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Title: Electricity system modelling for optimal planning and technology valuation
Author: Heuberger, Clara Franziska
ISNI:       0000 0004 7223 7680
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2018
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This dissertation explores the field of electricity systems modelling and optimisation. We develop new tools, techniques, and concepts to advance systems and technology analysis. Based on a process systems engineering approach, we apply mixed-integer linear programming (MILP) to develop least-cost optimisation models of a national-scale power system in different temporal and complexity variations. The Electricity Systems Optimisation models integrate detailed power plant operation and long-term systems planning to overcome limitations of existing models for technology and system design. We present implementation strategies to include endogenous technology learning and myopic versus perfect foresight planning considering disruptive events. This is enabled through data processing and MILP reformulation techniques developed and applied in this work. We further introduce a new technology valuation metric, the System Value (SV), which quantifies the reduction in total system cost caused upon the deployment of a power generation or storage technology. Unlike purely cost-based metrics, the SV enables a fair comparison of different power technologies taking the whole-system impacts of deployment into account. We find that the SV of a given technology is a function of its penetration level and initial configuration of the system. In a future United Kingdom setting, grid-level energy storage provides the greatest value under a premise of decarbonisation and maintaining security of supply. Additionally, dispatchable and flexible low-carbon power generation, such as Carbon Capture and Storage equipped power plants prove particularly valuable in being able to accommodate higher levels of intermittent renewable power generation and providing ancillary services. On a systems level, we find that including endogenous technology learning in power systems planning emphasises the economic advantage of early investments in low-carbon technology. Myopic power systems planning can lead to sub-optimal capacity expansion. Even under the possibility of breakthrough technologies becoming available before mid-century, deploying existing low-carbon technologies early on proves advantageous from an economic and climate perspective.
Supervisor: Mac Dowell, Niall ; Shah, Nilay ; Staffell, Iain Sponsor: International Energy Agency Greenhouse Gas R&D Programme
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral