleer, William
    Helsen, Lieve
    APPLIED ENERGY, 2016, 177 : 636 - 648
  • [34] Peak-demand Management for Improving Undervoltages in Distribution Systems with Electric Vehicle Connection by Stationary Battery
    Piromjit, Pittayut
    Tayjasanant, Thavatchai
    2017 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE AND EXPO, ASIA-PACIFIC (ITEC ASIA-PACIFIC), 2017, : 1557 - 1562
  • [35] Optimal Scheduling Strategy of Distribution Network Based on Electric Vehicle Forecasting
    Li, Fenglei
    Dou, Chunxia
    Xu, Shiyun
    ELECTRONICS, 2019, 8 (07)
  • [36] FINANCIAL FORECASTING MODEL FOR RURAL ELECTRIC DISTRIBUTION SYSTEMS
    STOVER, CN
    IEEE TRANSACTIONS ON POWER APPARATUS AND SYSTEMS, 1974, PA93 (06): : 1740 - 1740
  • [37] Prediction of Electric Vehicle Penetration and Its Impacts on Distribution Systems: A Real-World Case Study in Maryland
    Wang, Wenyu
    Ye, Zuzhao
    Yu, Nanpeng
    Chen, Po-Chen
    2024 IEEE CONFERENCE ON TECHNOLOGIES FOR SUSTAINABILITY, SUSTECH, 2024, : 390 - 396
  • [38] Electric Vehicle Charging Demand Forecasting Model Based on Data-driven Approach
    Xing Q.
    Chen Z.
    Huang X.
    Zhang Z.
    Leng Z.
    Xu Y.
    Zhao Q.
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2020, 40 (12): : 3796 - 3812
  • [39] Impact of electric vehicle charging demand on power distribution grid congestion
    Li, Yanning
    Jenn, Alan
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2024, 121 (18)
  • [40] Travel Motif-Based Learning Scheme for Electric Vehicle Charging Demand Forecasting
    Rashid, Mamunur
    Elfouly, Tarek
    Chen, Nan
    2023 IEEE VEHICLE POWER AND PROPULSION CONFERENCE, VPPC, 2023,