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Title: A stochastic optimization framework for anticipatory transmission investment
Author: Konstantelos, Ioannis
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2013
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An unprecedented amount of renewable generation is to be connected to the UK grid in the coming decades, giving rise to new power flow patterns and warranting unprecedented amounts of transmission investment. However, significant uncertainty surrounds the state of the electricity system, primarily in terms of the size, location and type of new generators to be connected. These sources of uncertainty render the system planner unable to make fully informed decisions about future transmission investment. This thesis presents a stochastic formulation for the transmission expansion planning problem under uncertainty in future generation developments. The problem has been modelled as a multi-stage stochastic optimization problem where the expected system cost is to be minimized. Uncertainty is captured in the form of a multi-stage scenario tree that portrays a range of possible future system states and transition probabilities. A set of investment options with different upgradeability levels and construction times have been included in the formulation to capture the diverse choices present in a realistic setting, where the planner can choose to invest in an anticipatory manner. A novel multi-cut Benders decomposition scheme is used to render the model tractable for large systems with multiple scenarios and operating points. The developed tool can identify the optimal long-term investment strategy based on the triptych of economic efficiency, adequate security provision and acceptable risk. Simulation results on test systems validate that the stochastic approach can lead to further expected cost minimization when compared to methods that ignore the planner’s decision flexibility. Moreover, decisions are taken with subsequent adaptability in mind. The benefit of keeping future expansion options open is properly valued; investment paths that enable future delivery at lower costs are favoured while premature project commitment is avoided.
Supervisor: Strbac, Goran Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available