Traffic-Aware Microgrid Planning for Dynamic Wireless Electric Vehicle Charging Roadways

TL;DR

This paper introduces a traffic-aware microgrid planning framework for dynamic wireless EV charging that integrates traffic patterns with energy demand to optimize infrastructure and reduce costs.

Contribution

It presents a novel method combining traffic modeling with power system optimization for efficient DWC microgrid planning.

Findings

Traffic-aware planning reduces system costs compared to worst-case approaches.

The model effectively captures spatio-temporal EV charging demand based on traffic data.

Performance demonstrated on a real highway segment under various traffic conditions.

Abstract

Dynamic wireless charging (DWC) is an emerging technology that has the potential to reduce charging downtime and on-board battery size, particularly in heavy-duty electric vehicles (EVs). However, its spatiotemporal, dynamic, high-power demands pose challenges for power system operations. Since DWC demand depends on traffic characteristics such as speed, density, and dwell time, effective infrastructure planning must account for the coupling between traffic behavior and EV energy consumption. In this paper, we propose a novel traffic-aware microgrid planning framework for DWC. First, we use the macroscopic cell transmission model to estimate spatio-temporal EV charging demand along DWC corridors and integrate this demand into an AC optimal power flow formulation to design a supporting microgrid. Our framework explicitly links traffic patterns with energy demand and demonstrates that traffic-aware microgrid planning yields significantly lower system costs than worst-case traffic-based approaches. We demonstrate the performance of our model on a segment of I-210W in California under a wide range of traffic conditions.