Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis

Ziangqi Zhu; Mingzhi Zhang; Barry Mather; Pranav Kulkami; Andrew Meintz

doi:10.1109/ISGT49243.2021.9372170

Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis

Ziangqi Zhu, Mingzhi Zhang, Barry Mather, Pranav Kulkami, Andrew Meintz

National Renewable Energy Laboratory

Research output: Contribution to conference › Paper › peer-review

4 Scopus Citations

Abstract

This work presents a systematic methodology for the location selection of fast-charging stations for heavy-duty electric vehicles (EVs) based on both geospatial and electric grid analysis. The geospatial analysis is based on real-world geographic information system (GIS) data of road networks and existing supportive infrastructures. The grid analysis is implemented based on node-level analysis of potential impacts on voltages and power losses in the distribution system. A case study using a realistic, three-phase, unbalanced distribution feeder from California and extracted real-world GIS data is used to demonstrate the intuitiveness and effectiveness of the proposed methodology for the location selection of fast-charging stations for heavy-duty EVs considering both electric and existing transportation infrastructures.

Original language	American English
Number of pages	5
DOIs	https://doi.org/10.1109/ISGT49243.2021.9372170 https://doi.org/10.1109/ISGT49243.2021.9372170
State	Published - 16 Feb 2021
Event	2021 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2021 - Washington, United States Duration: 16 Feb 2021 → 18 Feb 2021

Conference

Conference	2021 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2021
Country/Territory	United States
City	Washington
Period	16/02/21 → 18/02/21

Bibliographical note

See NREL/CP-5D00-77823 for preprint

NREL Publication Number

NREL/CP-5D00-79800

Keywords

Fast-charging stations
GIS
Grid analysis
Heavy-duty EVs
Location selection
Transportation

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Cite this

Zhu, Z., Zhang, M., Mather, B., Kulkami, P., & Meintz, A. (2021). Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis. Paper presented at 2021 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2021, Washington, United States. https://doi.org/10.1109/ISGT49243.2021.9372170, https://doi.org/10.1109/ISGT49243.2021.9372170

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abstract = "This work presents a systematic methodology for the location selection of fast-charging stations for heavy-duty electric vehicles (EVs) based on both geospatial and electric grid analysis. The geospatial analysis is based on real-world geographic information system (GIS) data of road networks and existing supportive infrastructures. The grid analysis is implemented based on node-level analysis of potential impacts on voltages and power losses in the distribution system. A case study using a realistic, three-phase, unbalanced distribution feeder from California and extracted real-world GIS data is used to demonstrate the intuitiveness and effectiveness of the proposed methodology for the location selection of fast-charging stations for heavy-duty EVs considering both electric and existing transportation infrastructures.",

keywords = "Fast-charging stations, GIS, Grid analysis, Heavy-duty EVs, Location selection, Transportation",

author = "Ziangqi Zhu and Mingzhi Zhang and Barry Mather and Pranav Kulkami and Andrew Meintz",

note = "See NREL/CP-5D00-77823 for preprint; 2021 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2021 ; Conference date: 16-02-2021 Through 18-02-2021",

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Zhu, Z, Zhang, M , Mather, B, Kulkami, P & Meintz, A 2021, 'Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis', Paper presented at 2021 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2021, Washington, United States, 16/02/21 - 18/02/21. https://doi.org/10.1109/ISGT49243.2021.9372170, https://doi.org/10.1109/ISGT49243.2021.9372170

TY - CONF

T1 - Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis

AU - Zhu, Ziangqi

AU - Zhang, Mingzhi

AU - Mather, Barry

AU - Kulkami, Pranav

AU - Meintz, Andrew

N1 - See NREL/CP-5D00-77823 for preprint

PY - 2021/2/16

Y1 - 2021/2/16

N2 - This work presents a systematic methodology for the location selection of fast-charging stations for heavy-duty electric vehicles (EVs) based on both geospatial and electric grid analysis. The geospatial analysis is based on real-world geographic information system (GIS) data of road networks and existing supportive infrastructures. The grid analysis is implemented based on node-level analysis of potential impacts on voltages and power losses in the distribution system. A case study using a realistic, three-phase, unbalanced distribution feeder from California and extracted real-world GIS data is used to demonstrate the intuitiveness and effectiveness of the proposed methodology for the location selection of fast-charging stations for heavy-duty EVs considering both electric and existing transportation infrastructures.

AB - This work presents a systematic methodology for the location selection of fast-charging stations for heavy-duty electric vehicles (EVs) based on both geospatial and electric grid analysis. The geospatial analysis is based on real-world geographic information system (GIS) data of road networks and existing supportive infrastructures. The grid analysis is implemented based on node-level analysis of potential impacts on voltages and power losses in the distribution system. A case study using a realistic, three-phase, unbalanced distribution feeder from California and extracted real-world GIS data is used to demonstrate the intuitiveness and effectiveness of the proposed methodology for the location selection of fast-charging stations for heavy-duty EVs considering both electric and existing transportation infrastructures.

KW - Fast-charging stations

KW - GIS

KW - Grid analysis

KW - Heavy-duty EVs

KW - Location selection

KW - Transportation

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DO - 10.1109/ISGT49243.2021.9372170

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AN - SCOPUS:85103434845

T2 - 2021 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2021

Y2 - 16 February 2021 through 18 February 2021

ER -

Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis

Abstract

Conference

Bibliographical note

NREL Publication Number

Keywords

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