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Influence of wave resource assessment methodology on wave energy production estimates

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  • Robertson, Bryson
  • Bailey, Helen
  • Clancy, Dan
  • Ortiz, Juan
  • Buckham, Bradley

Abstract

Renewable energy resource inventories show that ocean waves are one of the most energy dense untapped resources in the world, and present an opportunity to generate significant quantities of electricity. To accurately assess the levels of usable energy over long periods, a parametric representation of the raw wave resource is required. This study investigates the variability across four wave energy assessment methods, and two input data sources, to quantify the uncertainties in WEC power production assessments. Two conventional methods were tested: a time-series method and a standard spectral method with a generic spectral shape. Two higher fidelity techniques were additionally studied; an aggregate spectral and a partitioned spectral method.

Suggested Citation

  • Robertson, Bryson & Bailey, Helen & Clancy, Dan & Ortiz, Juan & Buckham, Bradley, 2016. "Influence of wave resource assessment methodology on wave energy production estimates," Renewable Energy, Elsevier, vol. 86(C), pages 1145-1160.
    • Handle: RePEc:eee:renene:v:86:y:2016:i:c:p:1145-1160
    DOI: 10.1016/j.renene.2015.09.020
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Gonçalves, Marta & Martinho, Paulo & Guedes Soares, C., 2018. "A 33-year hindcast on wave energy assessment in the western French coast," Energy, Elsevier, vol. 165(PB), pages 790-801.
    2. Jahangir, Mohammad Hossein & Hosseini, Seyed Sina & Mehrpooya, Mehdi, 2018. "A detailed theoretical modeling and parametric investigation of potential power in heaving buoys," Energy, Elsevier, vol. 154(C), pages 201-209.
    3. Bailey, Helen & Robertson, Bryson & Buckham, Bradley, 2018. "Variability and stochastic simulation of power from wave energy converter arrays," Renewable Energy, Elsevier, vol. 115(C), pages 721-733.
    4. Wang, Yingguang & Wang, Lifu, 2018. "Towards realistically predicting the power outputs of wave energy converters: Nonlinear simulation," Energy, Elsevier, vol. 144(C), pages 120-128.
    5. Robertson, Bryson & Bailey, Helen & Leary, Matthew & Buckham, Bradley, 2021. "A methodology for architecture agnostic and time flexible representations of wave energy converter performance," Applied Energy, Elsevier, vol. 287(C).
    6. Wei-Cheng Wu & Zhaoqing Yang & Taiping Wang, 2018. "Wave Resource Characterization Using an Unstructured Grid Modeling Approach," Energies, MDPI, vol. 11(3), pages 1-15, March.
    7. Robertson, Bryson & Bailey, Helen & Buckham, Bradley, 2019. "Resource assessment parameterization impact on wave energy converter power production and mooring loads," Applied Energy, Elsevier, vol. 244(C), pages 1-15.
    8. Fairley, Iain & Lewis, Matthew & Robertson, Bryson & Hemer, Mark & Masters, Ian & Horrillo-Caraballo, Jose & Karunarathna, Harshinie & Reeve, Dominic E., 2020. "A classification system for global wave energy resources based on multivariate clustering," Applied Energy, Elsevier, vol. 262(C).
    9. Robertson, Bryson & Jin, Yuhe & Bailey, Helen & Buckham, Bradley, 2017. "Calibrating wave resource assessments through application of the triple collocation technique," Renewable Energy, Elsevier, vol. 114(PA), pages 166-179.
    10. Luczko, Ewelina & Robertson, Bryson & Bailey, Helen & Hiles, Clayton & Buckham, Bradley, 2018. "Representing non-linear wave energy converters in coastal wave models," Renewable Energy, Elsevier, vol. 118(C), pages 376-385.
    11. Markel Penalba & John V. Ringwood, 2016. "A Review of Wave-to-Wire Models for Wave Energy Converters," Energies, MDPI, vol. 9(7), pages 1-45, June.
    12. Robertson, Bryson & Bekker, Jessica & Buckham, Bradley, 2020. "Renewable integration for remote communities: Comparative allowable cost analyses for hydro, solar and wave energy," Applied Energy, Elsevier, vol. 264(C).

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