Integer-Clustering Optimization of Hydrogen and Battery EV Fleets Considering DERs

TL;DR

This paper presents a scalable optimization model for co-designing hydrogen and battery EV fleets with energy resources and infrastructure, providing insights into future integrated energy-transportation-hydrogen systems using real data.

Contribution

It introduces a novel integer-clustering optimization approach for fleet dispatch that enhances computational efficiency while maintaining accuracy.

Findings

Improved computational efficiency in fleet optimization.

Insights into the impact of electricity prices and hydrogen costs.

Analysis of system upgrades and emission constraints.

Abstract

Electrified transportation leads to a tighter integration between transportation and energy distribution systems. In this work, we develop scalable optimization models to co-design hydrogen and battery electric vehicle (EV) fleets, distributed energy resources, and fast-charging and hydrogen-fueling infrastructure to efficiently meet transportation demands. A novel integer-clustering formulation is used for optimizing fleet-level EV operation while maintaining accurate individual vehicle dispatch, which significantly improves the computation efficiency with guaranteed performance. We apply the optimization model to Boston's public transit bus network using real geospatial data and cost parameters. Realistic insights are provided into the future evolution of coupled electricity-transportation-hydrogen systems, including the effects of electricity price structure, hydrogen fuel cost, carbon emission constraint, temperature effects on EV range, and distribution system upgrade cost.