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Title: Spatially explicit modelling and optimisation of bioenergy supply-chain infrastructures
Author: Dunnett, Alexander
ISNI:       0000 0004 2703 5932
Awarding Body: Imperial College London
Current Institution: Imperial College London
Date of Award: 2011
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Bioenergy supply chains have the potential to deliver renewable low-carbon energy, increased energy security and diversification of agricultural markets. In comparison to fossil fuels, biomass feedstocks are characterised by low spatial-yield and bulk-energetic densities. Logistics are therefore a key factor in determining optimal locations for biomass utilisation, and a significant constraint on scales of deployment. Strategic planning of bioenergy implementation requires innovative, wholesystem modelling approaches which consider simultaneously both the technological and spatial configuration of the system. Furthermore, there exists a relatively short window of opportunity to explore the optimal configuration of bioenergy systems before they develop organically. Insights derived from such modelling approaches may be of vital importance in informing national and international policy as well as strategic decisions in industry. This thesis presents a spatially explicit modelling framework for the design and optimisation of bioenergy supply chain infrastructures. The framework integrates (i) spatial distributions of biomass supply, (ii) locations of energy demand, (iii) logistical flows, and (iv) technological economies of scale. A series of normative, minimum cost optimisation models are formulated as large mixed integer linear programming (MILP) problems. Model applications have focussed on Great Britain. Geographic Information System (GIS) tools have been used to generate a map of domestic biomass resources, existing infrastructure, and energy demands. A static-snapshot model has analysed the whole-system performance of integrated heat and power supply chains. Road, rail and ship transport of biomass have been examined, a range of alternative market structure scenarios characterised, and their impacts on the spatial configuration of cost-optimal infrastructures assessed. An innovative dynamic model formulation with endogenous technological learning has provided insight into mechanisms driving the spatial-dynamic evolution of the bioenergy infrastructure system. A general applicability of the framework to renewable energy systems modelling is recognised.
Supervisor: Adjiman, Claire ; Shah, Nilay Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral