Airport Electrified Ground Support Equipment for Providing Ancillary Services to the Grid

Alruwaili, Mohammed; Cipcigan, Liana

doi:10.2139/ssrn.4034076

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Ref. [43] proposed a method for employing EGSE in a day-ahead frequency regulation market based on airport flight schedule data, and the results showed that the EGSE could generate a substantial profit through vehicle-to-grid (V2G) services and frequency regulation ancillary services.…”

Section: Estimating Electric Ground Support Equipment Energy Demandmentioning

confidence: 99%

Energy Forecasting Model for Ground Movement Operation in Green Airport

et al. 2023

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The aviation industry has driven economic growth and facilitated cultural exchange over the past century. However, concerns have arisen regarding its contribution to greenhouse gas emissions and potential impact on climate change. In response to this challenge, stakeholders have proposed the use of electric ground support vehicles, powered by renewable energy sources, at airports. This solution aims to not only reduce emissions, but to also lower energy costs. Nonetheless, the successful implementation of such a system relies on accurate energy demand forecasting, which is influenced by flight data and fluctuations in renewable energy availability. This paper presents a novel data-driven, machine-learning-based energy prediction model that compared the performance of the Facebook Prophet and vector autoregressive integrated moving average algorithms to develop time series models to forecast the ground movement operation net energy demand in the airport, using historical flight data and an onsite airport-based PV power system (ASPV). The results demonstrate the superiority of the Facebook Prophet model over the vector autoregressive integrated moving average (VARIMA), highlighting its utility for airport operators and planners in managing energy consumption and preparing for future electrified ground movement operations at the airport.

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Section: Estimating Electric Ground Support Equipment Energy Demandmentioning

confidence: 99%