1 research outputs found
Future grid for a sustainable green airport: meeting the new loads of electric taxiing and electric aircraft.
Lao, Liyun - Associate SupervisorThis 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
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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.PhD in Energy and Powe