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

Alexandre Lucas, Giuseppe Prettico, Marco Giacomo Flammini, Evangelos Kotsakis, Gianluca Fulli and Marcelo Masera
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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

Energies, 2018, vol. 11, issue 7, 1-18

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.

Keywords: charging infrastructure; electric vehicles; service ratio; network design; exploratory data analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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