EV Infrastructure Planning and Grid Impact Assessment: A Case for Mexico

Alexandra von Meier

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2018-74
May 18, 2018

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-74.pdf

In much of the world, electric vehicle adoption is increasing. This is a welcome change for many of these countries, where gasoline-powered vehicles contribute to high levels of ozone and fine particulate matter (PM2.5), which results in tens of thousands of deaths a year.

City planners can use this document to formulate plans regarding where to build charging stations to meet new demand, how much infrastructure to build at each location, and how to plan new electrical infrastructure to meet such demand. Herein, we provide a framework to address the aforementioned issues.

Projections on EV sampling and the input data are used to construct Monte Carlo based models on where these cars will be by the hour. It shows how to obtain the locations and sizes of these stations using clustering algorithms and convex optimization. Load profiles are generated given statistics regarding home and work charging, in addition to the newly found information about public charging. Guadalajara was used as a case study for this model due to the fact that it is one of the foremost technology hubs in Latin America and that there was data provided about the city through the Instituto Nacional de Ecología y Cambio Climático (INECC).


BibTeX citation:

@techreport{von Meier:EECS-2018-74,
    Author = {von Meier, Alexandra},
    Title = {EV Infrastructure Planning and Grid Impact Assessment: A Case for Mexico},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2018},
    Month = {May},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-74.html},
    Number = {UCB/EECS-2018-74},
    Abstract = {In much of the world, electric vehicle adoption is increasing. This is a welcome change for many of these countries, where gasoline-powered vehicles contribute to high levels of ozone and fine particulate matter (PM2.5), which results in tens of thousands of deaths a year.

City planners can use this document to formulate plans regarding where to build charging stations to meet new demand, how much infrastructure to build at each location, and how to plan new electrical infrastructure to meet such demand. Herein, we provide a framework to address the aforementioned issues.

Projections on EV sampling and the input data are used to construct Monte Carlo based models on where these cars will be by the hour. It shows how to obtain the locations and sizes of these stations using clustering algorithms and convex optimization. Load profiles are generated given statistics regarding home and work charging, in addition to the newly found information about public charging. Guadalajara was used as a case study for this model due to the fact that it is one of the foremost technology hubs in Latin America and that there was data provided about the city through the Instituto Nacional de Ecología y Cambio Climático (INECC).}
}

EndNote citation:

%0 Report
%A von Meier, Alexandra
%T EV Infrastructure Planning and Grid Impact Assessment: A Case for Mexico
%I EECS Department, University of California, Berkeley
%D 2018
%8 May 18
%@ UCB/EECS-2018-74
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2018/EECS-2018-74.html
%F von Meier:EECS-2018-74