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A Novel Open-Source Simulator Of Electric Vehicles in a Demand-Side Management Scenario

Author

Listed:
  • Lucio Ciabattoni

    (Information Engingeering Department, Università Politecnica delle Marche, 60131 Ancona, Italy)

  • Stefano Cardarelli

    (Industrial Engingeering and Mathematical Sciences Department, Università Politecnica delle Marche, 60131 Ancona, Italy)

  • Marialaura Di Somma

    (Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 0010 Roma, Italy)

  • Giorgio Graditi

    (Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 0010 Roma, Italy)

  • Gabriele Comodi

    (Industrial Engingeering and Mathematical Sciences Department, Università Politecnica delle Marche, 60131 Ancona, Italy)

Abstract

Recently, due to the growth of the electric vehicle (EV) market, the investigation of grid-to-vehicle and vehicle-to-grid strategies has become a priority in both the electric mobility and distribution grid research areas. However, there is still a lack of large-scale data sets to test and deploy energy management strategies. In this paper, a fully customizable EV population simulator is presented as an attempt to fill this gap. The proposed tool is designed as a web simulator as well as a Matlab/Simulink block, in order to facilitate its integration in different projects and applications. It provides individual and aggregated charge, discharge and plugin/out event data for a population of EVs, considering both home and public charging stations. The population is generated on the basis of statistical data (which can be fully customized) including commuting distances, vehicle models, traffic and social behavior of the owners. A peak-shaving case study is finally proposed to show the potential of the simulator.

Suggested Citation

  • Lucio Ciabattoni & Stefano Cardarelli & Marialaura Di Somma & Giorgio Graditi & Gabriele Comodi, 2021. "A Novel Open-Source Simulator Of Electric Vehicles in a Demand-Side Management Scenario," Energies, MDPI, vol. 14(6), pages 1-16, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1558-:d:515232
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    References listed on IDEAS

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    Cited by:

    1. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    2. Anam Nadeem & Mosè Rossi & Erica Corradi & Lingkang Jin & Gabriele Comodi & Nadeem Ahmed Sheikh, 2022. "Energy-Environmental Planning of Electric Vehicles (EVs): A Case Study of the National Energy System of Pakistan," Energies, MDPI, vol. 15(9), pages 1-19, April.
    3. Du, Zhili & Zheng, Lirong & Lin, Boqiang, 2024. "Influence of charging stations accessibility on charging stations utilization," Energy, Elsevier, vol. 298(C).

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