Optimizing infrastructure and charging strategies for bidirectional electric delivery fleets under PV, grid, and cost constraints

The electrification of commercial delivery fleets introduces complex trade-offs between infrastructure investment, operational flexibility, and energy cost efficiency. This study presents a strategic optimization model designed to support long-term planning decisions for electric vehicle (EV) fleets and corresponding charging infrastructure. The model focuses on minimizing the Total Cost of Ownership (TCO) by jointly optimizing the sizing of photovoltaic (PV) systems, stationary battery storage, vehicle batteries, and grid connection capacity. It also incorporates fast and bidirectional charging capabilities to enhance system flexibility and reduce grid dependency. Formulated as a Mixed-Integer Linear Programming (MILP) problem, the model evaluates a wide range of infrastructure configurations under realistic boundary conditions, using one-hour time steps. Key input data include high-resolution, year-long PV generation profiles, dynamic electricity tariffs, vehicle energy consumption patterns, and operational schedules derived from a real-world parcel logistics case study in Switzerland. The model is embedded in a scenario-based framework, allowing for the systematic exploration of infrastructure sizing options and their associated cost-performance trade-offs. Results demonstrate how the temporal alignment between PV production and fleet availability, as well as the capacity to store and redistribute energy within the fleet, significantly influence cost optimization outcomes. The model highlights the value of bidirectional charging and seasonal planning in reducing peak grid demand and operational expenses. This work provides a scalable decision-support tool for logistics operators seeking to transition to electric fleets while maintaining cost control and energy resilience.