Projects per year
Abstract / Description of output
Policymakers need to be confident that decisions based on the outputs of energy system models will be robust in the realworld. To make robust decisions it is critical that the consequences of uncertainty in model outputs are assessed. This paper presents statistical methodology for quantifying uncertainty associated with the output of a computer model of the longterm GB electricity supply. The output of the computer model studied is the projection of wholesale electricity prices from 2016 to 2030. The effect on wholesale prices of both uncertainty in input parameters and structural discrepancy is modelled. A probability distribution is used to model uncertainty over four inputs of the model: gas price, demand, EU ETS price and future offshore deployment. Estimates of the structural discrepancy introduced by the use of smoothed gas price projections and assuming that coal prices out to 2030 are known are obtained from experimentation with the computer model. A statistical model, known as an emulator, is fitted to a set of computer model evaluations and used to model uncertainty in the output of the computer model at inputs that have not been tested. The emulator is combined with the probability distribution over the inputs and the estimate of structural discrepancy to make an assessment of the overall uncertainty in the wholesale electricity price projections. A sensitivity analysis is also performed to investigate the effect of each of the four inputs on the trajectory of wholesale electricity prices.
Original language  English 

Pages (fromto)  4255 
Journal  Sustainable Energy, Grids and Networks 
Volume  13 
Early online date  2 Dec 2017 
DOIs  
Publication status  Published  1 Mar 2018 
Fingerprint
Dive into the research topics of 'Quantifying uncertainty in wholesale electricity price projections using Bayesian emulation of a generation investment model'. Together they form a unique fingerprint.Projects
 3 Finished

High Energy and Power Density (HEAPD) Solutions to Large Energy Deficits
12/09/16 → 29/12/17
Project: Research

Uncertainty analysis of hierarchical energy systems models: Models versus real energy systems
12/09/16 → 22/09/18
Project: Research

HubNet: Research Leadership and Networking for Energy Networks (Extension)
12/09/16 → 30/09/18
Project: Research
Profiles

Chris Dent
 School of Mathematics  Personal Chair of Industrial Mathematics
Person: Academic: Research Active (Teaching)

Amy Wilson
 School of Mathematics  Lecturer in Industrial Mathematics
Person: Academic: Research Active (Research Assistant)