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Deriving Current Cost Requirements from Future Targets: Case Studies for Emerging Offshore Renewable Energy Technologies

Author

Listed:
  • Shona Pennock

    (School of Engineering, Institute for Energy Systems, University of Edinburgh, Edinburgh EH9 3DW, UK)

  • Anna Garcia-Teruel

    (School of Engineering, Institute for Energy Systems, University of Edinburgh, Edinburgh EH9 3DW, UK)

  • Donald R. Noble

    (School of Engineering, Institute for Energy Systems, University of Edinburgh, Edinburgh EH9 3DW, UK)

  • Owain Roberts

    (School of Engineering, Institute for Energy Systems, University of Edinburgh, Edinburgh EH9 3DW, UK)

  • Adrian de Andres

    (School of Engineering, Institute for Energy Systems, University of Edinburgh, Edinburgh EH9 3DW, UK)

  • Charlotte Cochrane

    (School of Engineering, Institute for Energy Systems, University of Edinburgh, Edinburgh EH9 3DW, UK)

  • Henry Jeffrey

    (School of Engineering, Institute for Energy Systems, University of Edinburgh, Edinburgh EH9 3DW, UK)

Abstract

This work investigates potential cost reduction trajectories of three emerging offshore renewable energy technologies (floating offshore wind, tidal stream, and wave) with respect to meeting ambitious cost targets set out in the Strategic Energy Technology Implementation Plans (SET-Plans) for Offshore Wind and Ocean Energy. A methodology is presented which calculates target costs for current early-stage devices, starting from the 2030 SET-Plan levelised cost targets. Component-based experience curves have been applied as part of the methodology, characterised through the comparative maturity level of each technology-specific cost centre. The resultant early-stage target costs are then compared with actual costs for current devices to highlight where further cost reduction is still required. It has been found that innovation and development requirements to reach these targets vary greatly between different technologies, based on their current level of technological maturity. Future funding calls and programmes should be designed with these variables in mind to support innovative developments in offshore renewables. The method presented in this paper has been applied to publicly available cost data for emerging renewable technologies and is fully adaptable to calculate the innovation requirements for specific early-stage renewable energy devices.

Suggested Citation

  • Shona Pennock & Anna Garcia-Teruel & Donald R. Noble & Owain Roberts & Adrian de Andres & Charlotte Cochrane & Henry Jeffrey, 2022. "Deriving Current Cost Requirements from Future Targets: Case Studies for Emerging Offshore Renewable Energy Technologies," Energies, MDPI, vol. 15(5), pages 1-19, February.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1732-:d:758741
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    References listed on IDEAS

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    1. Rinaldi, Giovanni & Garcia-Teruel, Anna & Jeffrey, Henry & Thies, Philipp R. & Johanning, Lars, 2021. "Incorporating stochastic operation and maintenance models into the techno-economic analysis of floating offshore wind farms," Applied Energy, Elsevier, vol. 301(C).
    2. Yeh, Sonia & Rubin, Edward S., 2012. "A review of uncertainties in technology experience curves," Energy Economics, Elsevier, vol. 34(3), pages 762-771.
    3. Tooraj Jamasb, 2007. "Technical Change Theory and Learning Curves: Patterns of Progress in Electricity Generation Technologies," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 51-72.
    4. Myhr, Anders & Bjerkseter, Catho & Ågotnes, Anders & Nygaard, Tor A., 2014. "Levelised cost of energy for offshore floating wind turbines in a life cycle perspective," Renewable Energy, Elsevier, vol. 66(C), pages 714-728.
    5. Tianna Bloise Thomaz & David Crooks & Encarni Medina-Lopez & Leonore van Velzen & Henry Jeffrey & Joseba Lopez Mendia & Raul Rodriguez Arias & Pablo Ruiz Minguela, 2019. "O&M Models for Ocean Energy Converters: Calibrating through Real Sea Data," Energies, MDPI, vol. 12(13), pages 1-20, June.
    6. Guanche, R. & de Andrés, A.D. & Simal, P.D. & Vidal, C. & Losada, I.J., 2014. "Uncertainty analysis of wave energy farms financial indicators," Renewable Energy, Elsevier, vol. 68(C), pages 570-580.
    7. O'Connor, M. & Lewis, T. & Dalton, G., 2013. "Operational expenditure costs for wave energy projects and impacts on financial returns," Renewable Energy, Elsevier, vol. 50(C), pages 1119-1131.
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    Cited by:

    1. Pablo Ruiz-Minguela & Donald R. Noble & Vincenzo Nava & Shona Pennock & Jesus M. Blanco & Henry Jeffrey, 2022. "Estimating Future Costs of Emerging Wave Energy Technologies," Sustainability, MDPI, vol. 15(1), pages 1-25, December.
    2. Enrico Giglio & Ermando Petracca & Bruno Paduano & Claudio Moscoloni & Giuseppe Giorgi & Sergej Antonello Sirigu, 2023. "Estimating the Cost of Wave Energy Converters at an Early Design Stage: A Bottom-Up Approach," Sustainability, MDPI, vol. 15(8), pages 1-39, April.
    3. Ferreira, D.N. & Gato, L.M.C. & Eça, L., 2023. "Efficiency of biradial impulse turbines concerning rotor blade angle, guide-vane deflection and blockage," Energy, Elsevier, vol. 266(C).
    4. Chenglong Guo & Wanan Sheng & Dakshina G. De Silva & George Aggidis, 2023. "A Review of the Levelized Cost of Wave Energy Based on a Techno-Economic Model," Energies, MDPI, vol. 16(5), pages 1-30, February.

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