Energy Management of Airport Service Electric Vehicles to Match Renewable Generation through Rollout Approach

TL;DR

This paper introduces a rollout-based energy management strategy for airport service electric vehicles that minimizes operational costs and aligns charging with renewable energy, outperforming simple heuristics.

Contribution

It develops a new customized rollout control method for ASEV dynamics, enabling near-optimal scheduling to reduce costs and match renewable generation.

Findings

Rollout approach reduces total operation cost by approximately 10% compared to greedy heuristic.

Method effectively adapts to short-notice flight schedule changes.

Demonstrates effectiveness on both summer and winter days.

Abstract

Traditional diesel-based airport service vehicles are characterized by a heavy-duty, high-usage-frequency nature and a high carbon intensity per vehicle per hour. Transforming these vehicles into electric vehicles would reduce CO2 emissions and potentially save energy costs in the context of rising fuel prices, if a proper energy management of airport service electric vehicles (ASEVs) is performed. To perform such an energy management, this paper proposes a new customized rollout approach, as a near-optimal control method for a new ASEV dynamics model, which models the ASEV states, their transitions over time, and how control decisions affect them. The rollout approach yields a near-optimal control strategy for the ASEVs to transport luggage and to charge batteries, with the objective to minimize the operation cost, which incentivizes the charging of the ASEVs to match renewable generation. Case studies demonstrate that the rollout approach effectively overcomes the "curse of dimensionality". On both typical summer and winter days, the rollout algorithm results in a total cost approximately 10% less than that of the underlying "greedy charging" heuristic, which charges a battery whenever its state of charge is not the maximum. The rollout algorithm is proven to be adaptive towards flight schedule changes at short notice.