Abstract
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.
Highlights
Computer models are widely used to help study the behaviour of energy systems and to make decisions about these systems
To incorporate structural discrepancy relating to coal and gas prices in our uncertainty specification, we model our uncertainty in the wholesale price time series w(x) as w(x) = f(x)T LT + εPCA + εg + εc, (5)
The mean of the projections is shown by a solid black line. This black line represents the mean wholesale price projection integrated over uncertainty in the input parameters, structural discrepancy and uncertainty arising from the limited number of model evaluations
Summary
Computer models are widely used to help study the behaviour of energy systems and to make decisions about these systems (e.g. see [1] and [2]). Even for inputs that have not been run, an approximation of the model output at that input (given by the mean of the emulator) and the error in this approximation (given by the standard deviation of the emulator) can be obtained Emulation makes it possible to assess uncertainty when the number of model evaluations is limited, whilst quantifying the additional uncertainty arising from this sparse coverage of the input space. This paper uses emulation and a model for structural discrepancy to assess uncertainty in projections of the wholesale electricity price obtained using a computer model.
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