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Cost trajectories of low carbon electricity generation technologies in the UK: A study of cost uncertainty

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  • Peter G. Levi

    (Department of Engineering, University of Cambridge)

  • Michael G. Pollitt

    (Cambridge Judge Business School, University of Cambridge)

Abstract

Cost uncertainty has latterly come to be presented in the UK’s Department of Energy and Climate Change (DECC) Levelised Cost of Electricity (LCOE) estimates using sensitivities; ‘high’ and ‘low’ figures presented alongside central estimates. This presentation of uncertainty is limited in its provision of context, and of an overall picture of how costs and uncertainty vary over time. Two analyses are performed using the published DECC cost estimates for three electricity generation technologies – nuclear, offshore wind and Carbon Capture and Storage (CCS). The first analysis analyses cost trajectories from selected DECC LCOE estimates and presents them alongside contextual data, resulting in contextual cost landscapes. The second evaluates the associated temporal estimate uncertainty in the decade 2020-2030; an approach aimed at capturing the temporal consistency of estimates, alongside variations in magnitude. Nuclear estimates are found to be both the most consistent and lowest in magnitude. Offshore wind and CCS suffer from comparatively large cost and uncertainty premiums. The implications for the direction of policy are then discussed in the context of conflicting past experience and hidden costs.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • Peter G. Levi & Michael G. Pollitt, 2015. "Cost trajectories of low carbon electricity generation technologies in the UK: A study of cost uncertainty," Working Papers EPRG 1501, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    • Handle: RePEc:enp:wpaper:eprg1501
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    References listed on IDEAS

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    Cited by:

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    3. Claire Nicolas & Stéphane Tchung-Ming & Emmanuel Hache, 2016. "Energy transition in transportation under cost uncertainty, an assessment based on robust optimization," Working Papers hal-02475943, HAL.
    4. Tu, Qiang & Mo, Jianlei & Liu, Zhuoran & Gong, Chunxu & Fan, Ying, 2021. "Using green finance to counteract the adverse effects of COVID-19 pandemic on renewable energy investment-The case of offshore wind power in China," Energy Policy, Elsevier, vol. 158(C).
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    6. Santos, Maria João & Ferreira, Paula & Araújo, Madalena, 2016. "A methodology to incorporate risk and uncertainty in electricity power planning," Energy, Elsevier, vol. 115(P2), pages 1400-1411.
    7. Alina Ștefania Chenic & Alin Ioan Cretu & Adrian Burlacu & Nicolae Moroianu & Daniela Vîrjan & Dragos Huru & Mihaela Roberta Stanef-Puica & Vladimir Enachescu, 2022. "Logical Analysis on the Strategy for a Sustainable Transition of the World to Green Energy—2050. Smart Cities and Villages Coupled to Renewable Energy Sources with Low Carbon Footprint," Sustainability, MDPI, vol. 14(14), pages 1-30, July.
    8. Willems, Bert & Pollitt, Michael & von der Fehr, Nils-Henrik & Banet, Catherine, 2022. "The European Wholesale Electricty Market: From Crisis to Net Zero," Other publications TiSEM 2f225964-853e-4d30-a46d-0, Tilburg University, School of Economics and Management.

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    More about this item

    Keywords

    cost projections; nuclear; wind power; carbon capture and storage;
    All these keywords.

    JEL classification:

    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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