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Reducing variability in the cost of energy of ocean energy arrays

Author

Listed:
  • Topper, Mathew B.R.
  • Nava, Vincenzo
  • Collin, Adam J.
  • Bould, David
  • Ferri, Francesco
  • Olson, Sterling S.
  • Dallman, Ann R.
  • Roberts, Jesse D.
  • Ruiz-Minguela, Pablo
  • Jeffrey, Henry F.

Abstract

Variability in the predicted cost of energy of an ocean energy converter array is more substantial than for other forms of energy generation, due to the combined stochastic action of weather conditions and failures. If the variability is great enough, then this may influence future financial decisions. This paper provides the unique contribution of quantifying variability in the predicted cost of energy and introduces a framework for investigating reduction of variability through investment in components. Following review of existing methodologies for parametric analysis of ocean energy array design, the development of the DTOcean software tool is presented. DTOcean can quantify variability by simulating the design, deployment and operation of arrays with higher complexity than previous models, designing sub-systems at component level. A case study of a theoretical floating wave energy converter array is used to demonstrate that the variability in levelised cost of energy (LCOE) can be greatest for the smallest arrays and that investment in improved component reliability can reduce both the variability and most likely value of LCOE. A hypothetical study of improved electrical cables and connectors shows reductions in LCOE up to 2.51% and reductions in the variability of LCOE of over 50%; these minima occur for different combinations of components.

Suggested Citation

  • Topper, Mathew B.R. & Nava, Vincenzo & Collin, Adam J. & Bould, David & Ferri, Francesco & Olson, Sterling S. & Dallman, Ann R. & Roberts, Jesse D. & Ruiz-Minguela, Pablo & Jeffrey, Henry F., 2019. "Reducing variability in the cost of energy of ocean energy arrays," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 263-279.
  • Handle: RePEc:eee:rensus:v:112:y:2019:i:c:p:263-279
    DOI: 10.1016/j.rser.2019.05.032
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    Cited by:

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    3. 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.
    4. Penalba, Markel & Ulazia, Alain & Saénz, Jon & Ringwood, John V., 2020. "Impact of long-term resource variations on wave energy Farms: The Icelandic case," Energy, Elsevier, vol. 192(C).
    5. Topper, Mathew B.R. & Olson, Sterling S. & Roberts, Jesse D., 2021. "On the benefits of negative hydrodynamic interactions in small tidal energy arrays," Applied Energy, Elsevier, vol. 297(C).
    6. Clemente, D. & Rosa-Santos, P. & Taveira-Pinto, F., 2021. "On the potential synergies and applications of wave energy converters: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    7. Owain Roberts & Jillian Catherine Henderson & Anna Garcia-Teruel & Donald R. Noble & Inès Tunga & Jonathan Hodges & Henry Jeffrey & Tim Hurst, 2021. "Bringing Structure to the Wave Energy Innovation Process with the Development of a Techno-Economic Tool," Energies, MDPI, vol. 14(24), pages 1-25, December.
    8. Bianchi, Marco & Fernandez, Iratxe Fernandez, 2024. "A systematic methodology to assess local economic impacts of ocean renewable energy projects: Application to a tidal energy farm," Renewable Energy, Elsevier, vol. 221(C).

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