Quantifying uncertainty in wholesale electricity price projections using Bayesian emulation of a generation investment model

Wilson, A.L.; Dent, C.J.; Goldstein, M.

Abstract

Policy-makers need to be confident that decisions based on the outputs of energy system models will be robust in the real-world. 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 long-term 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.

Citation

Wilson, A., Dent, C., & Goldstein, M. (2018). Quantifying uncertainty in wholesale electricity price projections using Bayesian emulation of a generation investment model. Sustainable Energy, Grids and Networks, 13, 42-55. https://doi.org/10.1016/j.segan.2017.11.003

• Journal Article Type: Article
• Acceptance Date: Nov 9, 2017
• Online Publication Date: Dec 2, 2017
• Publication Date: 2018-03
• Deposit Date: Jan 3, 2018
• Publicly Available Date: Jan 3, 2018
• Journal: Sustainable Energy, Grids and Networks
• Electronic ISSN: 2352-4677
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Pages: 42-55
• DOI: https://doi.org/10.1016/j.segan.2017.11.003
• Public URL: https://durham-repository.worktribe.com/output/1369356