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Decision-support tool for assessing future nuclear reactor generation portfolios

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  • Jain, Shashi
  • Roelofs, Ferry
  • Oosterlee, Cornelis W.

Abstract

Capital costs, fuel, operation and maintenance (O&M) costs, and electricity prices play a key role in the economics of nuclear power plants. Often standardized reactor designs are required to be locally adapted, which often impacts the project plans and the supply chain. It then becomes difficult to ascertain how these changes will eventually reflect in costs, which makes the capital costs component of nuclear power plants uncertain. Different nuclear reactor types compete economically by having either lower and less uncertain construction costs, increased efficiencies, lower and less uncertain fuel cycles and O&M costs etc. The decision making process related to nuclear power plants requires a holistic approach that takes into account the key economic factors and their uncertainties. We here present a decision-support tool that satisfactorily takes into account the major uncertainties in the cost elements of a nuclear power plant, to provide an optimal portfolio of nuclear reactors. The portfolio so obtained, under our model assumptions and the constraints considered, maximizes the combined returns for a given level of risk or uncertainty. These decisions are made using a combination of real option theory and mean–variance portfolio optimization.

Suggested Citation

  • Jain, Shashi & Roelofs, Ferry & Oosterlee, Cornelis W., 2014. "Decision-support tool for assessing future nuclear reactor generation portfolios," Energy Economics, Elsevier, vol. 44(C), pages 99-112.
  • Handle: RePEc:eee:eneeco:v:44:y:2014:i:c:p:99-112
    DOI: 10.1016/j.eneco.2014.03.021
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    References listed on IDEAS

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    Cited by:

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    2. David Juárez-Luna, 2021. "Power generation portfolios: A parametric formulation of the efficient frontier," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 16(1), pages 1-29, Enero - M.
    3. Su, Qing & Zhou, Peng & Ding, Hao, 2025. "Portfolio optimization of diversified energy transition investments with multiple risks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 219(C).
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    5. Salah Ud-Din Khan & Zeyad Almutairi & Meshari Alanazi, 2021. "Techno-Economic Assessment of Fuel Cycle Facility of System Integrated Modular Advanced Reactor (SMART)," Sustainability, MDPI, vol. 13(21), pages 1-11, October.
    6. Cardin, Michel-Alexandre & Zhang, Sizhe & Nuttall, William J., 2017. "Strategic real option and flexibility analysis for nuclear power plants considering uncertainty in electricity demand and public acceptance," Energy Economics, Elsevier, vol. 64(C), pages 226-237.
    7. Petchrompo, Sanyapong & Parlikad, Ajith Kumar, 2019. "A review of asset management literature on multi-asset systems," Reliability Engineering and System Safety, Elsevier, vol. 181(C), pages 181-201.
    8. Juárez-Luna, David, 2020. "Beneficios económicos y ambientales de la energía nuclear [Economic and environmental benefits of nuclear energy]," MPRA Paper 98790, University Library of Munich, Germany.

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    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • D92 - Microeconomics - - Micro-Based Behavioral Economics - - - Intertemporal Firm Choice, Investment, Capacity, and Financing
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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