Global wave energy resource classification system for regional energy planning and project development

Ahn, Seongho; Neary, Vincent S.; Haas, Kevin A.

doi:10.1016/j.rser.2022.112438

Global wave energy resource classification system for regional energy planning and project development

Journal Article · Wed Apr 20 00:00:00 EDT 2022 · Renewable and Sustainable Energy Reviews

DOI:https://doi.org/10.1016/j.rser.2022.112438· OSTI ID:1877175

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Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Georgia Inst. of Technology, Atlanta, GA (United States)

Efforts to streamline and codify wave energy resource characterization and assessment for regional energy planning and wave energy converter (WEC) project development have motivated the recent development of resource classification systems. Given the unique interplay between WEC absorption and resource attributes, viz, available wave power frequency, directionality, and seasonality, various consensus resource classification metrics have been introduced. However, the main international standards body for the wave energy industry has not reached consensus on a wave energy resource classification system designed with clear goals to facilitate resource assessment, regional energy planning, project site selection, project feasibility studies, and selection of WEC concepts or archetypes that are most suitable for a given wave energy climate. In this work, a primary consideration of wave energy generation is the available energy that can be captured by WECs with different resonant frequency and directional bandwidths. Therefore, the proposed classification system considers combinations of three different wave power classifications: the total wave power, the frequency-constrained wave power, and the frequency-directionally constrained wave power. The dominant wave period bands containing the most wave power are sub-classification parameters that provide useful information for designing frequency and directionally constrained WECs. The bulk of the global wave energy resource is divided into just 22 resource classes representing distinct wave energy climates that could serve as a common language and reference framework for wave energy resource assessment if codified within international standards.

View Accepted Manuscript (DOE)

Research Organization:: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Hydropower Technology Program; USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: NA0003525

OSTI ID:: 1877175

Report Number(s):: SAND2022-9785J; 708378

Journal Information:: Renewable and Sustainable Energy Reviews, Journal Name: Renewable and Sustainable Energy Reviews Vol. 162; ISSN 1364-0321

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (38)

Efficient primary energy conversion in irregular waves Korde, U. A. Ocean Engineering, Vol. 26, Issue 7 https://doi.org/10.1016/S0029-8018(98)00017-1	journal	July 1999
Wind as an alternative source of energy in Jordan Habali, S. M.; Amr, Mohammad; Saleh, Isaac Energy Conversion and Management, Vol. 42, Issue 3 https://doi.org/10.1016/S0196-8904(00)00054-6	journal	February 2001
Wind potential assessment of Quebec Province Ilinca, Adrian; McCarthy, Ed; Chaumel, Jean-Louis Renewable Energy, Vol. 28, Issue 12 https://doi.org/10.1016/S0960-1481(03)00072-7	journal	October 2003
A global wave power resource and its seasonal, interannual and long-term variability Reguero, B. G.; Losada, I. J.; Méndez, F. J. Applied Energy, Vol. 148 https://doi.org/10.1016/j.apenergy.2015.03.114	journal	June 2015
A classification system for global wave energy resources based on multivariate clustering Fairley, Iain; Lewis, Matthew; Robertson, Bryson Applied Energy, Vol. 262 https://doi.org/10.1016/j.apenergy.2020.114515	journal	March 2020
Wave energy resource characterization and assessment for coastal waters of the United States Ahn, Seongho; Haas, Kevin A.; Neary, Vincent S. Applied Energy, Vol. 267 https://doi.org/10.1016/j.apenergy.2020.114922	journal	June 2020
Wave energy resource characterization employing joint distributions in frequency-direction-time domain Ahn, Seongho; Neary, Vincent S. Applied Energy, Vol. 285 https://doi.org/10.1016/j.apenergy.2020.116407	journal	March 2021
Extreme wave statistical methods and implications for coastal analyses Hiles, Clayton E.; Robertson, Bryson; Buckham, Bradley J. Estuarine, Coastal and Shelf Science, Vol. 223 https://doi.org/10.1016/j.ecss.2019.04.010	journal	July 2019
On the modelling of swell spectra Lucas, Cláudia; Guedes Soares, C. Ocean Engineering, Vol. 108 https://doi.org/10.1016/j.oceaneng.2015.08.017	journal	November 2015
Non-stationary historical trends in wave energy climate for coastal waters of the United States Ahn, Seongho; Neary, Vincent S. Ocean Engineering, Vol. 216 https://doi.org/10.1016/j.oceaneng.2020.108044	journal	November 2020
Modeling mean relation between peak period and energy period of ocean surface wave systems Ahn, Seongho Ocean Engineering, Vol. 228 https://doi.org/10.1016/j.oceaneng.2021.108937	journal	May 2021
Validation of a thirty year wave hindcast using the Climate Forecast System Reanalysis winds Chawla, Arun; Spindler, Deanna M.; Tolman, Hendrik L. Ocean Modelling, Vol. 70 https://doi.org/10.1016/j.ocemod.2012.07.005	journal	October 2013
Wave modelling in coastal and inner seas Cavaleri, L.; Abdalla, S.; Benetazzo, A. Progress in Oceanography, Vol. 167 https://doi.org/10.1016/j.pocean.2018.03.010	journal	October 2018
Analysis of the nearshore wave energy resource Folley, M.; Whittaker, T. J. T. Renewable Energy, Vol. 34, Issue 7 https://doi.org/10.1016/j.renene.2009.01.003	journal	July 2009
Assessment of wave energy resources in Hawaii Stopa, Justin E.; Cheung, Kwok Fai; Chen, Yi-Leng Renewable Energy, Vol. 36, Issue 2 https://doi.org/10.1016/j.renene.2010.07.014	journal	February 2011
Quantifying the global wave power resource Gunn, Kester; Stock-Williams, Clym Renewable Energy, Vol. 44 https://doi.org/10.1016/j.renene.2012.01.101	journal	August 2012
Wave energy resources along the Hawaiian Island chain Stopa, Justin E.; Filipot, Jean-François; Li, Ning Renewable Energy, Vol. 55 https://doi.org/10.1016/j.renene.2012.12.030	journal	July 2013
Assessment of wave energy in the Canary Islands Gonçalves, Marta; Martinho, Paulo; Guedes Soares, C. Renewable Energy, Vol. 68 https://doi.org/10.1016/j.renene.2014.03.017	journal	August 2014
Wave energy resource assessment in Menorca (Spain) Sierra, J. P.; Mösso, C.; González-Marco, D. Renewable Energy, Vol. 71 https://doi.org/10.1016/j.renene.2014.05.017	journal	November 2014
Wave energy potential along the Atlantic coast of Morocco Sierra, J. P.; Martín, C.; Mösso, C. Renewable Energy, Vol. 96 https://doi.org/10.1016/j.renene.2016.04.071	journal	October 2016
Nearshore wave energy resource characterization along the East Coast of the United States Ahn, Seongho; Neary, Vincent S.; Allahdadi, Mohammad Nabi Renewable Energy, Vol. 172 https://doi.org/10.1016/j.renene.2021.03.037	journal	July 2021
Assessment of the global ocean wind energy resource Zheng, Chong wei; Pan, Jing Renewable and Sustainable Energy Reviews, Vol. 33 https://doi.org/10.1016/j.rser.2014.01.065	journal	May 2014
Wave energy resource classification system for US coastal waters Ahn, Seongho; Haas, Kevin A.; Neary, Vincent S. Renewable and Sustainable Energy Reviews, Vol. 104 https://doi.org/10.1016/j.rser.2019.01.017	journal	April 2019
An assessment of wind energy potential in the Beibu Gulf considering the energy demands of the Beibu Gulf Economic Rim Chen, Xinping; Foley, Aoife; Zhang, Zenghai Renewable and Sustainable Energy Reviews, Vol. 119 https://doi.org/10.1016/j.rser.2019.109605	journal	March 2020
Wave exploitability index and wave resource classification Martinez, A.; Iglesias, G. Renewable and Sustainable Energy Reviews, Vol. 134 https://doi.org/10.1016/j.rser.2020.110393	journal	December 2020
Brazil's offshore wind energy potential assessment based on a Spatial Multi-Criteria Decision Analysis Vinhoza, Amanda; Schaeffer, Roberto Renewable and Sustainable Energy Reviews, Vol. 146 https://doi.org/10.1016/j.rser.2021.111185	journal	August 2021
Investigation of trends in extreme value wave height and wind speed: EXTREME VALUE TRENDS Young, I. R.; Vinoth, J.; Zieger, S. Journal of Geophysical Research: Oceans, Vol. 117, Issue C11 https://doi.org/10.1029/2011JC007753	journal	March 2012
A proposed spectral form for fully developed wind seas based on the similarity theory of S. A. Kitaigorodskii Pierson, Willard J.; Moskowitz, Lionel Journal of Geophysical Research, Vol. 69, Issue 24 https://doi.org/10.1029/JZ069i024p05181	journal	December 1964
Optimal Detection of Changepoints With a Linear Computational Cost Killick, R.; Fearnhead, P.; Eckley, I. A. Journal of the American Statistical Association, Vol. 107, Issue 500 https://doi.org/10.1080/01621459.2012.737745	journal	September 2012
Watersheds in digital spaces: an efficient algorithm based on immersion simulations Vincent, L.; Soille, P. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 13, Issue 6 https://doi.org/10.1109/34.87344	journal	June 1991
Source Terms in a Third-Generation Wind Wave Model Tolman, Hendrik L.; Chalikov, Dmitry Journal of Physical Oceanography, Vol. 26, Issue 11 https://doi.org/10.1175/1520-0485(1996)026<2497:STIATG>2.0.CO;2	journal	November 1996
The NCEP Climate Forecast System Reanalysis Saha, Suranjana; Moorthi, Shrinivas; Pan, Hua-Lu Bulletin of the American Meteorological Society, Vol. 91, Issue 8 https://doi.org/10.1175/2010BAMS3001.1	journal	August 2010
Semiempirical Dissipation Source Functions for Ocean Waves. Part I: Definition, Calibration, and Validation Ardhuin, Fabrice; Rogers, Erick; Babanin, Alexander V. Journal of Physical Oceanography, Vol. 40, Issue 9 https://doi.org/10.1175/2010JPO4324.1	journal	September 2010
A review of wave energy converter technology Drew, B.; Plummer, A. R.; Sahinkaya, M. N. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, Vol. 223, Issue 8 https://doi.org/10.1243/09576509JPE782	journal	December 2009
Dominant Wave Energy Systems and Conditional Wave Resource Characterization for Coastal Waters of the United States Ahn, Seongho; Haas, Kevin A.; Neary, Vincent S. Energies, Vol. 13, Issue 12 https://doi.org/10.3390/en13123041	journal	June 2020
Characterization of Extreme Wave Conditions for Wave Energy Converter Design and Project Risk Assessment Neary, Vincent S.; Ahn, Seongho; Seng, Bibiana E. Journal of Marine Science and Engineering, Vol. 8, Issue 4 https://doi.org/10.3390/jmse8040289	journal	April 2020
Wave Climate Resource Analysis Based on a Revised Gamma Spectrum for Wave Energy Conversion Technology Pastor, Jeremiah; Liu, Yucheng Sustainability, Vol. 8, Issue 12 https://doi.org/10.3390/su8121321	journal	December 2016
Classification systems for wave energy resources and WEC technologies Neary, V. S.; Coe, R. G.; Cruz, J. International Marine Energy Journal, Vol. 1, Issue 2 (Nov) https://doi.org/10.36688/imej.1.71-79	journal	November 2018