Grid-Aware Robust Fast-charging Station Deployment
Grid-Aware Robust Fast-Charging Station Deployment for Electric Buses Under Socioeconomic Considerations
Principal Investigator: Chaoyue Zhao, Professor, Department of Industrial & Systems Engineering
Co-Principal Investigator: Cynthia Chen, Professor, Department of Civil & Environmental Engineering
Project Duration: 12 months
Project Budget (Federal UTC Funds): $94,361
Project Budget (Cost-share): $47,181
Institution: University of Washington
Abstract
The fast-charging technology enables electric buses to be quickly recharged during trips so that on-route operations can be maintained with modest battery capacity. Where to place the fast-charging stations and how to ensure the availability, efficacy, and efficiency of charging infrastructure become very important issues and have been studied in recent years; however, most of existing literature of this research only focus on the transit system itself and aim to reduce the costs. There are still several critical issues remain to be unsolved for the deployment of electric bus fast-charging stations. The major objective of this project is to develop a comprehensive optimization model to select the optimal location of fast-charging stations of electric buses. It aims at addressing several conceptual and methodological complexities inherited in the interconnected transportation-electricity infrastructure systems and the socioeconomic considerations for installing new charging stations to disadvantaged communities. In order to make the location selection decisions reliable, the optimization will also take into account a variety of uncertainties, such as traffic, local grid conditions, and energy usage between stops. A robust optimization methodology is adapted to guarantee that the selected locations are trustworthy enough to handle any real case scenario of uncertainty without violation of constraints. Benders’ decomposition approach is utilized to effectively solve the resulting two-stage robust optimization problem in an iterative way.