Traffic-Aware Microgrid Planning for Dynamic Wireless Electric Vehicle Charging Roadways
This work addresses infrastructure planning for electric vehicle charging, offering a solution for power system operators, but it is incremental as it builds on existing traffic and power flow models.
The paper tackles the challenge of planning microgrids for dynamic wireless charging (DWC) roadways by accounting for traffic patterns, and it shows that this approach reduces system costs compared to worst-case traffic-based methods, with demonstrated performance on a California highway segment.
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.