A Model Predictive Control Scheme for Flight Scheduling and Energy Management of Electric Aviation Networks

TL;DR

This work introduces a Model Predictive Control framework that jointly optimizes flight routing and battery energy management for electric regional aviation networks to reduce grid dependency. By formulating the problem on a time-varying DAG and integrating on-site renewable generation with battery storage, the approach supports real-time reallocation of flights and charging within a receding-horizon MILP. Numerical evaluation on the Cape Air network with real weather data demonstrates that the method can cut grid energy use by 10–37% compared to a baseline without re-routing, highlighting the potential for airports to operate as energy hubs. The study advances the field by coupling aircraft routing with airport energy systems under weather-aware forecasts, with implications for grid resilience and renewable integration in regional aviation.

Abstract

This paper presents a Model Predictive Control (MPC) scheme for flight scheduling and energy management of electric aviation networks, where electric aircraft transport passengers between electrified airports equipped with sustainable energy sources and battery storage, with the goal of minimizing grid dependency. Specifically, we first model the aircraft flight and charge scheduling problem jointly with the airport energy management problem, explicitly accounting for local weather forecasts. Second, we frame the minimum-grid-energy operational problem as a mixed-integer linear program and solve it in a receding horizon fashion, where the route assignment and charging decisions of each aircraft can be dynamically reassigned to mitigate disruptions. We showcase the proposed MPC scheme on real-world data taken from a conventional flight network and weather conditions in the US American North East. The proposed framework saves between 10 and 37% of grid energy requirements when compared to a baseline without re-routing. Hence, results show that MPC can effectively guarantee operation of the network by efficiently re-assigning flights and rescheduling aircraft charging, while maximizing the efficiency of the on-site energy systems.

Figures (6)

• Figure 1: Energy model of an airport $h\in\mathcal{H}$ with renewable energy sources in addition to the grid connection and a stationary battery (BESS). Arrows indicate positive direction of power flow.
• Figure 2: Example of a DAG constructed at time $t_\mathrm{M}$ for a horizon of $N$ time steps $\Delta t$ apart with 2 airports and 1 flight scheduled in the horizon. The virtual flight edges in the set of virtual flight edges corresponding to the first edge in $\mathcal{A}^f_t$ are highlighted in red. Aircraft 1 is on the ground at $t_\mathrm{M}$, while aircraft 2 is en-route and thus its path origin is marked in the graph at its estimated time of arrival.
• Figure 3: Scheme for implementation of the Aircraft Routing and Charge Scheduling (ARCS). Blocks with rounded edges are exogenous inputs and variables refer to the respective quantity at the measured time unless otherwise specified.
• Figure 4: Flight network operated by Cape Air in New England.
• Figure 5: Comparison of projected (muted,dashed) and actual (solid) state of charge (SoC) trajectories for a selected time window at Martha's Vineyard Airport (MVY). Flights 40/55 are from/to HPN, 40/(53,58) from/to BOS.