A Stochastic Programming Approach for Electric Vehicle Charging Network Design

The advantages of electric vehicles (EVs) include reduction of greenhouse gas and other emissions, energy security, and fuel economy. The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations. We propose a two-stage s...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems Vol. 20; no. 5; pp. 1870 - 1882
Main Authors: Faridimehr, Sina, Venkatachalam, Saravanan, Chinnam, Ratna Babu
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Batteries
charging network
Charging stations
Communities
Drivers
Dwell time
electric vehicle
Electric vehicle charging
Electric vehicles
Fuel economy
Greenhouse effect
Greenhouse gases
Mathematical programming
Network design
Programming
sample average approximation
Stations
Stochastic processes
Stochastic programming
Two-stage stochastic programming
Uncertainty
ISSN:1524-9050, 1558-0016
Online Access:Get full text
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Summary:The advantages of electric vehicles (EVs) include reduction of greenhouse gas and other emissions, energy security, and fuel economy. The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations. We propose a two-stage stochastic programming model to determine the optimal network of charging stations for a community, considering uncertainties in the arrival and dwell times of vehicles, the state of charge of arriving vehicles' batteries, drivers' walking ranges and charging preferences, demand during weekdays and weekends, and the community's rate of EV adoption. We conducted studies using the sample average approximation method, which asymptotically converges to an optimal solution for a two-stage stochastic problem. However, this method is computationally expensive for large-scale instances. Therefore, we also developed a heuristic to produce nearly optimal solutions quickly for our data instances. We conducted computational experiments using various publicly available data sources and evaluated the benefits of the solutions for a given community, both quantitatively and qualitatively.
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ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2018.2841391