Spatially Explicit Modelling and Optimisation of Bioenergy Supply-Chain Infrastructures
Author(s)
Dunnett, Alexander
Type
Thesis
Abstract
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.
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.
Date Issued
2009-09
Date Awarded
2011-04
Copyright Statement
Attribution NoDerivatives 4.0 International Licence (CC BY-ND)
License URL
Advisor
Adjiman, Claire
Shah, Nilay
Creator
Dunnett, Alexander
Publisher Department
Chemical Engineering and Chemical Technology
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)