Title
Estimation of Electric Demand from Electric Vehicles Using Spatial Regressions
Date Issued
01 September 2019
Access level
metadata only access
Resource Type
conference paper
Author(s)
Universidad Federal del ABC
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
Abstract
The acquisition of electric vehicles depends on socioeconomic factors and does not occur homogeneously in the different zones of urban areas for the early years of the electric vehicles penetrations. The concentration of these vehicles can be found by spatial regressions that correlate statically the electric vehicles rate by subarea with the socioeconomic factors of their neighboring regions. Such correlation allows characterizing the influence of the inhabitants in neighboring regions to the purchase of electric vehicles. Therefore, this work aims to show how spatial regressions can provide useful information to determine the load growth by the electric vehicles recharging. To exemplify the information quality, provide from such regression classes, the application of two regressions is performed for a medium-sized city in Brazil in order to determine the best location of charging stations for electric vehicles and the maximum diversified demand in each subarea.
Language
English
OCDE Knowledge area
Ingeniería eléctrica, Ingeniería electrónica
Economía
Ingeniería del transporte
Subjects
Scopus EID
2-s2.0-85075721441
Resource of which it is part
2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019
ISBN of the container
9781538695678
Conference
2019 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT Latin America 2019 Gramado 15 September 2019 through 18 September 2019
Sponsor(s)
This study was financed in part by the INCT-INERGE; by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001; by São Paulo Research Foundation (FAPESP) under grants: 2015/21972-6, 2017/01909-3, 2017/22577-9, 2019/00466-6 and by CNPq under grants: 307281/2016-7, 422044/2018-0.
Sources of information:
Directorio de Producción Científica
Scopus