IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i12p3482-d573622.html
   My bibliography  Save this article

Modeling Sea Ice Effects for Wave Energy Resource Assessments

Author

Listed:
  • Ruth Branch

    (Pacific Northwest National Laboratory, Coastal Sciences Division, Seattle, WA 98109, USA)

  • Gabriel García-Medina

    (Pacific Northwest National Laboratory, Coastal Sciences Division, Seattle, WA 98109, USA)

  • Zhaoqing Yang

    (Pacific Northwest National Laboratory, Coastal Sciences Division, Seattle, WA 98109, USA
    Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA)

  • Taiping Wang

    (Pacific Northwest National Laboratory, Coastal Sciences Division, Seattle, WA 98109, USA)

  • Fadia Ticona Rollano

    (Pacific Northwest National Laboratory, Coastal Sciences Division, Seattle, WA 98109, USA)

  • Lucia Hosekova

    (Applied Physics Laboratory, University of Washington, Seattle, WA 98105, USA)

Abstract

Wave-generated power has potential as a valuable coastal resource, but the wave climate needs to be mapped for feasibility before wave energy converters are installed. Numerical models are used for wave resource assessments to quantify the amount of available power and its seasonality. Alaska is the U.S. state with the longest coastline and has extensive wave resources, but it is affected by seasonal sea ice that dampens the wave energy and the full extent of this dampening is unknown. To accurately characterize the wave resource in regions that experience seasonal sea ice, coastal wave models must account for these effects. The aim of this study is to determine how the dampening effects of sea ice change wave energy resource assessments in the nearshore. Here, we show that by combining high-resolution sea ice imagery with a sea ice/wave dampening parameterization in an unstructured grid, the Simulating Waves Nearshore (SWAN) model improves wave height predictions and demonstrates the extent to which wave power decreases when sea ice is present. The sea ice parametrization decreases the bias and root mean square errors of wave height comparisons with two wave buoys and predicts a decrease in the wave power of up to 100 kW/m in areas around Prince William Sound, Alaska. The magnitude of the improvement of the model/buoy comparison depends on the coefficients used to parameterize the wave–ice interaction.

Suggested Citation

  • Ruth Branch & Gabriel García-Medina & Zhaoqing Yang & Taiping Wang & Fadia Ticona Rollano & Lucia Hosekova, 2021. "Modeling Sea Ice Effects for Wave Energy Resource Assessments," Energies, MDPI, vol. 14(12), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3482-:d:573622
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/12/3482/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/12/3482/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stopa, Justin E. & Cheung, Kwok Fai & Chen, Yi-Leng, 2011. "Assessment of wave energy resources in Hawaii," Renewable Energy, Elsevier, vol. 36(2), pages 554-567.
    2. Canals Silander, Miguel F. & García Moreno, Carlos G., 2019. "On the spatial distribution of the wave energy resource in Puerto Rico and the United States Virgin Islands," Renewable Energy, Elsevier, vol. 136(C), pages 442-451.
    3. García-Medina, Gabriel & Yang, Zhaoqing & Wu, Wei-Cheng & Wang, Taiping, 2021. "Wave resource characterization at regional and nearshore scales for the U.S. Alaska coast based on a 32-year high-resolution hindcast," Renewable Energy, Elsevier, vol. 170(C), pages 595-612.
    4. Chang, Grace & Jones, Craig A. & Roberts, Jesse D. & Neary, Vincent S., 2018. "A comprehensive evaluation of factors affecting the levelized cost of wave energy conversion projects," Renewable Energy, Elsevier, vol. 127(C), pages 344-354.
    5. Yang, Zhaoqing & García-Medina, Gabriel & Wu, Wei-Cheng & Wang, Taiping, 2020. "Characteristics and variability of the nearshore wave resource on the U.S. West Coast," Energy, Elsevier, vol. 203(C).
    6. Zhang, Na & Li, Shuai & Wu, Yongsheng & Wang, Keh-Han & Zhang, Qinghe & You, Zai-Jin & Wang, Jin, 2020. "Effects of sea ice on wave energy flux distribution in the Bohai Sea," Renewable Energy, Elsevier, vol. 162(C), pages 2330-2343.
    7. 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).
    8. Li, Ning & García-Medina, Gabriel & Cheung, Kwok Fai & Yang, Zhaoqing, 2021. "Wave energy resources assessment for the multi-modal sea state of Hawaii," Renewable Energy, Elsevier, vol. 174(C), pages 1036-1055.
    9. Ahn, Seongho & Neary, Vincent S. & Allahdadi, Mohammad Nabi & He, Ruoying, 2021. "Nearshore wave energy resource characterization along the East Coast of the United States," Renewable Energy, Elsevier, vol. 172(C), pages 1212-1224.
    10. Wu, Wei-Cheng & Wang, Taiping & Yang, Zhaoqing & García-Medina, Gabriel, 2020. "Development and validation of a high-resolution regional wave hindcast model for U.S. West Coast wave resource characterization," Renewable Energy, Elsevier, vol. 152(C), pages 736-753.
    11. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Zhaoqing & García Medina, Gabriel & Neary, Vincent S. & Ahn, Seongho & Kilcher, Levi & Bharath, Aidan, 2023. "Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters," Renewable Energy, Elsevier, vol. 212(C), pages 803-817.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. García Medina, Gabriel & Yang, Zhaoqing & Li, Ning & Cheung, Kwok Fai & Lutu-McMoore, Elinor, 2023. "Wave climate and energy resources in American Samoa from a 42-year high-resolution hindcast," Renewable Energy, Elsevier, vol. 210(C), pages 604-617.
    2. Seongho Ahn & Kevin A. Haas & Vincent S. Neary, 2020. "Dominant Wave Energy Systems and Conditional Wave Resource Characterization for Coastal Waters of the United States," Energies, MDPI, vol. 13(12), pages 1-26, June.
    3. Li, Ning & García Medina, Gabriel & Yang, Zhaoqing & Cheung, Kwok Fai & Hitzl, David & Chen, Yi-Leng, 2023. "Wave climate and energy resources in the Mariana Islands from a 42-year high-resolution hindcast," Renewable Energy, Elsevier, vol. 215(C).
    4. Yang, Zhaoqing & García Medina, Gabriel & Neary, Vincent S. & Ahn, Seongho & Kilcher, Levi & Bharath, Aidan, 2023. "Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters," Renewable Energy, Elsevier, vol. 212(C), pages 803-817.
    5. Coe, Ryan G. & Ahn, Seongho & Neary, Vincent S. & Kobos, Peter H. & Bacelli, Giorgio, 2021. "Maybe less is more: Considering capacity factor, saturation, variability, and filtering effects of wave energy devices," Applied Energy, Elsevier, vol. 291(C).
    6. Kilcher, Levi & García Medina, Gabriel & Yang, Zhaoqing, 2023. "A scalable wave resource assessment methodology: Application to U.S. waters," Renewable Energy, Elsevier, vol. 217(C).
    7. Rusu, Liliana, 2022. "The near future expected wave power in the coastal environment of the Iberian Peninsula," Renewable Energy, Elsevier, vol. 195(C), pages 657-669.
    8. Li, Ning & García-Medina, Gabriel & Cheung, Kwok Fai & Yang, Zhaoqing, 2021. "Wave energy resources assessment for the multi-modal sea state of Hawaii," Renewable Energy, Elsevier, vol. 174(C), pages 1036-1055.
    9. Ahn, Seongho & Haas, Kevin A. & Neary, Vincent S., 2020. "Wave energy resource characterization and assessment for coastal waters of the United States," Applied Energy, Elsevier, vol. 267(C).
    10. Delpey, Matthias & Lastiri, Ximun & Abadie, Stéphane & Roeber, Volker & Maron, Philippe & Liria, Pedro & Mader, Julien, 2021. "Characterization of the wave resource variability in the French Basque coastal area based on a high-resolution hindcast," Renewable Energy, Elsevier, vol. 178(C), pages 79-95.
    11. Ahn, Seongho & Neary, Vincent S. & Allahdadi, Mohammad Nabi & He, Ruoying, 2021. "Nearshore wave energy resource characterization along the East Coast of the United States," Renewable Energy, Elsevier, vol. 172(C), pages 1212-1224.
    12. Ahn, Seongho & Neary, Vincent S. & Haas, Kevin A., 2022. "Global wave energy resource classification system for regional energy planning and project development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    13. 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).
    14. Akdemir, Kerem Ziya & Robertson, Bryson & Oikonomou, Konstantinos & Kern, Jordan & Voisin, Nathalie & Hanif, Sarmad & Bhattacharya, Saptarshi, 2023. "Opportunities for wave energy in bulk power system operations," Applied Energy, Elsevier, vol. 352(C).
    15. Ahn, Seongho & Neary, Vincent S. & Ha, Taemin, 2023. "A practical method for modeling temporally-averaged ocean wave frequency-directional spectra for characterizing wave energy climates," Renewable Energy, Elsevier, vol. 207(C), pages 499-511.
    16. Ahn, Seongho & Neary, Vincent S., 2021. "Wave energy resource characterization employing joint distributions in frequency-direction-time domain," Applied Energy, Elsevier, vol. 285(C).
    17. Wan, Yong & Zheng, Chongwei & Li, Ligang & Dai, Yongshou & Esteban, M. Dolores & López-Gutiérrez, José-Santos & Qu, Xiaojun & Zhang, Xiaoyu, 2020. "Wave energy assessment related to wave energy convertors in the coastal waters of China," Energy, Elsevier, vol. 202(C).
    18. Choupin, O. & Pinheiro Andutta, F. & Etemad-Shahidi, A. & Tomlinson, R., 2021. "A decision-making process for wave energy converter and location pairing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    19. Lokuliyana, R.L.K. & Folley, M. & Gunawardane, S.D.G.S.P. & Wickramanayake, P.N., 2020. "Sri Lankan wave energy resource assessment and characterisation based on IEC standards," Renewable Energy, Elsevier, vol. 162(C), pages 1255-1272.
    20. Ribeiro, A.S. & deCastro, M. & Costoya, X. & Rusu, Liliana & Dias, J.M. & Gomez-Gesteira, M., 2021. "A Delphi method to classify wave energy resource for the 21st century: Application to the NW Iberian Peninsula," Energy, Elsevier, vol. 235(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3482-:d:573622. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.