IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v133y2019icp343-353.html
   My bibliography  Save this article

Assessment of surface wind datasets for estimating offshore wind energy along the Central California Coast

Author

Listed:
  • Wang, Yi-Hui
  • Walter, Ryan K.
  • White, Crow
  • Farr, Hayley
  • Ruttenberg, Benjamin I.

Abstract

In the United States, Central California has gained significant interest in offshore wind energy due to its strong winds and proximity to existing grid connections. This study provides a comprehensive evaluation of near-surface wind datasets in this region, including satellite-based observations (QuikSCAT, ASCAT, and CCMP V2.0), reanalysis (NARR and MERRA), and regional atmospheric models (WRF and WIND Toolkit). This work highlights spatiotemporal variations in the performance of the respective datasets in relation to in-situ buoy measurements using error metrics over both seasonal and diurnal time scales. The two scatterometers (QuikSCAT and ASCAT) showed the best overall performance, albeit with significantly less spatial and temporal resolution relative to other datasets. These datasets only slightly outperformed the next best dataset (WIND Toolkit), which has significantly greater temporal and spatial resolution as well as estimates of winds aloft. Considering tradeoffs between spatiotemporal resolution of the underlying datasets, error metrics relative to in-situ measurements, and the availability of data aloft, the WIND Toolkit appears to be the best dataset for this region. The framework and tradeoff analysis this research developed and demonstrated to assess offshore wind datasets can be applied in other regions where offshore wind energy is being considered.

Suggested Citation

  • Wang, Yi-Hui & Walter, Ryan K. & White, Crow & Farr, Hayley & Ruttenberg, Benjamin I., 2019. "Assessment of surface wind datasets for estimating offshore wind energy along the Central California Coast," Renewable Energy, Elsevier, vol. 133(C), pages 343-353.
    • Handle: RePEc:eee:renene:v:133:y:2019:i:c:p:343-353
    DOI: 10.1016/j.renene.2018.10.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148118311923
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2018.10.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ingeborg Graabak & Magnus Korpås, 2016. "Variability Characteristics of European Wind and Solar Power Resources—A Review," Energies, MDPI, vol. 9(6), pages 1-31, June.
    2. S. E. Lester & J. M. Stevens & R. R. Gentry & C. V. Kappel & T. W. Bell & C. J. Costello & S. D. Gaines & D. A. Kiefer & C. C. Maue & J. E. Rensel & R. D. Simons & L. Washburn & C. White, 2018. "Marine spatial planning makes room for offshore aquaculture in crowded coastal waters," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    3. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    4. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2017. "Offshore winds and wind energy production estimates derived from ASCAT, OSCAT, numerical weather prediction models and buoys – A comparative study for the Iberian Peninsula Atlantic coast," Renewable Energy, Elsevier, vol. 102(PB), pages 433-444.
    5. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    6. Sun, Xiaojing & Huang, Diangui & Wu, Guoqing, 2012. "The current state of offshore wind energy technology development," Energy, Elsevier, vol. 41(1), pages 298-312.
    7. Lester, Sarah E. & Costello, Christopher & Halpern, Benjamin S. & Gaines, Steven D. & White, Crow & Barth, John A., 2013. "Evaluating tradeoffs among ecosystem services to inform marine spatial planning," Marine Policy, Elsevier, vol. 38(C), pages 80-89.
    8. Sharp, Ed & Dodds, Paul & Barrett, Mark & Spataru, Catalina, 2015. "Evaluating the accuracy of CFSR reanalysis hourly wind speed forecasts for the UK, using in situ measurements and geographical information," Renewable Energy, Elsevier, vol. 77(C), pages 527-538.
    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. Costoya, X. & deCastro, M. & Carvalho, D. & Gómez-Gesteira, M., 2020. "On the suitability of offshore wind energy resource in the United States of America for the 21st century," Applied Energy, Elsevier, vol. 262(C).
    2. Christina Ortega & Amin Younes & Mark Severy & Charles Chamberlin & Arne Jacobson, 2020. "Resource and Load Compatibility Assessment of Wind Energy Offshore of Humboldt County, California," Energies, MDPI, vol. 13(21), pages 1-27, October.
    3. Travis C. Douville & Dhruv Bhatnagar, 2021. "Exploring the Grid Value of Offshore Wind Energy in Oregon," Energies, MDPI, vol. 14(15), pages 1-16, July.
    4. Markus Gross & Vanesa Magar & Alfredo Peña, 2020. "The Effect of Averaging, Sampling, and Time Series Length on Wind Power Density Estimations," Sustainability, MDPI, vol. 12(8), pages 1-13, April.
    5. González-Alonso de Linaje, N. & Mattar, C. & Borvarán, D., 2019. "Quantifying the wind energy potential differences using different WRF initial conditions on Mediterranean coast of Chile," Energy, Elsevier, vol. 188(C).
    6. Majidi Nezhad, M. & Heydari, A. & Groppi, D. & Cumo, F. & Astiaso Garcia, D., 2020. "Wind source potential assessment using Sentinel 1 satellite and a new forecasting model based on machine learning: A case study Sardinia islands," Renewable Energy, Elsevier, vol. 155(C), pages 212-224.
    7. Minhyeop Kang & Kyungnam Ko & Minyeong Kim, 2020. "Verification of the Reliability of Offshore Wind Resource Prediction Using an Atmosphere–Ocean Coupled Model," Energies, MDPI, vol. 13(1), pages 1-15, January.
    8. Quetzalcoatl Hernandez-Escobedo & Javier Garrido & Fernando Rueda-Martinez & Gerardo Alcalá & Alberto-Jesus Perea-Moreno, 2019. "Wind Power Cogeneration to Reduce Peak Electricity Demand in Mexican States Along the Gulf of Mexico," Energies, MDPI, vol. 12(12), pages 1-22, June.
    9. Nikolaos Kokkos & Maria Zoidou & Konstantinos Zachopoulos & Meysam Majidi Nezhad & Davide Astiaso Garcia & Georgios Sylaios, 2021. "Wind Climate and Wind Power Resource Assessment Based on Gridded Scatterometer Data: A Thracian Sea Case Study," Energies, MDPI, vol. 14(12), pages 1-16, June.
    10. Katikas, Loukas & Dimitriadis, Panayiotis & Koutsoyiannis, Demetris & Kontos, Themistoklis & Kyriakidis, Phaedon, 2021. "A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series," Applied Energy, Elsevier, vol. 295(C).
    11. Rabbani, R. & Zeeshan, M., 2020. "Exploring the suitability of MERRA-2 reanalysis data for wind energy estimation, analysis of wind characteristics and energy potential assessment for selected sites in Pakistan," Renewable Energy, Elsevier, vol. 154(C), pages 1240-1251.
    12. He, Junyi & Chan, P.W. & Li, Qiusheng & Lee, C.W., 2020. "Spatiotemporal analysis of offshore wind field characteristics and energy potential in Hong Kong," Energy, Elsevier, vol. 201(C).

    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. Sward, J.A. & Ault, T.R. & Zhang, K.M., 2023. "Spatial biases revealed by LiDAR in a multiphysics WRF ensemble designed for offshore wind," Energy, Elsevier, vol. 262(PA).
    2. Kitzing, Lena, 2014. "Risk implications of renewable support instruments: Comparative analysis of feed-in tariffs and premiums using a mean–variance approach," Energy, Elsevier, vol. 64(C), pages 495-505.
    3. González-Alonso de Linaje, N. & Mattar, C. & Borvarán, D., 2019. "Quantifying the wind energy potential differences using different WRF initial conditions on Mediterranean coast of Chile," Energy, Elsevier, vol. 188(C).
    4. Gualtieri, G., 2022. "Analysing the uncertainties of reanalysis data used for wind resource assessment: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    5. Costoya, X. & deCastro, M. & Carvalho, D. & Gómez-Gesteira, M., 2020. "On the suitability of offshore wind energy resource in the United States of America for the 21st century," Applied Energy, Elsevier, vol. 262(C).
    6. Nagababu, Garlapati & Kachhwaha, Surendra Singh & Naidu, Natansh K. & Savsani, Vimal, 2017. "Application of reanalysis data to estimate offshore wind potential in EEZ of India based on marine ecosystem considerations," Energy, Elsevier, vol. 118(C), pages 622-631.
    7. Akosah, Nana Kwame & Alagidede, Imhotep Paul & Schaling, Eric, 2020. "Testing for asymmetry in monetary policy rule for small-open developing economies: Multiscale Bayesian quantile evidence from Ghana," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    8. Plain, N. & Hingray, B. & Mathy, S., 2019. "Accounting for low solar resource days to size 100% solar microgrids power systems in Africa," Renewable Energy, Elsevier, vol. 131(C), pages 448-458.
    9. Cui, Xueting & Zhu, Shushang & Sun, Xiaoling & Li, Duan, 2013. "Nonlinear portfolio selection using approximate parametric Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2124-2139.
    10. Pichler, Anton & Poledna, Sebastian & Thurner, Stefan, 2021. "Systemic risk-efficient asset allocations: Minimization of systemic risk as a network optimization problem," Journal of Financial Stability, Elsevier, vol. 52(C).
    11. Peter A. Abken & Milind M. Shrikhande, 1997. "The role of currency derivatives in internationally diversified portfolios," Economic Review, Federal Reserve Bank of Atlanta, vol. 82(Q 3), pages 34-59.
    12. Dhanya Jothimani & Ravi Shankar & Surendra S. Yadav, 2018. "A Big data analytical framework for portfolio optimization," Papers 1811.07188, arXiv.org, revised Nov 2018.
    13. Leonard J. Mirman & Egas M. Salgueiro & Marc Santugini, 2013. "Integrating Real and Financial Decisions of the Firm," Cahiers de recherche 1333, CIRPEE.
    14. Dominique Guégan & Wayne Tarrant, 2012. "On the necessity of five risk measures," Annals of Finance, Springer, vol. 8(4), pages 533-552, November.
    15. Andriosopoulos, Kostas & Nomikos, Nikos, 2014. "Performance replication of the Spot Energy Index with optimal equity portfolio selection: Evidence from the UK, US and Brazilian markets," European Journal of Operational Research, Elsevier, vol. 234(2), pages 571-582.
    16. Raffestin, Louis, 2014. "Diversification and systemic risk," Journal of Banking & Finance, Elsevier, vol. 46(C), pages 85-106.
    17. Sridhar, Shrihari & Naik, Prasad A. & Kelkar, Ajay, 2017. "Metrics unreliability and marketing overspending," International Journal of Research in Marketing, Elsevier, vol. 34(4), pages 761-779.
    18. Vithayasrichareon, Peerapat & MacGill, Iain F., 2013. "Assessing the value of wind generation in future carbon constrained electricity industries," Energy Policy, Elsevier, vol. 53(C), pages 400-412.
    19. Gruber, Lutz F. & West, Mike, 2017. "Bayesian online variable selection and scalable multivariate volatility forecasting in simultaneous graphical dynamic linear models," Econometrics and Statistics, Elsevier, vol. 3(C), pages 3-22.
    20. repec:dau:papers:123456789/2256 is not listed on IDEAS
    21. Gupta, Pankaj & Mittal, Garima & Mehlawat, Mukesh Kumar, 2013. "Expected value multiobjective portfolio rebalancing model with fuzzy parameters," Insurance: Mathematics and Economics, Elsevier, vol. 52(2), pages 190-203.

    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:eee:renene:v:133:y:2019:i:c:p:343-353. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.