IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i12p3448-d572916.html
   My bibliography  Save this article

Wind Climate and Wind Power Resource Assessment Based on Gridded Scatterometer Data: A Thracian Sea Case Study

Author

Listed:
  • Nikolaos Kokkos

    (Laboratory of Ecological Engineering and Technology, Department of Environmental Engineering, Democritus University of Thrace, 67100 Xanthi, Greece)

  • Maria Zoidou

    (Laboratory of Ecological Engineering and Technology, Department of Environmental Engineering, Democritus University of Thrace, 67100 Xanthi, Greece)

  • Konstantinos Zachopoulos

    (Laboratory of Ecological Engineering and Technology, Department of Environmental Engineering, Democritus University of Thrace, 67100 Xanthi, Greece)

  • Meysam Majidi Nezhad

    (Department of Astronautics, Electrical and Energy Engineering (DIAEE), Sapienza University of Rome, 00184 Rome, Italy)

  • Davide Astiaso Garcia

    (Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome, 00197 Rome, Italy)

  • Georgios Sylaios

    (Laboratory of Ecological Engineering and Technology, Department of Environmental Engineering, Democritus University of Thrace, 67100 Xanthi, Greece)

Abstract

The present analysis utilized the 6-hourly data of wind speed (zonal and meridional) for the period between 2011 and 2019, as retrieved from the Copernicus Marine Environmental Service (CMEMS), covering the Thracian Sea (the northern part of the Aegean Sea). Data were estimated from the global wind fields derived from the Advanced Scatterometer (ASCAT) L2b scatterometer on-board Meteorological Operational (METOP) satellites, and then processed towards the equivalent neutral-stability 10 m winds with a spatial resolution of 0.25° × 0.25°. The analysis involved: (a) descriptive statistics on wind speed and direction data; (b) frequency distributions of daily-mean wind speeds per wind direction sector; (c) total wind energy content assessment per wind speed increment and per sector; (d) total annual wind energy production (in MWh/yr); and (e) wind power density, probability density function, and Weibull wind speed distribution, together with the relevant dimensionless shape and scale parameters. Our results show that the Lemnos Plateau has the highest total wind energy content (4455 kWh/m 2 /yr). At the same time, the area to the SW of the Dardanelles exhibits the highest wind energy capacity factor (~37.44%), producing 7546 MWh/yr. This indicates that this zone could harvest wind energy through wind turbines, having an efficiency in energy production of 37%. Lower capacity factors of 24–28% were computed at the nearshore Thracian Sea zone, producing between 3000 and 5600 MWh/yr.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3448-:d:572916
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/12/3448/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/12/3448/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fyrippis, Ioannis & Axaopoulos, Petros J. & Panayiotou, Gregoris, 2010. "Wind energy potential assessment in Naxos Island, Greece," Applied Energy, Elsevier, vol. 87(2), pages 577-586, February.
    2. Mostafaeipour, Ali, 2010. "Feasibility study of offshore wind turbine installation in Iran compared with the world," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 1722-1743, September.
    3. Pimenta, Felipe & Kempton, Willett & Garvine, Richard, 2008. "Combining meteorological stations and satellite data to evaluate the offshore wind power resource of Southeastern Brazil," Renewable Energy, Elsevier, vol. 33(11), pages 2375-2387.
    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. Latinopoulos, D. & Kechagia, K., 2015. "A GIS-based multi-criteria evaluation for wind farm site selection. A regional scale application in Greece," Renewable Energy, Elsevier, vol. 78(C), pages 550-560.
    6. Arun Kumar, Surisetty V.V. & Nagababu, Garlapati & Kumar, Raj, 2019. "Comparative study of offshore winds and wind energy production derived from multiple scatterometers and met buoys," Energy, Elsevier, vol. 185(C), pages 599-611.
    7. 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.
    8. Borhan, Y., 1998. "Mesoscale interactions on wind energy potential in the northern Aegean region: a case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 2(4), pages 353-360, December.
    9. 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.
    10. Satir, Mert & Murphy, Fionnuala & McDonnell, Kevin, 2018. "Feasibility study of an offshore wind farm in the Aegean Sea, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2552-2562.
    11. Marie Laure Delignette-Muller & Christophe Dutang, 2015. "fitdistrplus : An R Package for Fitting Distributions," Post-Print hal-01616147, HAL.
    12. Karamanis, D. & Tsabaris, C. & Stamoulis, K. & Georgopoulos, D., 2011. "Wind energy resources in the Ionian Sea," Renewable Energy, Elsevier, vol. 36(2), pages 815-822.
    13. Delignette-Muller, Marie Laure & Dutang, Christophe, 2015. "fitdistrplus: An R Package for Fitting Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i04).
    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. Benedetto Nastasi & Meysam Majidi Nezhad, 2021. "GIS and Remote Sensing for Renewable Energy Assessment and Maps," Energies, MDPI, vol. 15(1), pages 1-3, December.
    2. Nasser Shahsavari-Pour & Sadegh Bahador & Azim Heydari & Afef Fekih, 2022. "Analyzing Tehran’s Air Pollution Using System Dynamics Approach," Sustainability, MDPI, vol. 14(3), pages 1-16, January.

    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. 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).
    2. Jiang, Dong & Zhuang, Dafang & Huang, Yaohuan & Wang, Jianhua & Fu, Jingying, 2013. "Evaluating the spatio-temporal variation of China's offshore wind resources based on remotely sensed wind field data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 142-148.
    3. Schulte, Benedikt & Sachs, Anna-Lena, 2020. "The price-setting newsvendor with Poisson demand," European Journal of Operational Research, Elsevier, vol. 283(1), pages 125-137.
    4. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.
    5. Avanzi, Benjamin & Taylor, Greg & Wang, Melantha & Wong, Bernard, 2021. "SynthETIC: An individual insurance claim simulator with feature control," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 296-308.
    6. K. G. Reddy & M. G. M. Khan, 2020. "stratifyR: An R Package for optimal stratification and sample allocation for univariate populations," Australian & New Zealand Journal of Statistics, Australian Statistical Publishing Association Inc., vol. 62(3), pages 383-405, September.
    7. Chen, Shang & He, Liang & Cao, Yinxuan & Wang, Runhong & Wu, Lianhai & Wang, Zhao & Zou, Yufeng & Siddique, Kadambot H.M. & Xiong, Wei & Liu, Manshuang & Feng, Hao & Yu, Qiang & Wang, Xiaoming & He, J, 2021. "Comparisons among four different upscaling strategies for cultivar genetic parameters in rainfed spring wheat phenology simulations with the DSSAT-CERES-Wheat model," Agricultural Water Management, Elsevier, vol. 258(C).
    8. Riva-Palacio, Alan & Leisen, Fabrizio, 2021. "Compound vectors of subordinators and their associated positive Lévy copulas," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    9. Islam, M.R. & Saidur, R. & Rahim, N.A., 2011. "Assessment of wind energy potentiality at Kudat and Labuan, Malaysia using Weibull distribution function," Energy, Elsevier, vol. 36(2), pages 985-992.
    10. Minji Lee & Sun Ju Chung & Youngjo Lee & Sera Park & Jun-Gun Kwon & Dai Jin Kim & Donghwan Lee & Jung-Seok Choi, 2020. "Investigation of Correlated Internet and Smartphone Addiction in Adolescents: Copula Regression Analysis," IJERPH, MDPI, vol. 17(16), pages 1-12, August.
    11. Veronesi, F. & Grassi, S. & Raubal, M., 2016. "Statistical learning approach for wind resource assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 836-850.
    12. Tuy, Soklin & Lee, Han Soo & Chreng, Karodine, 2022. "Integrated assessment of offshore wind power potential using Weather Research and Forecast (WRF) downscaling with Sentinel-1 satellite imagery, optimal sites, annual energy production and equivalent C," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    13. Phillip M. Gurman & Tom Ross & Andreas Kiermeier, 2018. "Quantitative Microbial Risk Assessment of Salmonellosis from the Consumption of Australian Pork: Minced Meat from Retail to Burgers Prepared and Consumed at Home," Risk Analysis, John Wiley & Sons, vol. 38(12), pages 2625-2645, December.
    14. Adam R. Martin & Rachel O. Mariani & Kimberley A. Cathline & Michael Duncan & Nicholas J. Paroshy & Gavin Robertson, 2022. "Soil Compaction Drives an Intra-Genotype Leaf Economics Spectrum in Wine Grapes," Agriculture, MDPI, vol. 12(10), pages 1-16, October.
    15. El Alimi, Souheil & Maatallah, Taher & Dahmouni, Anouar Wajdi & Ben Nasrallah, Sassi, 2012. "Modeling and investigation of the wind resource in the gulf of Tunis, Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 5466-5478.
    16. Liang Cui & Ye Xu & Ling Xu & Guohe Huang, 2021. "Wind Farm Location Special Optimization Based on Grid GIS and Choquet Fuzzy Integral Method in Dalian City, China," Energies, MDPI, vol. 14(9), pages 1-13, April.
    17. Bahaj, AbuBakr S. & Mahdy, Mostafa & Alghamdi, Abdulsalam S. & Richards, David J., 2020. "New approach to determine the Importance Index for developing offshore wind energy potential sites: Supported by UK and Arabian Peninsula case studies," Renewable Energy, Elsevier, vol. 152(C), pages 441-457.
    18. Héctor Nájera & David Gordon, 2023. "A Monte Carlo Study of Some Empirical Methods to Find the Optimal Poverty Line in Multidimensional Poverty Measurement," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 167(1), pages 391-419, June.
    19. Athanasios Zisos & Georgia-Konstantina Sakki & Andreas Efstratiadis, 2023. "Mixing Renewable Energy with Pumped Hydropower Storage: Design Optimization under Uncertainty and Other Challenges," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    20. Valentas Gruzauskas & Aurelija Burinskiene & Andrius Krisciunas, 2023. "Application of Information-Sharing for Resilient and Sustainable Food Delivery in Last-Mile Logistics," Mathematics, MDPI, vol. 11(2), pages 1-21, January.

    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:3448-:d:572916. 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.