Browsing by Author "Ajayi, Adedayo"

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    Energy forecasting model for ground movement operation in green airport
    (MDPI, 2023-06-28) Ajayi, Adedayo; Luk, Patrick Chi-Kwong; Lao, Liyun; Khan, Mohammad Farhan
    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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    Future grid for a sustainable green airport: meeting the new loads of electric taxiing and electric aircraft.
    (Cranfield University, 2023-01) Ajayi, Adedayo; Luk, Patrick Chi-Kwong; Lao, Liyun
    This thesis proposes a novel electric grid in the airside to meet zero-emission targets for ground movement operations in future airports, as mandated by Aeronautics Research performance target in Europe's (ACARE) FlightPath 2050. The grid delivers power from a renewable energy source through a flexible powerline using an autonomous electric taxiing robot (A-ETR) based on the concept of Energy As A Service (EAAS) for taxiing large aircraft and charging stations for ground vehicles. Four layers of optimisation are required to realise the viability of this new grid. The first optimisation layer involves creating an analytical model of the A-ETR using real-world data from Cranfield University's Airport based solar PV system and its Boeing 737 research aircraft and optimising its performance and efficiency using vehicle-level data-driven machine learning- based optimisation. As a result, the proposed grid achieves zero-emission taxiing and a 91% reduction in fuel compared to a standard baseline. The second layer optimises energy management in the microgrid using machine learning-based forecasting models to predict PV output and optimise charging and discharging cycles of A-ETR batteries to match solar resources and electricity rates. The result shows that the support vector regression (SVR) model best predicted PV output and optimised BESS charge/discharge cycles to achieve zero-emission airport ground movement operations while reducing the microgrid operating costs. However, ground traffic and load profiles increase as the model expands to include commercial airports. Therefore, the third optimisation layer develops a machine learning-based data-driven energy prediction optimisation to ensure microgrid resilience under the increased load. The model employs the Facebook Prophet algorithm to enhance the precision of energy demand prediction for airport ground movement operations across three- time horizons. The results facilitate the generation of reliable forecasts for clean energy production and ground movement energy demand at the airport. A fourth layer of optimisation has been developed to address the limitations of solar PV energy, which depend on the weather and cannot be dispatched, as well iii as the increase in airport traffic. The layer uses wind power and data from a "green" airport to complement PV power output. This model uses the stochastic model predictive control-based cascade feedforward neural network (SMPC- CFFNN) to optimise power flow between the microgrid and RES sources and support V2G capabilities. The results demonstrate that a Zero-emission microgrid for ground movement at green airports can be achieved through optimal power flow management and time optimisation. Reliability and resilience are crucial for a proposed microgrid ecosystem. We consider different network configurations to connect the existing airport grid. Two microgrid architectures, LVAC and LVDC, are compared based on their point of common connections (PCC) to evaluate the technical and economic implications on the airport's distribution network. We verify and validate the model's performance in terms of power quality, short circuit fault levels, system protection requirements, voltage profile, power losses, and equipment/system overloading to determine the optimal architecture. The results indicate that the A-ETR can provide ancillary services to the grid and enable novel emergency response systems. The comprehensive results from the multi-layered system-level optimisation approach adopted in this thesis not only validate the novelty of the proposed study but also serve to provide compelling evidence for its potential to provide viable solutions to the electrification challenges for future green airports by creating an ecosystem between airport ground operations and on-site renewable energy generating sources.