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Effects of different wind data sources in offshore wind power assessment

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  • Soukissian, Takvor H.
  • Papadopoulos, Anastasios

Abstract

Currently, approximately 5.3% of electricity production in Europe comes from wind energy. The increase of the size and the improved efficiency of wind generators have permitted their utilization offshore, leading to exploitation of offshore wind energy. Although offshore wind farms are well established in northern European countries, in the Mediterranean Sea they are still in their infancy. It is expected that within the next few years, offshore wind farming will grow considerably in this area. The accurate estimation of the wind speed fields is of most importance for the assessment of offshore wind energy resources. In this work, the effects of alternative wind data sources on the wind climate analysis are examined along with the offshore wind power density estimation in four locations across the Aegean Sea. In order to develop correction relations for satellite and model wind data, taking as reference the buoy measurements, the data are analysed and calibrated using the Error-In-Variables approach. The effects of the different data sources on the wind climate analysis and the estimation of the mean wind power density before and after the calibration procedure are presented and discussed. The Error-In-Variables approach performed better and reduced significantly the uncertainties of the alternative data sources.

Suggested Citation

  • Soukissian, Takvor H. & Papadopoulos, Anastasios, 2015. "Effects of different wind data sources in offshore wind power assessment," Renewable Energy, Elsevier, vol. 77(C), pages 101-114.
    • Handle: RePEc:eee:renene:v:77:y:2015:i:c:p:101-114
    DOI: 10.1016/j.renene.2014.12.009
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    References listed on IDEAS

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    1. Al-Yahyai, Sultan & Charabi, Yassine & Al-Badi, Abdullah & Gastli, Adel, 2012. "Nested ensemble NWP approach for wind energy assessment," Renewable Energy, Elsevier, vol. 37(1), pages 150-160.
    2. Nor, Khalid Mohamed & Shaaban, Mohamed & Abdul Rahman, Hasimah, 2014. "Feasibility assessment of wind energy resources in Malaysia based on NWP models," Renewable Energy, Elsevier, vol. 62(C), pages 147-154.
    3. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
    4. Sailor, D.J & Hu, T & Li, X & Rosen, J.N, 2000. "A neural network approach to local downscaling of GCM output for assessing wind power implications of climate change," Renewable Energy, Elsevier, vol. 19(3), pages 359-378.
    5. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
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    2. 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.
    3. Florin Onea & Eugen Rusu, 2018. "Sustainability of the Reanalysis Databases in Predicting the Wind and Wave Power along the European Coasts," Sustainability, MDPI, vol. 10(1), pages 1-16, January.
    4. Elsner, Paul, 2019. "Continental-scale assessment of the African offshore wind energy potential: Spatial analysis of an under-appreciated renewable energy resource," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 394-407.
    5. Abramic, A. & García Mendoza, A. & Haroun, R., 2021. "Introducing offshore wind energy in the sea space: Canary Islands case study developed under Maritime Spatial Planning principles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 145(C).
    6. Hugo Algarvio & António Couto & Fernando Lopes & Ana Estanqueiro, 2019. "Changing the Day-Ahead Gate Closure to Wind Power Integration: A Simulation-Based Study," Energies, MDPI, vol. 12(14), pages 1-20, July.
    7. 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).
    8. Nezhad, M. Majidi & Neshat, M. & Groppi, D. & Marzialetti, P. & Heydari, A. & Sylaios, G. & Garcia, D. Astiaso, 2021. "A primary offshore wind farm site assessment using reanalysis data: a case study for Samothraki island," Renewable Energy, Elsevier, vol. 172(C), pages 667-679.
    9. 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.
    10. Dimitra G. Vagiona & Manos Kamilakis, 2018. "Sustainable Site Selection for Offshore Wind Farms in the South Aegean—Greece," Sustainability, MDPI, vol. 10(3), pages 1-18, March.
    11. Takvor H. Soukissian & Dimitra Denaxa & Flora Karathanasi & Aristides Prospathopoulos & Konstantinos Sarantakos & Athanasia Iona & Konstantinos Georgantas & Spyridon Mavrakos, 2017. "Marine Renewable Energy in the Mediterranean Sea: Status and Perspectives," Energies, MDPI, vol. 10(10), pages 1-56, September.
    12. Soukissian, Takvor H. & Karathanasi, Flora E., 2016. "On the use of robust regression methods in wind speed assessment," Renewable Energy, Elsevier, vol. 99(C), pages 1287-1298.

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