Analysis of Energy Consumption at Public Charging Stations, A Nebraska Case Study

被引：17
作者：
Almaghrebi, Ahmad [1 ]
Al Juheshi, Fares [1 ]
Nekl, Jarod [1 ]
James, Kevin [1 ]
Alahmad, Mahmoud [1 ]
机构：
[1] Univ Nebraska Lincoln, Omaha, NE 68588 USA
来源：
2020 IEEE TRANSPORTATION ELECTRIFICATION CONFERENCE & EXPO (ITEC) | 2020年
关键词：
Charging Behavior; Energy Consumption; Plug-in Charging Demand; Electric Vehicles; Public Charging Stations; PLUG-IN HYBRID; ELECTRIC VEHICLE; IMPACTS; DEMAND; INFRASTRUCTURE; OPTIMIZATION; BENEFITS;
D O I：
10.1109/itec48692.2020.9161456
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：
Plug-in Electric Vehicle (PEV) charging stations can have a significant influence on the electric grid and its reliability. In order to evaluate the influence of rapid penetration of PEVs and their charging infrastructure on the electric grid, and assess their potential benefits to the future micro-grid, statistical analysis of PEV charging behavior is essential for planning as well as running public charging stations in different areas. In this paper, 27,481 charging sessions from public charging stations in the state of Nebraska are analyzed to determine the relationship between energy consumption, time, and location of charging. A multinomial logistic regression model is presented to aid utility companies in anticipating the charging demand of PEV users, as well as the impact on the electric grid at increasing PEV penetration rates.
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页数：6
相关论文
共 30 条
  • [1] Sparse Reconstruction of Glucose Fluxes Using Continuous Glucose Monitors
    Al-Matouq, Ali Ahmed
    Laleg-Kirati, Taous-Meriem
    Novara, Carlo
    Rabbone, Ivana
    Vincent, Tyrone
    [J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2020, 17 (05) : 1797 - 1809
  • [2] Alahmad A, 2016, IEEE INT CONF INNOV, P82
  • [3] Almaghrebi A., 2020, The Impact of PEV User Charging Behavior in Building Public Charging Infrastructure
  • [4] A review of EVs charging: From the perspective of energy optimization, optimization approaches, and charging techniques
    Amjad, Muhammad
    Ahmad, Ayaz
    Rehmani, Mubashir Husain
    Umer, Tariq
    [J]. TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT, 2018, 62 : 386 - 417
  • [5] PEV Charging Profile Prediction and Analysis Based on Vehicle Usage Data
    Ashtari, Ali
    Bibeau, Eric
    Shahidinejad, Soheil
    Molinski, Tom
    [J]. IEEE TRANSACTIONS ON SMART GRID, 2012, 3 (01) : 341 - 350
  • [6] Ensemble machine learning-based algorithm for electric vehicle user behavior prediction
    Chung, Yu-Wei
    Khaki, Behnam
    Li, Tianyi
    Chu, Chicheng
    Gadh, Rajit
    [J]. APPLIED ENERGY, 2019, 254
  • [7] Chung YW, 2018, 2018 IEEE INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS)
  • [8] Impacts of Electric Vehicle charging under electricity market operations
    Foley, Aoife
    Tyther, Barry
    Calnan, Patrick
    Gallachoir, Brian O.
    [J]. APPLIED ENERGY, 2013, 101 : 93 - 102
  • [9] Guha A, 2018, IEEE TRANSP ELECT C, P731, DOI 10.1109/ITEC.2018.8450105
  • [10] Hai Y, 2021, P 3 ANN IEEE PROGN S, DOI [10.1109/PHM.2012.6228904, DOI 10.1109/PHM.2012.6228904]
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