Optimisation, Machine Learning and AI for Rapid Grid Decarbonisation
Abstract.
The national and transcontinental electricity grids of today are
based on devices such as coal furnaces, steam turbines, copper and
steel wires, electric transformers, and electromechanical power
switches that have remained unchanged for 100 years. However
imperceptibly, the components and operational management of this
great machine, the grid, has began to change irreversibly. This is
fortunate, as climate science tells us we must reduce
CO<sub>2</sub> emissions
from the energy sector to zero by 2050 and
to 50% of current levels by 2030 if we are to prevent dangerous
climate changes in future world that is over 1.5 degree hotter
that today. Now utility scale wind and solar PV farms as large as
coal, gas and nuclear generators are being deployed more cheaply
than it is possible to build and operate generators using older
technologies. In some cases, even these new technologies can be
cheaper that even merely the operating costs of older
technologies. In addition, low cost rooftop solar PV has also
enabled consumers to become self-suppliers and also contributors
to the supply of energy for their neighbours. Moreover, the ādumbā
grid of the past, is becoming āsmarterā. This is enabled through a
combination of ubiquitous low-cost telecommunication and
programmable devices at the edge of the grid such as smart meters,
smart PV inverters, smart air conditioners and home energy
management systems. The final component is the electrification of
the private transport system that will finally eliminate the need
for fossil fuels. The implications of this are that it is now
necessary to rapidly replan and reinvest in the energy system at
rates and in ways that are unprecedented in industrial
civilisations history. While the majority of hardware technology
already exist, the missing piece of the puzzle are new computers
science technologies, and particularly Optimisation, Machine
Learning, Forecasting and Data analytics methods needed to plan
and operate this rapidly transforming system.
In this talk I
will describe a range of ways existing computer science tools in
the Optimisation, AI, ML and other areas we and others are
enhancing in order to better operate and plan the existing power
system. I will focus on identifying emerging research
opportunities in areas that are needed to complete the
transformation to a cheaper, smarter and zero carbon energy
system.