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A Joint Evaluation of the Wind and Wave Energy Resources Close to the Greek Islands

Author

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  • Daniel Ganea

    (Department of Mechanical Engineering, Faculty of Engineering, “Dunărea de Jos” University of Galati, 47 Domneasca Street, Galati 800008, Romania)

  • Valentin Amortila

    (Department of Mechanical Engineering, Faculty of Engineering, “Dunărea de Jos” University of Galati, 47 Domneasca Street, Galati 800008, Romania)

  • Elena Mereuta

    (Department of Mechanical Engineering, Faculty of Engineering, “Dunărea de Jos” University of Galati, 47 Domneasca Street, Galati 800008, Romania)

  • Eugen Rusu

    (Department of Mechanical Engineering, Faculty of Engineering, “Dunărea de Jos” University of Galati, 47 Domneasca Street, Galati 800008, Romania)

Abstract

The objective of this work is to analyze the wind and wave energy potential in the proximity of the Greek islands. Thus, by evaluating the synergy between wind and waves, a more comprehensive picture of the renewable energy resources in the target area is provided. In this study, two different data sources are considered. The first data set is provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) through the ERA-Interim project and covers an 11-year period, while the second data set is Archiving, Validation and Interpretation of Satellite Oceanographic data (AVISO) and covers six years of information. Using these data, parameters such as wind speed, significant wave height (SWH) and mean wave period (MWP) are analyzed. The following marine areas are targeted: Ionian Sea, Aegean Sea, Sea of Crete, Libyan Sea and Levantine Sea, near the coastal environment of the Greek islands. Initially, 26 reference points were considered. For a more detailed analysis, the number of reference points was narrowed down to 10 that were considered more relevant. Since in the island environments the resources are in general rather limited, the proposed work provides some outcomes concerning the wind and wave energy potential and the synergy between these two natural resources in the vicinity of the Greek islands. From the analysis performed, it can be noticed that the most energetic wind conditions are encountered west of Cios Island, followed by the regions east of Tinos and northeast of Crete. In these locations, the annual average values of the wind power density (Pwind) are in the range of 286–298.6 W/m 2 . Regarding the wave power density (Pwave), the most energetic locations can be found in the vicinity of Crete, north, south and southeast of the island. There, the wave energy potential is in the range of 2.88–2.99 kW/m.

Suggested Citation

  • Daniel Ganea & Valentin Amortila & Elena Mereuta & Eugen Rusu, 2017. "A Joint Evaluation of the Wind and Wave Energy Resources Close to the Greek Islands," Sustainability, MDPI, vol. 9(6), pages 1-22, June.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:6:p:1025-:d:101588
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    References listed on IDEAS

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    Cited by:

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    6. Daniel Ganea & Elena Mereuta & Liliana Rusu, 2018. "Estimation of the Near Future Wind Power Potential in the Black Sea," Energies, MDPI, vol. 11(11), pages 1-21, November.
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    9. Rusu, Eugen & Onea, Florin, 2019. "An assessment of the wind and wave power potential in the island environment," Energy, Elsevier, vol. 175(C), pages 830-846.
    10. Rusu, Liliana, 2019. "The wave and wind power potential in the western Black Sea," Renewable Energy, Elsevier, vol. 139(C), pages 1146-1158.
    11. Herrero-Novoa, Cristina & Pérez, Isidro A. & Sánchez, M. Luisa & García, Ma Ángeles & Pardo, Nuria & Fernández-Duque, Beatriz, 2017. "Wind speed description and power density in northern Spain," Energy, Elsevier, vol. 138(C), pages 967-976.
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    13. 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.
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    15. 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.
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    18. Jianzhou Wang & Chunying Wu & Tong Niu, 2019. "A Novel System for Wind Speed Forecasting Based on Multi-Objective Optimization and Echo State Network," Sustainability, MDPI, vol. 11(2), pages 1-34, January.

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