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Determination of Electricity Demand by Personal Light Electric Vehicles (PLEVs): An Example of e-Motor Scooters in the Context of Large City Management in Poland

Author

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  • Anna Brdulak

    (Faculty of Computer Science and Management, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

  • Grażyna Chaberek

    (Faculty of Oceanography and Geography, University of Gdańsk, 80-309 Gdynia, Poland)

  • Jacek Jagodziński

    (Faculty of Electronics, Wrocław University of Science and Technology, 50-370 Wrocław, Poland)

Abstract

Personal light electric vehicles (PLEVs) are a phenomenon that can currently be observed in cities, intended to be an ecological form of transport. The authors of the paper make an attempt to determine electricity consumption by PLEVs in the context of managing a large city in accordance with the concept of sustainable development. The article is of a cognitive nature. Research questions posed against the background of the goal formulated are as follows: how strong will the demand for PLEVs be (in the example of e-motor scooters, taking into consideration the number of vehicles) and for the electricity consumed by PLEVs. The method used is a simulation model. The conducted analyses demonstrate that a dynamic growth of PLEVs will result in an increased energy demand, which must be taken into account by the cities, developing according to the sustainable development conception.

Suggested Citation

  • Anna Brdulak & Grażyna Chaberek & Jacek Jagodziński, 2020. "Determination of Electricity Demand by Personal Light Electric Vehicles (PLEVs): An Example of e-Motor Scooters in the Context of Large City Management in Poland," Energies, MDPI, vol. 13(1), pages 1-18, January.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:194-:d:304073
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    References listed on IDEAS

    as
    1. Seebauer, Sebastian, 2015. "Why early adopters engage in interpersonal diffusion of technological innovations: An empirical study on electric bicycles and electric scooters," Transportation Research Part A: Policy and Practice, Elsevier, vol. 78(C), pages 146-160.
    2. Walker, Paul D. & Roser, Holger M., 2015. "Energy consumption and cost analysis of hybrid electric powertrain configurations for two wheelers," Applied Energy, Elsevier, vol. 146(C), pages 279-287.
    3. Allegrini, Jonas & Orehounig, Kristina & Mavromatidis, Georgios & Ruesch, Florian & Dorer, Viktor & Evins, Ralph, 2015. "A review of modelling approaches and tools for the simulation of district-scale energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1391-1404.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. Derek W. Bunn & Angelica Gianfreda & Stefan Kermer, 2018. "A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market," Energies, MDPI, vol. 11(10), pages 1-13, October.
    6. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting: Part II — Probabilistic forecasting," Energy Economics, Elsevier, vol. 79(C), pages 171-182.
    7. Bosetti, Valentina & Longden, Thomas, 2013. "Light duty vehicle transportation and global climate policy: The importance of electric drive vehicles," Energy Policy, Elsevier, vol. 58(C), pages 209-219.
    8. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1520-1532.
    9. Flah Aymen & Chokri Mahmoudi, 2019. "A Novel Energy Optimization Approach for Electrical Vehicles in a Smart City," Energies, MDPI, vol. 12(5), pages 1-22, March.
    10. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    11. Bruno Canizes & João Soares & Angelo Costa & Tiago Pinto & Fernando Lezama & Paulo Novais & Zita Vale, 2019. "Electric Vehicles’ User Charging Behaviour Simulator for a Smart City," Energies, MDPI, vol. 12(8), pages 1-20, April.
    12. Manfren, Massimiliano & Caputo, Paola & Costa, Gaia, 2011. "Paradigm shift in urban energy systems through distributed generation: Methods and models," Applied Energy, Elsevier, vol. 88(4), pages 1032-1048, April.
    13. Arkadiusz Kijek & Tomasz Kijek, 2010. "Modelling of innovation diffusion," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 20(3-4), pages 53-68.
    14. Hwang, Jenn Jiang, 2010. "Promotional policy for renewable energy development in Taiwan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 1079-1087, April.
    15. Esther Salmeron-Manzano & Francisco Manzano-Agugliaro, 2018. "The Electric Bicycle: Worldwide Research Trends," Energies, MDPI, vol. 11(7), pages 1-16, July.
    16. Massiani, Jérôme & Gohs, Andreas, 2015. "The choice of Bass model coefficients to forecast diffusion for innovative products: An empirical investigation for new automotive technologies," Research in Transportation Economics, Elsevier, vol. 50(C), pages 17-28.
    17. Hwang, Jenn Jiang, 2010. "Sustainable transport strategy for promoting zero-emission electric scooters in Taiwan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(5), pages 1390-1399, June.
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    Cited by:

    1. Paweł Piotrowski & Dariusz Baczyński & Marcin Kopyt, 2022. "Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development," Energies, MDPI, vol. 15(15), pages 1-27, August.
    2. Anna Brdulak & Grażyna Chaberek & Jacek Jagodziński, 2020. "Development Forecasts for the Zero-Emission Bus Fleet in Servicing Public Transport in Chosen EU Member Countries," Energies, MDPI, vol. 13(16), pages 1-20, August.
    3. Andrea Carloni & Federico Baronti & Roberto Di Rienzo & Roberto Roncella & Roberto Saletti, 2021. "An Open-Hardware and Low-Cost Maintenance Tool for Light-Electric-Vehicle Batteries," Energies, MDPI, vol. 14(16), pages 1-10, August.
    4. Anna Brdulak & Grażyna Chaberek & Jacek Jagodziński, 2021. "BASS Model Analysis in “Crossing the Chasm” in E-Cars Innovation Diffusion Scenarios," Energies, MDPI, vol. 14(11), pages 1-16, May.
    5. Ann Kathrin Stinder & Nora Schelte & Semih Severengiz, 2022. "Application of Mixed Methods in Transdisciplinary Research Projects on Sustainable Mobility," Sustainability, MDPI, vol. 14(11), pages 1-25, June.

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