Optimizing Urban Electric Vehicle Charging and Battery Swapping Infrastructure: A Location-Inventory-Grid Model

TL;DR

This paper presents an integrated model for optimizing urban EV charging and battery swapping infrastructure, balancing cost, grid stability, and environmental sustainability under uncertainty.

Contribution

It introduces a novel location-inventory-grid model with a continuous approximation approach to address complex joint optimization problems.

Findings

Centralized charging with frequency regulation reduces costs.

Centralized charging enhances grid stability.

Operational flexibility may be constrained near optimal solutions.

Abstract

The rapid rise of electric vehicles (EVs) places unprecedented stress on both urban mobility systems and low-voltage power grids. Designing battery swapping and charging networks that are cost-efficient, grid-compatible, and sustainable is therefore a pressing yet complex challenge: service providers must jointly optimize station locations, battery inventory, and grid interaction under high-dimensional uncertainty. We develop an integrated location-inventory-grid model and employ a continuous approximation approach to overcome the intractability of discrete formulations. Our analysis compares centralized versus decentralized charging, with and without participation in frequency regulation. The results reveal that centralized charging, when combined with frequency regulation, not only reduces cost but also strengthens grid stability. However, it may constrain operational flexibility near the optimum, potentially challenging efforts to mitigate environmental impacts by lowering battery inventories. These results offer actionable guidance for cost-efficient, environmentally sustainable, and grid-compatible scaling of urban EV infrastructure to meet the demands of large-scale EV adoption.