Details of Grant 

EPSRC Reference:	EP/K03832X/1
Title:	Uncertainty analysis of hierarchical energy systems models: Models versus real energy systems
Principal Investigator:	Dent, Professor C
Other Investigators:	Goldstein, Professor M
Researcher Co-Investigators:
Project Partners:
Department:	Engineering and Computing Sciences
Organisation:	Durham, University of
Scheme:	Standard Research
Starts:	05 May 2014	Ends:	11 September 2016	Value (£):	367,435
EPSRC Research Topic Classifications:	Energy Efficiency
EPSRC Industrial Sector Classifications:	Energy
Related Grants:
Panel History:	Panel Date Panel Name Outcome 07 May 2013 Engineering Prioritisation Meeting 7/8 May 2013 Announced
Summary on Grant Application Form
Mathematical models are used widely in the planning and operation of energy systems, and in the development of public energy policy. The aim is to understand the impact of new policies, technologies and market operations. This is particularly significant at present due to the need to decarbonise energy systems over the coming decades, which is driving change in energy supply at a very rapid pace. Specific recent uses of large scale modelling studies in formal government policy Impact Assessments are the 2012 Energy Bill (which among other models uses economic modelling to project future generation investment under different policy options) and the 4th Carbon Budget (which provides the legal limit on GB carbon emissions from 2023 to 2027, and uses the UK MARKAL model for projecting evolution of the energy system under a given background scenario). This project will study the relationship between mathematical and computer models of energy systems, and the real systems that they attempt to describe, with the purpose of enabling better model-based decisions in industry and government. Until such a relationship has been established between the model and the physical system that the model purports to represent, it is impossible to draw fully robust conclusions based on the model. This model/reality relationship will necessarily be probabilistic, expressing the degree to which uncertainty about the world can be resolved by careful use of the model. We will show, by a careful choice of exemplars, the ways in which such probabilistic relationships may be constructed for a wide range of energy systems models. When we have linked the model and the physical system, the model can be embedded in a meaningful decision support system for choosing sensible future actions. This involves honest and careful assessment of all of the uncertainties involved in the planning process and consequent forecasting of the uncertainties associated with each possible planning choice. Using the selected exemplars, we will show how to replace current energy planning scenarios with a scrupulous uncertainty based guide to the consequences of current and future actions. While much of this project involves specific exemplars, our intentions are general, namely to derive methodology which is widely applicable across the whole field of energy systems modelling and planning and therefore to show the potential for transformative analysis across the whole field. For this reason, our exemplars have been chosen to reflect general features common to a wide variety of energy models, which generally comprise systems of interconnected models which are each complex in their own right (e.g. a market model linking to an assessment of the engineering consequences of investment decisions, or a model describing interacting transmission and distribution networks). General methodology for the analysis of computer models will be tailored to the requirements of energy systems analysis. Further, some aspects, and in particular the development of effective uncertainty analysis for linked computer models, will have impact across modelling applications in many other fields. Exemplars used will include studying the interaction between investment in generating capacity and the risks of supply shortages, the participation of resources embedded within distribution networks in the national energy market, and embedding a model of a particular sector of the energy economy within a model which projects the evolution of the whole energy economy. We will work on these exemplars in discussion with industrial collaborators, in order to identify how our methods must be designed and communicated in order for them to lead to eventual field application.
Key Findings
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Potential use in non-academic contexts
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