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Resource assessment parameterization impact on wave energy converter power production and mooring loads

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  • Robertson, Bryson
  • Bailey, Helen
  • Buckham, Bradley

Abstract

Assessing and quantifying a wave energy resource is difficult and complex. Parameterizing the spectral wave information, the distribution of winds and current directions and magnitudes, and the flow of tides, inherently discards valuable information and introduces uncertainty. The impact of this uncertainty on wave energy converter power production, mooring loads, and survivability metrics is still vague and unresolved. This novel work quantifies the uncertainty, and ranks the relative impact, of differing parameterizations of the wave and environmental conditions on wave energy converter power and mooring line tension predictions. The results shows a higher fidelity representation, when compared to the International Electrotechnical Commission Technical Specification requirements, reduces mean power predictions by 11.6%, but increases maximum predictions by 328%. For mooring tension (one metric for survivability), a higher fidelity representation indicates the frequently applied specification methods overpredicts the mean and maximum tensions by 182% and 729%, respectively. This research identifies wave groupiness and wind speed as dominant additional parameters to include for improved power estimation; and the difference between wave and current direction, and current speed for improve mooring line tension predictions. By quantifying the relative impact and ranking of environmental conditions on both power and mooring tension predictions, this novel work highlights the necessary and sufficient fidelity within the parameterization of environmental conditions for minimizing uncertainty in assessments of wave energy technologies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:244:y:2019:i:c:p:1-15
    DOI: 10.1016/j.apenergy.2019.03.208
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    Cited by:

    1. Farajvand, Mahdiyeh & Grazioso, Valerio & García-Violini, Demián & Ringwood, John V., 2023. "Uncertainty estimation in wave energy systems with applications in robust energy maximising control," Renewable Energy, Elsevier, vol. 203(C), pages 194-204.
    2. Chen, Y.-L. & Lin, C.-C. & Chen, J.-H. & Lee, Y.-H. & Tzang, S.-Y., 2023. "Characteristics of wave energy resources on coastal waters of northeast Taiwan," Renewable Energy, Elsevier, vol. 202(C), pages 1-16.
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    5. Choupin, O. & Têtu, A. & Del Río-Gamero, B. & Ferri, F. & Kofoed, JP., 2022. "Premises for an annual energy production and capacity factor improvement towards a few optimised wave energy converters configurations and resources pairs," Applied Energy, Elsevier, vol. 312(C).
    6. Zou, Shangyan & Robertson, Bryson & Paudel, Sanjaya, 2023. "Geospatial Analysis of Technical U.S. Wave Net Power Potential," Renewable Energy, Elsevier, vol. 210(C), pages 725-736.
    7. Mahmoodi, Kumars & Ghassemi, Hassan & Razminia, Abolhassan, 2020. "Performance assessment of a two-body wave energy converter based on the Persian Gulf wave climate," Renewable Energy, Elsevier, vol. 159(C), pages 519-537.
    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. Beya, Ignacio & Buckham, Bradley & Robertson, Bryson, 2021. "Impact of tidal currents and model fidelity on wave energy resource assessments," Renewable Energy, Elsevier, vol. 176(C), pages 50-66.

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