IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i8p2081-d532678.html
   My bibliography  Save this article

Methodology for Estimating the Spatial and Temporal Power Demand of Private Electric Vehicles for an Entire Urban Region Using Open Data

Author

Listed:
  • Florian Straub

    (Chair for Methods of Product Development and Mechatronics, Technical University of Berlin, Strasse des 17. Juni 135, 10623 Berlin, Germany)

  • Simon Streppel

    (Chair for Methods of Product Development and Mechatronics, Technical University of Berlin, Strasse des 17. Juni 135, 10623 Berlin, Germany)

  • Dietmar Göhlich

    (Chair for Methods of Product Development and Mechatronics, Technical University of Berlin, Strasse des 17. Juni 135, 10623 Berlin, Germany)

Abstract

With continuous proliferation of private battery electric vehicles (BEVs) in urban regions, the demand for electrical energy and power is constantly increasing. Electrical grid infrastructure operators are facing the question of where and to what extent they need to expand their infrastructure in order to meet the additional demand. Therefore, the aim of this paper is to develop an activity-based mobility model that supports electrical grid operators in detecting and evaluating possible overloads within the electrical grid, deriving from the aforementioned electrification. We apply our model, which fully relies on open data, to the urban area of Berlin. In addition to a household travel survey, statistics on the population density, the degree of motorisation, and the household income in fine spatial resolution are key data sources for generation of the model. The results show that the spatial distribution of the BEV charging energy demand is highly heterogeneous. The demand per capita is higher in peripheral areas of the city, while the demand per m 2 area is higher in the inner city. For reference areas, we analysed the temporal distribution of the BEV charging power demand, by assuming that the vehicles are solely charged at their residential district. We show that the households’ power demand peak in the evening coincide with the BEV power demand peak while the total power demand can increase up to 77.9%.

Suggested Citation

  • Florian Straub & Simon Streppel & Dietmar Göhlich, 2021. "Methodology for Estimating the Spatial and Temporal Power Demand of Private Electric Vehicles for an Entire Urban Region Using Open Data," Energies, MDPI, vol. 14(8), pages 1-21, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2081-:d:532678
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/8/2081/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/8/2081/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dietmar Göhlich & Kai Nagel & Anne Magdalene Syré & Alexander Grahle & Kai Martins-Turner & Ricardo Ewert & Ricardo Miranda Jahn & Dominic Jefferies, 2021. "Integrated Approach for the Assessment of Strategies for the Decarbonization of Urban Traffic," Sustainability, MDPI, vol. 13(2), pages 1-31, January.
    2. Matteo Muratori, 2018. "Impact of uncoordinated plug-in electric vehicle charging on residential power demand," Nature Energy, Nature, vol. 3(3), pages 193-201, March.
    3. Daina, Nicolò & Sivakumar, Aruna & Polak, John W., 2017. "Modelling electric vehicles use: a survey on the methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 447-460.
    4. Yong, Jia Ying & Ramachandaramurthy, Vigna K. & Tan, Kang Miao & Mithulananthan, N., 2015. "A review on the state-of-the-art technologies of electric vehicle, its impacts and prospects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 365-385.
    5. Green II, Robert C. & Wang, Lingfeng & Alam, Mansoor, 2011. "The impact of plug-in hybrid electric vehicles on distribution networks: A review and outlook," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 544-553, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Antonello Cammarano & Vincenzo Varriale & Francesca Michelino & Mauro Caputo, 2022. "Open and Crowd-Based Platforms: Impact on Organizational and Market Performance," Sustainability, MDPI, vol. 14(4), pages 1-26, February.
    2. Ana Carolina Kulik & Édwin Augusto Tonolo & Alberto Kisner Scortegagna & Jardel Eugênio da Silva & Jair Urbanetz Junior, 2021. "Analysis of Scenarios for the Insertion of Electric Vehicles in Conjunction with a Solar Carport in the City of Curitiba, Paraná—Brazil," Energies, MDPI, vol. 14(16), pages 1-15, August.
    3. Florian Straub & Otto Maier & Dietmar Göhlich, 2021. "Car-Access Attractiveness of Urban Districts Regarding Shopping and Working Trips for Usage in E-Mobility Traffic Simulations," Sustainability, MDPI, vol. 13(20), pages 1-29, October.
    4. Jiang, Qinhua & Zhang, Ning & Yueshuai He, Brian & Lee, Changju & Ma, Jiaqi, 2024. "Large-scale public charging demand prediction with a scenario- and activity-based approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).

    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. Graham Town & Seyedfoad Taghizadeh & Sara Deilami, 2022. "Review of Fast Charging for Electrified Transport: Demand, Technology, Systems, and Planning," Energies, MDPI, vol. 15(4), pages 1-30, February.
    2. Das, H.S. & Rahman, M.M. & Li, S. & Tan, C.W., 2020. "Electric vehicles standards, charging infrastructure, and impact on grid integration: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
    3. Rodrigues, João L. & Bolognesi, Hugo M. & Melo, Joel D. & Heymann, Fabian & Soares, F.J., 2019. "Spatiotemporal model for estimating electric vehicles adopters," Energy, Elsevier, vol. 183(C), pages 788-802.
    4. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    5. Yi, Tao & Cheng, Xiaobin & Peng, Peng, 2022. "Two-stage optimal allocation of charging stations based on spatiotemporal complementarity and demand response: A framework based on MCS and DBPSO," Energy, Elsevier, vol. 239(PC).
    6. 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).
    7. Fuad Un-Noor & Sanjeevikumar Padmanaban & Lucian Mihet-Popa & Mohammad Nurunnabi Mollah & Eklas Hossain, 2017. "A Comprehensive Study of Key Electric Vehicle (EV) Components, Technologies, Challenges, Impacts, and Future Direction of Development," Energies, MDPI, vol. 10(8), pages 1-84, August.
    8. Lin, Haiyang & Fu, Kun & Wang, Yu & Sun, Qie & Li, Hailong & Hu, Yukun & Sun, Bo & Wennersten, Ronald, 2019. "Characteristics of electric vehicle charging demand at multiple types of location - Application of an agent-based trip chain model," Energy, Elsevier, vol. 188(C).
    9. Barone, G. & Buonomano, A. & Calise, F. & Forzano, C. & Palombo, A., 2019. "Building to vehicle to building concept toward a novel zero energy paradigm: Modelling and case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 625-648.
    10. Jiang, Qinhua & Zhang, Ning & Yueshuai He, Brian & Lee, Changju & Ma, Jiaqi, 2024. "Large-scale public charging demand prediction with a scenario- and activity-based approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 179(C).
    11. Gaizka Saldaña & Jose Ignacio San Martin & Inmaculada Zamora & Francisco Javier Asensio & Oier Oñederra, 2019. "Electric Vehicle into the Grid: Charging Methodologies Aimed at Providing Ancillary Services Considering Battery Degradation," Energies, MDPI, vol. 12(12), pages 1-37, June.
    12. Yuping Lin & Kai Zhang & Zuo-Jun Max Shen & Lixin Miao, 2019. "Charging Network Planning for Electric Bus Cities: A Case Study of Shenzhen, China," Sustainability, MDPI, vol. 11(17), pages 1-27, August.
    13. Lauvergne, Rémi & Perez, Yannick & Françon, Mathilde & Tejeda De La Cruz, Alberto, 2022. "Integration of electric vehicles into transmission grids: A case study on generation adequacy in Europe in 2040," Applied Energy, Elsevier, vol. 326(C).
    14. Wangsness, Paal Brevik & Proost, Stef & Rødseth, Kenneth Løvold, 2021. "Optimal policies for electromobility: Joint assessment of transport and electricity distribution costs in Norway," Utilities Policy, Elsevier, vol. 72(C).
    15. Ali Saadon Al-Ogaili & Ali Q. Al-Shetwi & Thanikanti Sudhakar Babu & Yap Hoon & Majid A. Abdullah & Ameer Alhasan & Ammar Al-Sharaa, 2021. "Electric Buses in Malaysia: Policies, Innovations, Technologies and Life Cycle Evaluations," Sustainability, MDPI, vol. 13(21), pages 1-22, October.
    16. Mahmoudzadeh Andwari, Amin & Pesiridis, Apostolos & Rajoo, Srithar & Martinez-Botas, Ricardo & Esfahanian, Vahid, 2017. "A review of Battery Electric Vehicle technology and readiness levels," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 414-430.
    17. Ahmed Abdalrahman & Weihua Zhuang, 2017. "A Survey on PEV Charging Infrastructure: Impact Assessment and Planning," Energies, MDPI, vol. 10(10), pages 1-25, October.
    18. Mahmud, Khizir & Town, Graham E. & Morsalin, Sayidul & Hossain, M.J., 2018. "Integration of electric vehicles and management in the internet of energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 4179-4203.
    19. Einolander, Johannes & Lahdelma, Risto, 2022. "Multivariate copula procedure for electric vehicle charging event simulation," Energy, Elsevier, vol. 238(PA).
    20. Hu, Junjie & Morais, Hugo & Sousa, Tiago & Lind, Morten, 2016. "Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1207-1226.

    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:gam:jeners:v:14:y:2021:i:8:p:2081-:d:532678. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.