# A Stochastic Programming Approach for Electric Vehicle Charging Network Design

## TL;DR

This paper presents a stochastic programming model for designing EV charging networks that accounts for uncertainties in user behavior and adoption rates, offering both exact and heuristic solutions evaluated through computational experiments.

## Contribution

Introduces a two-stage stochastic programming framework for EV charging station placement, including a heuristic for large-scale problems and empirical validation with real data.

## Key findings

- Heuristic provides near-optimal solutions efficiently.
- Model effectively captures uncertainties in EV charging demand.
- Solutions improve EV infrastructure planning for communities.

## 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.