Abstract

This study addresses the issues of assessing and factoring in the effect of uncertainty growth on the possible performance of projections and their allowable errors. Relying on projects of nuclear power plants and combined cycle power plants as a case study, we assess the dependence of their economic performance indicators on possible changes in the conditions of their future operation in a given year. To assess the effect of the range and nature of input data uncertainty on the projections of the development of regional energy supply systems, we proposed a methodological toolkit that combines optimization with the Monte Carlo simulation. Its application to one of the options for commissioning new power plants in European Russia enabled us to estimate the possible response of the average and marginal cost of electricity in this aggregated region to the broadening of the uncertainty range of the gas price. We note that the assessment and comparison of the possible error of projected indicators with the requirements for their accuracy in making priority investment and other decisions facilitate the justification of the acceptable complexity of employed models and projection methods.

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