Title: A Stochastic Programming Approach for Electric Vehicle Charging Network Design,
Abstract: Advantages of electric vehicles (EV) 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 arrival and dwell time of vehicles,
battery state of charge of arriving vehicles, walkable range and charging
preferences of drivers, demand during weekdays and weekends, and rate of
adoption of EVs within a community. We conducted studies using sample average
approximation (SAA) method which asymptotically converges to an optimal
solution for a two-stage stochastic problem, however it is computationally
expensive for large-scale instances. Therefore, we developed a heuristic to
produce near to optimal solutions quickly for our data instances. We conducted
computational experiments using various publicly available data sources, and
benefits of the solutions are evaluated both quantitatively and qualitatively
for a given community.