Electrification has significant potential to reduce aviation's carbon footprint. However, transitioning from conventional-fuel aircraft to battery-powered Electric Aircraft (EA) will impose infrastructure and energy requirements on airports. Airports are not currently prepared to support the high-frequency operations of EA, particularly for upcoming applications such as short-haul and flight training operations. Without adequate preparation, such operations risk compromising service levels and creating new peak electricity loads. This study develops a stochastic optimization framework for sizing airport infrastructure that jointly determines the optimal number of spare batteries and chargers, accounting for electrical grid capacity constraints and potential grid upgrades. Airport operations are modeled daily using real flight arrival and departure data, in which each arriving aircraft returns a battery with a given state of charge, and each departing aircraft requires a fully charged battery. System performance is evaluated using a Monte Carlo simulation that randomly samples independent daily operating scenarios from historical data. Reliability is defined as the probability that all departures within a day are served without battery shortages and with delays less than the acceptable delay time. A parallel-machine, non-preemptive scheduling MILP with power coupling and lateness and cost objectives — approximated via a greedy simulation-based heuristic. The objective function includes maximum lateness, total lateness over the horizon, costs for spare batteries and chargers, and the cost of upgrading grid capacity. The proposed approach quantifies trade-offs among battery inventory, charger capacity, and grid reinforcement and evaluates their impacts on reliability and peak power demand. The results demonstrate how incorporating grid upgrade costs can shift optimal infrastructure designs and provide decision-makers with a transparent tool for planning airport electrification under operational uncertainty and power constraints.

Keywords

Resource Optimization, Electric Vehicle Adoption, Power grid, Electric Vehicle Charging Infrastructure, Environmental Sustainability