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Title: Stochastic scheduling of wind-integrated power systems
Author: Sturt, Alexander
ISNI:       0000 0004 2713 4551
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2011
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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.
Supervisor: Strbac, Goran Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral