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The Value of Learning about Critical Energy System Uncertainties
In this thesis, a sensitivity analysis is used to systematically classify and rank parametric uncertainties in an energy system optimisation model of the United Kingdom, ETI-ESME. A subset of the most influential uncertainties are then evaluated in a model which investigates the process of resolving uncertainty over time — learning. The learning model identifies strategies and optimal pathways for staged investment in these critical uncertainties. By soft-linking the learning model to an energy system optimisation model, the strategies also take into account the system-wide trade-offs for investment across individual or portfolios of technologies. A global sensitivity analysis method, the Method of Morris, was used to efficiently analyse the model over the full range and combination of input parameter values covering technology costs and efficiencies, resource costs, and technology/infrastructure build-rate and resource-constraints. The results of the global sensitivity analysis show that very few parameters are responsible for the majority of variation in the outputs from the model. These critical uncertainties can be separated into two groups according to their suitability for learning. Some of the important uncertainties identified, such as the price of fossil fuel resources available to the UK, are not amenable to learning and must be managed through risk-based approaches. The parameters which are amenable to learning, the availability of domestic biomass, and the rate at which carbon capture and storage technologies can be deployed, are then investigated using the learning model. The learning model is formulated as a stochastic mixed-integer programme, and gives insights into the dynamic trade-offs between competing learning options within the context of the whole energy system. A UK case study shows that, if the resources are known to be available, total discounted net benefit of the availability of 150TWh/year of domestic biomass is £30bn, while the ability to build CCS plant at a rate of 2GW/year is ...
The Value of Learning about Critical Energy System Uncertainties
In this thesis, a sensitivity analysis is used to systematically classify and rank parametric uncertainties in an energy system optimisation model of the United Kingdom, ETI-ESME. A subset of the most influential uncertainties are then evaluated in a model which investigates the process of resolving uncertainty over time — learning. The learning model identifies strategies and optimal pathways for staged investment in these critical uncertainties. By soft-linking the learning model to an energy system optimisation model, the strategies also take into account the system-wide trade-offs for investment across individual or portfolios of technologies. A global sensitivity analysis method, the Method of Morris, was used to efficiently analyse the model over the full range and combination of input parameter values covering technology costs and efficiencies, resource costs, and technology/infrastructure build-rate and resource-constraints. The results of the global sensitivity analysis show that very few parameters are responsible for the majority of variation in the outputs from the model. These critical uncertainties can be separated into two groups according to their suitability for learning. Some of the important uncertainties identified, such as the price of fossil fuel resources available to the UK, are not amenable to learning and must be managed through risk-based approaches. The parameters which are amenable to learning, the availability of domestic biomass, and the rate at which carbon capture and storage technologies can be deployed, are then investigated using the learning model. The learning model is formulated as a stochastic mixed-integer programme, and gives insights into the dynamic trade-offs between competing learning options within the context of the whole energy system. A UK case study shows that, if the resources are known to be available, total discounted net benefit of the availability of 150TWh/year of domestic biomass is £30bn, while the ability to build CCS plant at a rate of 2GW/year is ...
The Value of Learning about Critical Energy System Uncertainties
Usher, PW (author) / strachan, N / Keppo, I
2016-07-28
Doctoral thesis, UCL (University College London).
Theses
Electronic Resource
English
DDC:
690
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