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Contingency in nuclear power plant decommissioning cost estimation

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  • Rothwell, Geoffrey

Abstract

Hundreds of estimates have been made of decommissioning costs for commercial nuclear power plants. There is no standard method for making these estimates, particularly for estimating contingency, a type of risk premium, to cover unexpected costs during project execution. Following cost engineering guidelines for estimating contingency requires, among other project characteristics, consideration of (1) the cost estimate's probability distribution, (2) the cost estimate's variance, (3) the estimator's assumed accuracy range that will contain the final cost, (4) the estimator's level of confidence that the final cost will be in the stated accuracy range, and (5) the cost estimate user's risk aversion. The variance (or standard deviation) can be calculated using Monte Carlo techniques, which generally assume no correlation between individual activity costs. Because of portfolio effects, the simulated standard deviation of the aggregated activity cost is smaller than what would be suggested by nuclear decommissioning cost estimating guidelines. The simulated standard deviation is more reasonable when correlations are assumed to be non-zero between activities. This conclusion is shown by applying the proposed method for estimating contingency to activity costs estimated by the decommissioning operations contractor EnergySolutions to decommission the Kewaunee nuclear power plant in Wisconsin, while it is decommissioning four other US nuclear power units after it completed the decontamination and demolition of four units, including Zion 1&2. EnergySolutions could be approaching Nth-of-a-Kind decommissioning costs for US nuclear power plants, thus cost uncertainty should decrease.

Suggested Citation

  • Rothwell, Geoffrey, 2025. "Contingency in nuclear power plant decommissioning cost estimation," Energy Economics, Elsevier, vol. 149(C).
  • Handle: RePEc:eee:eneeco:v:149:y:2025:i:c:s0140988325005481
    DOI: 10.1016/j.eneco.2025.108721
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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • L74 - Industrial Organization - - Industry Studies: Primary Products and Construction - - - Construction
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • O38 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Government Policy
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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