Efficient Electric Vehicle Charging Allocation: A Two-Stage Optimization and Participation Analysis

TL;DR

This paper introduces a two-stage optimization framework for EV charging allocation that reduces congestion and improves station utilization while considering user participation and network effects.

Contribution

It presents a novel two-stage allocation method with a closed-form charging computation and a participation model for EV charging networks.

Findings

Significant reduction in worst-case congestion.

Limited impact on average utility.

Scalable patterns as station numbers grow.

Abstract

Electric vehicles (EVs) require substantially longer refueling times than gasoline vehicles, which can generate severe congestion at charging stations when demand concentrates. We propose a two-stage allocation framework for EV charging networks. In Stage 1, a central coordinator determines station-level admission quotas to control worst-station delay using a queue-informed congestion metric. In Stage 2, given these quotas and feasibility constraints (e.g., reachability), the coordinator solves a utility-maximizing capacitated assignment to allocate EVs across stations. To keep Stage~2 tractable while capturing heterogeneous charging needs, we precompute each EV-station pair's optimal charging amount in closed form under a battery-capacity constraint and then solve a transportation/assignment problem. Finally, we introduce a reduced-form participation model to characterize adoption thresholds under network benefits, spillovers, and coordination costs. Numerical experiments illustrate substantial reductions in worst-case congestion with limited impact on average utility, and highlight scaling patterns as the number of stations increases.