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

Xiangqi Zhu; Mingzhi Zhang; Barry Mather; Pranav Kulkarni; Andrew Meintz

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

Xiangqi Zhu, Mingzhi Zhang, Barry Mather, Pranav Kulkarni, Andrew Meintz

National Renewable Energy Laboratory

Research output: Contribution to conference › Paper

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 electrical grid analysis. The geo-spatial 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 distribution system. A case study using 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 station for heavy duty EVs.

Original language	American English
Number of pages	8
State	Published - 2021
Event	2021 IEEE Innovative Smart Grid Technologies - North America (ISGT NA) Duration: 15 Feb 2021 → 18 Feb 2021

Conference

Conference	2021 IEEE Innovative Smart Grid Technologies
City	North America (ISGT NA)
Period	15/02/21 → 18/02/21

Bibliographical note

See NREL/CP-5D00-79800 for paper as published in proceedings

NREL Publication Number

NREL/CP-5D00-77823

Keywords

fast charging stations
GIS
grid analysis
heavy duty EVs
location selection
transportation

Access to Document

https://www.nrel.gov/docs/fy21osti/77823.pdf

Cite this

@conference{36280d42b41b4e7eba7d4a0f841bb996,

title = "Location Selection of Fast-Charging Station for Heavy-Duty EVs Using GIS and Grid Analysis: Preprint",

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 electrical grid analysis. The geo-spatial 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 distribution system. A case study using 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 station for heavy duty EVs.",

keywords = "fast charging stations, GIS, grid analysis, heavy duty EVs, location selection, transportation",

author = "Xiangqi Zhu and Mingzhi Zhang and Barry Mather and Pranav Kulkarni and Andrew Meintz",

note = "See NREL/CP-5D00-79800 for paper as published in proceedings; 2021 IEEE Innovative Smart Grid Technologies ; Conference date: 15-02-2021 Through 18-02-2021",

year = "2021",

language = "American English",

}

TY - CONF

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

AU - Zhu, Xiangqi

AU - Zhang, Mingzhi

AU - Mather, Barry

AU - Kulkarni, Pranav

AU - Meintz, Andrew

N1 - See NREL/CP-5D00-79800 for paper as published in proceedings

PY - 2021

Y1 - 2021

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 electrical grid analysis. The geo-spatial 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 distribution system. A case study using 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 station for heavy duty EVs.

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 electrical grid analysis. The geo-spatial 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 distribution system. A case study using 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 station for heavy duty EVs.

KW - fast charging stations

KW - GIS

KW - grid analysis

KW - heavy duty EVs

KW - location selection

KW - transportation

M3 - Paper

T2 - 2021 IEEE Innovative Smart Grid Technologies

Y2 - 15 February 2021 through 18 February 2021

ER -

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

Abstract

Conference

Bibliographical note

NREL Publication Number

Keywords

Access to Document

Fingerprint

Cite this