The cost of balancing supply and demand will increase as power systems are decarbonised,
because the requirement for operating reserve will increase with the wind
penetration, while the flexible fossil-fuel generators, which have been the traditional
providers of reserve, will be displaced. While these costs can be mitigated through increased
interconnection, energy storage, and demand-side market participation, a fundamental
review of system operational policy is also needed to ensure that the available
reserves are scheduled optimally. Stochastic Unit Commitment can find the commitment
and dispatch decisions that minimise the expected system costs, including the potential
costs of unserved energy, given the short-term uncertainties of wind and other variables.
It therefore has the potential to provide the most efficient possible paradigm for the operation
of wind-integrated systems. Because the system’s ability to respond to wind fluctuations
is constrained by intertemporal limitations of the other components, time domain
simulations are needed to assess the performance of different operational strategies or
generator fleet characteristics. However, Stochastic Unit Commitment has demanding
computational requirements that can render it impractical for long-term simulations of a
large power system.
This thesis develops a new tool for simulating the operation of large, wind-integrated
power systems using stochastic scheduling, with the emphasis on computational efficiency.
Embedded within it are new models for characterising time series of aggregated
wind output and wind forecast errors; these models are integrated with a Stochastic Unit
Commitment algorithm within a Monte Carlo framework. We explore simplifications
that can mitigate the computational burden without unduly compromising the quality
of the analysis. Simulations with the tool show that fully stochastic scheduling can reduce
operating costs by around 4% relative to traditional deterministic approaches, in a
system with a 50% wind penetration.