A Two-Stage Stochastic Optimization Model for the Equitable Deployment of Fixed and Mobile Electric Vehicle Charging Stations

TL;DR

This paper introduces a two-stage stochastic optimization model for deploying fixed and mobile EV charging stations to improve coverage, equity, and responsiveness to demand variability, aiding policymakers in infrastructure planning.

Contribution

It presents a novel two-stage stochastic mixed-integer programming model incorporating an adapted Edge Scanning Algorithm for optimal EV charging station deployment.

Findings

Enhanced coverage and system resilience demonstrated in numerical experiments.

Reduced unmet demand through demand-responsive station allocation.

Effective integration of existing stations to avoid redundancy.

Abstract

A major barrier to wide adoption of Electric Vehicles (EVs) is the absence of reliable and equitable charging infrastructure. Poorly located charging stations create coverage gaps and slow down EV adoption, especially in underserved communities. This paper proposes a two-stage stochastic mixed-integer programming model for the optimal deployment of Fixed and Mobile Charging Stations (FCSs and MCSs) across multiple zones and periods. Initially, a finite dominating set of candidate locations is identified using the Edge Scanning Algorithm for a Single Refueling Station (ESS), an exact continuous-location method. We modify the ESS algorithm to incorporate existing public charging stations, thereby avoiding redundant coverage. In the first stage of our model, FCSs are allocated based on long-term traffic patterns, budgetary constraints, and socioeconomic factors to ensure stable baseline coverage. The second stage dynamically assigns MCSs in response to short-term demand fluctuations and uncertainties, aiming to minimize relocation costs while maximizing coverage. We use a scenario-based framework to capture demand variability. Numerical experiments on realistic networks demonstrate the model's capacity to enhance system resilience and reduce unmet demand. These findings offer practical insights for planners and policymakers seeking to develop accessible and demand-responsive EV charging infrastructure.