IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v233-234y2019ip644-658.html
   My bibliography  Save this article

Electric vehicles’ impacts on residential electric local profiles – A stochastic modelling approach considering socio-economic, behavioural and spatial factors

Author

Listed:
  • Fischer, David
  • Harbrecht, Alexander
  • Surmann, Arne
  • McKenna, Russell

Abstract

This paper presents a stochastic bottom-up model to assess electric vehicles’ (EV) impact on load profiles at different parking locations as well as their potential for load management strategies. The central innovation lies in the consideration of socio-economic, technical and spatial factors, all of which influence charging electricity demand and behaviour at different locations. Based on a detailed statistical analysis of a large dataset on German mobility, the most statistically significant influencing factors on residential charging behaviour could be identified. Whilst household type and economic status are the most important factors for the number of cars per household, the driver’s occupation has the strongest influence on the first departure time and parking time whilst at work. EV use is modelled using an inhomogeneous Markov-chain to sample a sequence of destinations of each car trip, depending (amongst other factors) on the occupation of the driver, the weekday and the time of the day. Probability distributions for the driven kilometres, driving durations and parking durations are used to model presence at a charger and calculate electricity demand. The probability distributions are retrieved from a national mobility dataset of 70,000 car trips and filtered for a set of socio-economic and demographic factors. Individual charging behaviour is included in the model using a logistic function accounting for the sensitivity of the driver towards (low) battery SOC. The model output is compared to the mobility dataset to test its validity and shown to have a deviation in key household mobility characteristics of just a few percentage points. The model is then employed to analyse the impact of uncontrolled charging of EV on the residential load profile. It is found that the absolute load peaks will increase by up to a factor of 8.5 depending on the loading infrastructure, the load in high load hours will increase by approx. a factor of three and annual electricity demand will approximately double.

Suggested Citation

  • Fischer, David & Harbrecht, Alexander & Surmann, Arne & McKenna, Russell, 2019. "Electric vehicles’ impacts on residential electric local profiles – A stochastic modelling approach considering socio-economic, behavioural and spatial factors," Applied Energy, Elsevier, vol. 233, pages 644-658.
    • Handle: RePEc:eee:appene:v:233-234:y:2019:i::p:644-658
    DOI: 10.1016/j.apenergy.2018.10.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918315666
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.10.010?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Harris, Chioke B. & Webber, Michael E., 2014. "An empirically-validated methodology to simulate electricity demand for electric vehicle charging," Applied Energy, Elsevier, vol. 126(C), pages 172-181.
    2. Munkhammar, Joakim & Widén, Joakim & Rydén, Jesper, 2015. "On a probability distribution model combining household power consumption, electric vehicle home-charging and photovoltaic power production," Applied Energy, Elsevier, vol. 142(C), pages 135-143.
    3. Richardson, David B., 2013. "Electric vehicles and the electric grid: A review of modeling approaches, Impacts, and renewable energy integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 19(C), pages 247-254.
    4. Li, Xiaomin & Chen, Pu & Wang, Xingwu, 2017. "Impacts of renewables and socioeconomic factors on electric vehicle demands – Panel data studies across 14 countries," Energy Policy, Elsevier, vol. 109(C), pages 473-478.
    5. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M. & Jenkins, Nick & Carroll, Steve & Barker, Myles, 2016. "A data-driven approach for characterising the charging demand of electric vehicles: A UK case study," Applied Energy, Elsevier, vol. 162(C), pages 763-771.
    6. Iversen, Emil B. & Morales, Juan M. & Madsen, Henrik, 2014. "Optimal charging of an electric vehicle using a Markov decision process," Applied Energy, Elsevier, vol. 123(C), pages 1-12.
    7. Wang, Hewu & Zhang, Xiaobin & Ouyang, Minggao, 2015. "Energy consumption of electric vehicles based on real-world driving patterns: A case study of Beijing," Applied Energy, Elsevier, vol. 157(C), pages 710-719.
    8. Harbrecht, Alexander & McKenna, Russell & Fischer, David & Fichtner, Wolf, 2018. "Behavior-oriented modeling of electric vehicle load profiles: A stochastic simulation model considering different household characteristics, charging decisions and locations," Working Paper Series in Production and Energy 29, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    9. Azadfar, Elham & Sreeram, Victor & Harries, David, 2015. "The investigation of the major factors influencing plug-in electric vehicle driving patterns and charging behaviour," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 1065-1076.
    10. Morrissey, Patrick & Weldon, Peter & O’Mahony, Margaret, 2016. "Future standard and fast charging infrastructure planning: An analysis of electric vehicle charging behaviour," Energy Policy, Elsevier, vol. 89(C), pages 257-270.
    11. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    12. Salah, Florian & Ilg, Jens P. & Flath, Christoph M. & Basse, Hauke & Dinther, Clemens van, 2015. "Impact of electric vehicles on distribution substations: A Swiss case study," Applied Energy, Elsevier, vol. 137(C), pages 88-96.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yan, Jie & Zhang, Jing & Liu, Yongqian & Lv, Guoliang & Han, Shuang & Alfonzo, Ian Emmanuel Gonzalez, 2020. "EV charging load simulation and forecasting considering traffic jam and weather to support the integration of renewables and EVs," Renewable Energy, Elsevier, vol. 159(C), pages 623-641.
    2. Xydas, Erotokritos & Marmaras, Charalampos & Cipcigan, Liana M. & Jenkins, Nick & Carroll, Steve & Barker, Myles, 2016. "A data-driven approach for characterising the charging demand of electric vehicles: A UK case study," Applied Energy, Elsevier, vol. 162(C), pages 763-771.
    3. Arias, Mariz B. & Kim, Myungchin & Bae, Sungwoo, 2017. "Prediction of electric vehicle charging-power demand in realistic urban traffic networks," Applied Energy, Elsevier, vol. 195(C), pages 738-753.
    4. Zhang, Jing & Yan, Jie & Liu, Yongqian & Zhang, Haoran & Lv, Guoliang, 2020. "Daily electric vehicle charging load profiles considering demographics of vehicle users," Applied Energy, Elsevier, vol. 274(C).
    5. Ji, Zhenya & Huang, Xueliang, 2018. "Plug-in electric vehicle charging infrastructure deployment of China towards 2020: Policies, methodologies, and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 710-727.
    6. Moon, Sang-Keun & Kim, Jin-O, 2017. "Balanced charging strategies for electric vehicles on power systems," Applied Energy, Elsevier, vol. 189(C), pages 44-54.
    7. Wang, Wanying & Zhang, Qiang & Peng, Zhanglin & Shao, Zhen & Li, Xuefang, 2020. "An empirical evaluation of different usage pattern between car-sharing battery electric vehicles and private ones," Transportation Research Part A: Policy and Practice, Elsevier, vol. 135(C), pages 115-129.
    8. Chung, Yu-Wei & Khaki, Behnam & Li, Tianyi & Chu, Chicheng & Gadh, Rajit, 2019. "Ensemble machine learning-based algorithm for electric vehicle user behavior prediction," Applied Energy, Elsevier, vol. 254(C).
    9. Muhammad Naveed Iqbal & Lauri Kütt & Matti Lehtonen & Robert John Millar & Verner Püvi & Anton Rassõlkin & Galina L. Demidova, 2021. "Travel Activity Based Stochastic Modelling of Load and Charging State of Electric Vehicles," Sustainability, MDPI, vol. 13(3), pages 1-14, February.
    10. Schücking, Maximilian & Jochem, Patrick & Fichtner, Wolf & Wollersheim, Olaf & Stella, Kevin, 2017. "Charging strategies for economic operations of electric vehicles in commercial applications," MPRA Paper 91599, University Library of Munich, Germany.
    11. Shepero, Mahmoud & Munkhammar, Joakim & Widén, Joakim & Bishop, Justin D.K. & Boström, Tobias, 2018. "Modeling of photovoltaic power generation and electric vehicles charging on city-scale: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 61-71.
    12. Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2017. "Electricity costs for a Level 3 electric vehicle fueling station integrated with a building," Applied Energy, Elsevier, vol. 191(C), pages 367-384.
    13. Yang, Zhile & Li, Kang & Foley, Aoife, 2015. "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 396-416.
    14. Flores, Robert J. & Shaffer, Brendan P. & Brouwer, Jacob, 2016. "Electricity costs for an electric vehicle fueling station with Level 3 charging," Applied Energy, Elsevier, vol. 169(C), pages 813-830.
    15. Arias, Mariz B. & Bae, Sungwoo, 2016. "Electric vehicle charging demand forecasting model based on big data technologies," Applied Energy, Elsevier, vol. 183(C), pages 327-339.
    16. Li, Xiaohui & Wang, Zhenpo & Zhang, Lei & Sun, Fengchun & Cui, Dingsong & Hecht, Christopher & Figgener, Jan & Sauer, Dirk Uwe, 2023. "Electric vehicle behavior modeling and applications in vehicle-grid integration: An overview," Energy, Elsevier, vol. 268(C).
    17. Simona Bigerna & Silvia Micheli, 2018. "Attitudes Toward Electric Vehicles: The Case of Perugia Using a Fuzzy Set Analysis," Sustainability, MDPI, vol. 10(11), pages 1-14, November.
    18. Yvenn Amara-Ouali & Yannig Goude & Pascal Massart & Jean-Michel Poggi & Hui Yan, 2021. "A Review of Electric Vehicle Load Open Data and Models," Energies, MDPI, vol. 14(8), pages 1-35, April.
    19. Mingdong Sun & Chunfu Shao & Chengxiang Zhuge & Pinxi Wang & Xiong Yang & Shiqi Wang, 2022. "Uncovering travel and charging patterns of private electric vehicles with trajectory data: evidence and policy implications," Transportation, Springer, vol. 49(5), pages 1409-1439, October.
    20. Natascia Andrenacci & Roberto Ragona & Antonino Genovese, 2020. "Evaluation of the Instantaneous Power Demand of an Electric Charging Station in an Urban Scenario," Energies, MDPI, vol. 13(11), pages 1-19, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:233-234:y:2019:i::p:644-658. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.