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Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis

Author

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  • Alexandre Lucas

    (European Commission, JRC, Directorate C Energy, Transport and Climate, PO Box 2, 1755 ZG Petten, The Netherlands)

  • Giuseppe Prettico

    (European Commission, JRC, Directorate C Energy, Transport and Climate, PO Box 2, 1755 ZG Petten, The Netherlands)

  • Marco Giacomo Flammini

    (European Commission, JRC, Directorate C Energy, Transport and Climate, PO Box 2, 1755 ZG Petten, The Netherlands)

  • Evangelos Kotsakis

    (European Commission, JRC, Directorate C Energy, Transport and Climate, PO Box 2, 1755 ZG Petten, The Netherlands)

  • Gianluca Fulli

    (European Commission, JRC, Directorate C Energy, Transport and Climate, PO Box 2, 1755 ZG Petten, The Netherlands)

  • Marcelo Masera

    (European Commission, JRC, Directorate C Energy, Transport and Climate, PO Box 2, 1755 ZG Petten, The Netherlands)

Abstract

Electric vehicle (EV) charging infrastructure rollout is well under way in several power systems, namely North America, Japan, Europe, and China. In order to support EV charging infrastructures design and operation, little attempt has been made to develop indicator-based methods characterising such networks across different regions. This study defines an assessment methodology, composed by eight indicators, allowing a comparison among EV public charging infrastructures. The proposed indicators capture the following: energy demand from EVs, energy use intensity, charger’s intensity distribution, the use time ratios, energy use ratios, the nearest neighbour distance between chargers and availability, the total service ratio, and the carbon intensity as an environmental impact indicator. We apply the methodology to a dataset from ElaadNL, a reference smart charging provider in The Netherlands, using open source geographic information system (GIS) and R software. The dataset reveals higher energy intensity in six urban areas and that 50% of energy supplied comes from 19.6% of chargers. Correlations of spatial density are strong and nearest neighbouring distances range from 1101 to 9462 m. Use time and energy use ratios are 11.21% and 3.56%. The average carbon intensity is 4.44 gCO 2eq /MJ. Finally, the indicators are used to assess the impact of relevant public policies on the EV charging infrastructure use and roll-out.

Suggested Citation

  • Alexandre Lucas & Giuseppe Prettico & Marco Giacomo Flammini & Evangelos Kotsakis & Gianluca Fulli & Marcelo Masera, 2018. "Indicator-Based Methodology for Assessing EV Charging Infrastructure Using Exploratory Data Analysis," Energies, MDPI, vol. 11(7), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1869-:d:158556
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    References listed on IDEAS

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    2. Dimitrios Rizopoulos & Domokos Esztergár-Kiss, 2020. "A Method for the Optimization of Daily Activity Chains Including Electric Vehicles," Energies, MDPI, vol. 13(4), pages 1-21, February.
    3. Alexandre Lucas & Ricardo Barranco & Nazir Refa, 2019. "EV Idle Time Estimation on Charging Infrastructure, Comparing Supervised Machine Learning Regressions," Energies, MDPI, vol. 12(2), pages 1-17, January.
    4. Einolander, Johannes & Lahdelma, Risto, 2022. "Multivariate copula procedure for electric vehicle charging event simulation," Energy, Elsevier, vol. 238(PA).
    5. Anastasios Tsakalidis & Andreea Julea & Christian Thiel, 2019. "The Role of Infrastructure for Electric Passenger Car Uptake in Europe," Energies, MDPI, vol. 12(22), pages 1-18, November.
    6. Cláudia A. Soares Machado & Harmi Takiya & Charles Lincoln Kenji Yamamura & José Alberto Quintanilha & Fernando Tobal Berssaneti, 2020. "Placement of Infrastructure for Urban Electromobility: A Sustainable Approach," Sustainability, MDPI, vol. 12(16), pages 1-18, August.
    7. Milan Straka & Pasquale De Falco & Gabriella Ferruzzi & Daniela Proto & Gijs van der Poel & Shahab Khormali & v{L}ubov{s} Buzna, 2019. "Predicting popularity of EV charging infrastructure from GIS data," Papers 1910.02498, arXiv.org.
    8. Christian Thiel & Andreea Julea & Beatriz Acosta Iborra & Nerea De Miguel Echevarria & Emanuela Peduzzi & Enrico Pisoni & Jonatan J. Gómez Vilchez & Jette Krause, 2019. "Assessing the Impacts of Electric Vehicle Recharging Infrastructure Deployment Efforts in the European Union," Energies, MDPI, vol. 12(12), pages 1-23, June.
    9. Armin Razmjoo & Meysam Majidi Nezhad & Lisa Gakenia Kaigutha & Mousa Marzband & Seyedali Mirjalili & Mehdi Pazhoohesh & Saim Memon & Mehdi A. Ehyaei & Giuseppe Piras, 2021. "Investigating Smart City Development Based on Green Buildings, Electrical Vehicles and Feasible Indicators," Sustainability, MDPI, vol. 13(14), pages 1-14, July.
    10. Mona Kabus & Lars Nolting & Benedict J. Mortimer & Jan C. Koj & Wilhelm Kuckshinrichs & Rik W. De Doncker & Aaron Praktiknjo, 2020. "Environmental Impacts of Charging Concepts for Battery Electric Vehicles: A Comparison of On-Board and Off-Board Charging Systems Based on a Life Cycle Assessment," Energies, MDPI, vol. 13(24), pages 1-31, December.

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