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Evaluation of the near future wave energy resources in the Black Sea under two climate scenarios

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  • Rusu, Liliana

Abstract

The objective of this study is to evaluate the near future wave power resources (2021–2050) in the Black Sea, based on the results coming from a wave modelling system forced with wind fields provided by a Regional Climate Model (RCM). Such wind fields have been produced in the framework of the EURO-CORDEX experiment. They cover the entire European domain and have a resolution of 0.11°. Two different wind fields are considered in the wave model simulations, according to two Representative Concentration Pathways (RCPs) scenarios, namely RCP4.5 and RCP8.5. The SWAN (Simulating Waves Nearshore) model, which has been implemented over entire the Black Sea basin, is considered for the wave energy predictions. Furthermore, in order to estimate the impact of climate change on the wave energy resources in the Black Sea basin, the results obtained in the near future 30-year period (covering the early 21st century) are compared with historical data. These are the wave energy resources resulted from SWAN model simulations performed also for a 30-year (1976–2005) time-slice. Thus, these historical wave energy resources are obtained using historical RCM wind fields to force the wave model. The results show an increase in the range 0.05–0.18 kW/m (a rise of 5–16%) of the mean wave power under RCP4.5 scenario in almost all the western part of the Black Sea and also in the north-eastern area, while under RCP8.5 is quite double in. On the other hand, the linear regressions fitted to the annual means computed to each 30-year time-slice in 8 reference points did not indicate significant trends.

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  • Rusu, Liliana, 2019. "Evaluation of the near future wave energy resources in the Black Sea under two climate scenarios," Renewable Energy, Elsevier, vol. 142(C), pages 137-146.
    • Handle: RePEc:eee:renene:v:142:y:2019:i:c:p:137-146
    DOI: 10.1016/j.renene.2019.04.092
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    3. Kamranzad, Bahareh & Lin, Pengzhi & Iglesias, Gregorio, 2021. "Combining methodologies on the impact of inter and intra-annual variation of wave energy on selection of suitable location and technology," Renewable Energy, Elsevier, vol. 172(C), pages 697-713.
    4. Bingölbali, Bilal & Majidi, Ajab Gul & Akpınar, Adem, 2021. "Inter- and intra-annual wave energy resource assessment in the south-western Black Sea coast," Renewable Energy, Elsevier, vol. 169(C), pages 809-819.
    5. 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).
    6. Arguilé-Pérez, B. & Ribeiro, A.S. & Costoya, X. & deCastro, M. & Gómez-Gesteira, M., 2023. "Suitability of wave energy converters in northwestern Spain under the near future winter wave climate," Energy, Elsevier, vol. 278(PB).
    7. Rusu, Liliana, 2020. "A projection of the expected wave power in the Black Sea until the end of the 21st century," Renewable Energy, Elsevier, vol. 160(C), pages 136-147.
    8. Aydoğan, Burak & Görmüş, Tahsin & Ayat, Berna & Çarpar, Tunay, 2021. "Analysis of potential changes in the Black Sea wave power for the 21st century," Renewable Energy, Elsevier, vol. 169(C), pages 512-526.
    9. Américo S. Ribeiro & Maite deCastro & Liliana Rusu & Mariana Bernardino & João M. Dias & Moncho Gomez-Gesteira, 2020. "Evaluating the Future Efficiency of Wave Energy Converters along the NW Coast of the Iberian Peninsula," Energies, MDPI, vol. 13(14), pages 1-15, July.
    10. 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).
    11. 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.
    12. Pourali, Mahmoud & Kavianpour, Mohamad Reza & Kamranzad, Bahareh & Alizadeh, Mohamad Javad, 2023. "Future variability of wave energy in the Gulf of Oman using a high resolution CMIP6 climate model," Energy, Elsevier, vol. 262(PB).

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