Integer-Clustering Optimization and Energy Hub Co-Design of Hydrogen and Battery EV Fleets

The adoption of electric vehicles (EVs) results in a stronger integration between energy and transportation systems. In this work, we develop scalable optimization models to co-design heterogenous hydrogen and battery EV fleets and the supporting energy infrastructure (e.g., distributed energy resources, and fast-charging and hydrogen-fueling station) to efficiently meet transportation demands. A novel integer-clustering formulation is used to model large-scale EV operation at the fleet level while maintaining accurate individual vehicle dispatch, which significantly improves the computation efficiency with guaranteed performance relative to the true individual vehicle formulation. Furthermore, we extend the integer-clustering formulation to capture EV battery degradation by providing an exact closed-form reformulation of the nonlinear and iterative rainflow algorithm for cycle counting, which retains the MILP form. We apply the optimization model to a real-world case study of the public bus network in Boston using real trip schedule data and technoeconomic 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.