Sequential Charging Station Location Optimization under Uncertain Charging Behavior and User Growth

TL;DR

This paper develops stochastic programming models for sequential electric vehicle charging station placement, accounting for uncertain demand growth and user behavior, and demonstrates the benefits of multi-stage planning under demand variability.

Contribution

It introduces integrated stochastic models that incorporate user choice and demand uncertainty into sequential charging station location planning, with improved computational methods.

Findings

Multi-stage models outperform single-stage in high demand fluctuation scenarios.

Demand dispersion and user sensitivity significantly impact optimal station placement.

Second-order conic reformulation accelerates solution computation.

Abstract

Charging station availability is crucial for a thriving electric vehicle market. Due to budget constraints, locating these stations usually proceeds in phases, which calls for careful consideration of the (random) charging demand growth throughout the planning horizon. This paper integrates user choice behavior into two-stage and multi-stage stochastic programming models for intracity charging station planning under demand uncertainty. We derive a second-order conic representation for the nonlinear, nonconvex formulation by taking advantage of the binary nature of location variables and propose subgradient inequalities to accelerate computation. Numerical results demonstrate the value of employing multi-stage models, particularly in scenarios of high demand fluctuations, increased demand dispersion, and high user sensitivity to the distance-to-recharge.