Advertisement

Frontiers of Earth Science

, Volume 13, Issue 1, pp 132–150 | Cite as

Onshore-offshore wind energy resource evaluation based on synergetic use of multiple satellite data and meteorological stations in Jiangsu Province, China

  • Xianglin Wei
  • Yuewei Duan
  • Yongxue LiuEmail author
  • Song Jin
  • Chao Sun
Research Article
  • 28 Downloads

Abstract

The demand for efficient and cost-effective renewable energy is increasing as traditional sources of energy such as oil, coal, and natural gas, can no longer satisfy growing global energy demands. Among renewable energies, wind energy is the most prominent due to its low, manageable impacts on the local environment. Based on meteorological data from 2006 to 2014 and multi-source satellite data (i.e., Advanced Scatterometer, Quick Scatterometer, and Windsat) from 1999 to 2015, an assessment of the onshore and offshore wind energy potential in Jiangsu Province was performed by calculating the average wind speed, average wind direction, wind power density, and annual energy production (AEP). Results show that Jiangsu has abundant wind energy resources, which increase from inland to coastal areas. In onshore areas, wind power density is predominantly less than 200 W/m2, while in offshore areas, wind power density is concentrates in the range of 328–500 W/m2. Onshore areas comprise more than 13,573.24 km2, mainly located in eastern coastal regions with good wind farm potential. The total wind power capacity in onshore areas could be as much as 2.06 × 105 GWh. Meanwhile, offshore wind power generation in Jiangsu Province is calculated to reach 2 × 106 GWh, which is approximately four times the electricity demand of the entire Jiangsu Province. This study validates the effective application of Advanced Scatterometer, Quick Scatterometer, and Windsat data to coastal wind energy monitoring in Jiangsu. Moreover, the methodology used in this study can be effectively applied to other similar coastal zones.

Keywords

wind energy resource wind power density ASCAT QuikSCAT Windsat 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Notes

Acknowledgements

This research was supported by the Jiangsu Provincial Natural Science Foundation (Grant No. BK20160023) and the National Natural Science Foundation of China (Grant Nos. 41471068, 41230751, and 41171325). Note that any errors or shortcomings in the paper are the responsibility of the authors.

Supplementary material

11707_2018_699_MOESM1_ESM.pdf (1.4 mb)
Supplementary material, approximately 1.35 MB.

References

  1. Ahsbahs T, Badger M, Karagali I, Larsen X G (2017). Validation of Sentinel-1A SAR coastal wind speeds against scanning LiDAR. Remote Sens, 9(6): 552Google Scholar
  2. Alvarez I, Gomez-Gesteira M, Decastro M, Carvalho D (2014). Comparison of different wind products and buoy wind data with seasonality and interannual climate variability in the southern Bay of Biscay (2000‒2009). Deep Sea Res Part II Top Stud Oceanogr, 106: 38–48Google Scholar
  3. Anon (2004). Wind Power Outlook 2004. Office of Scientific & Technical Information Technical ReportsGoogle Scholar
  4. Archer C L, Jacobson M Z (2005). Evaluation of global wind power. J Geophys Res D Atmospheres, 110: D12110Google Scholar
  5. Barrington-Leigh C, Ouliaris M (2017). The renewable energy landscape in Canada: a spatial analysis. Renew Sustain Energy Rev, 75: 809–819Google Scholar
  6. Carvalho D, Rocha A, Gomez-Gesteira M, Alvarez I, Silva Santos C (2013). Comparison between CCMP, QuikSCAT and buoy winds along the Iberian Peninsula coast. Remote Sens Environ, 137: 173–183Google Scholar
  7. Christiansen M B, Koch W, Horstmann J, Hasager C B, Nielsen M (2006). Wind resource assessment from C-band SAR. Remote Sens Environ, 105(1): 68–81Google Scholar
  8. Coppin P, Ayotte K, Steggel N (2003). Wind resource assessment in Australia: a planners guide. CSIRO Wind Energy Research UnitGoogle Scholar
  9. Desholm M, Kahlert J (2005). Avian collision risk at an offshore wind farm. Biol Lett, 1(3): 296–298Google Scholar
  10. Doubrawa P, Barthelmie R J, Pryor S C, Hasager C B, Badger M, Karagali I (2015). Satellite winds as a tool for offshore wind resource assessment: the Great Lakes Wind Atlas. Remote Sens Environ, 168: 349–359Google Scholar
  11. Dvorak MJ, Archer C L, Jacobson MZ (2010). California offshore wind energy potential. Renew Energy, 35(6): 1244–1254Google Scholar
  12. Ebuchi N, Graber H C, Caruso M J (2002). Evaluation of wind vectors observed by QuikSCAT/SeaWinds using ocean buoy data. Journal of Atmospheric & Oceanic Technology, 19 (12): 2049–2062Google Scholar
  13. Foley A M, Leahy P G, Marvuglia A, Mckeogh E J (2012). Current methods and advances in forecasting of wind power generation. Renew Energy, 37(1): 1–8Google Scholar
  14. Frank H P, Larsen S E, Højstrup J (2015). Simulated wind power offshore using different parametrizations for the sea surface roughness. Wind Energy (Chichester Engl), 3(2): 67–79Google Scholar
  15. Hasager C B, Barthelmie R J, Christiansen M B, Nielsen M, Pryor S C (2006). Quantifying offshore wind resources from satellite wind maps: study area the North Sea. Wind Energy (Chichester Engl), 9(1‒2): 63–74Google Scholar
  16. Hasager C B, Mouche A, Badger M, Bingol F, Karagali I, Driesenaar T, Stoffelen A, Pena A, Longepe N (2015). Offshore wind climatology based on synergetic use of Envisat ASAR, ASCAT and QuikSCAT. Remote Sens Environ, 156: 247–263Google Scholar
  17. Hasager C B, Nielsen M, Astrup P, Barthelmie R, Dellwik E, Jensen N O, Jorgensen B H, Pryor S C, Rathmann O, Furevik B R (2005). Offshore resource estimation from satellite SAR wind field maps. Wind Energy (Chichester Engl), 8(4): 403–419Google Scholar
  18. Heng X, Ruizhao Z, Zhenbin Y, Chunhong Y (2001). Assessment of wind energy reserves in China. Acta Energies Solaris Sinica, 22: 167–170Google Scholar
  19. Jiang D, Zhuang D, Huang Y, Wang J, Fu J (2013). Evaluating the spatio-temporal variation of China’s offshore wind resources based on remotely sensed wind field data. Renew Sustain Energy Rev, 24: 142–148Google Scholar
  20. Jimenez B, Durante F, Lange B, Kreutzer T, Tambke J (2010). Offshore wind resource assessment with WAsP and MM5: comparative study for the German Bight. Wind Energy (Chichester Engl), 10(2): 121–134Google Scholar
  21. Kariniotakis G, Marti I, Casas D, Pinson P, Nielsen T S, Madsen H, Giebel G, Usaola J, Sanchez I, Palomares A M (2005). What performance can be expected by short-term wind power prediction models depending on site characteristics? In: Proceedings CD-ROM Brussels: European Wind Energy AssociationGoogle Scholar
  22. Lima D K S, Leao R P S, Dos Santos A C S, De Melo F D C, Couto VM, De Noronha A W T, Oliveira D S Jr (2015). Estimating the offshore wind resources of the State of Ceara in Brazil. Renew Energy, 83: 203–221Google Scholar
  23. Madders M, Whitfield D P (2006). Upland raptors and the assessment of wind farm impacts. Ibis, 148: 43–56Google Scholar
  24. Manwell J F, Rogers A L, Mcgowan J G, Bailey B H (2002). An offshore wind resource assessment study for New England. Renew Energy, 27 (2): 175–187Google Scholar
  25. Nagababu G, Kachhwaha S S, Saysani V, Banerjee R (2017). Evaluation of offshore wind power potential in the western coast of India: a preliminary study. Curr Sci, 112(1): 62–67Google Scholar
  26. Obama B (2017). The irreversible momentum of clean energy. Science, 355(6321): 126–129Google Scholar
  27. Ohunakin O S, Akinnawonu O O (2012). Assessment of wind energy potential and the economics of wind power generation in Jos, Plateau State, Nigeria. Energy Sustain Dev, 16(1): 78–83Google Scholar
  28. Pickett M H, Tang W, Rosenfeld L K, Wash C H (2003a). QuikSCAT satellite comparisons with nearshore buoy wind data off the U.S. West Coast. J Atmos Ocean Technol, 20(12): 1869–1879Google Scholar
  29. Pickett M H, Tang W, Rosenfeld L K, Wash C H (2003b). QuikSCAT satellite comparisons with nearshore buoy wind data off the US west coast. J Atmos Ocean Technol, 20(12): 1869–1879Google Scholar
  30. Pimenta F, Kempton W, Garvine R (2008). Combining meteorological stations and satellite data to evaluate the offshore wind power resource of southeastern Brazil. Renew Energy, 33(11): 2375–2387Google Scholar
  31. Purohit I, Purohit P (2009). Wind energy in India: status and future prospects. Journal of Renewable and Sustainable Energy, 1(4): 042701Google Scholar
  32. Quilfen Y, Prigent C, Chapron B, Mouche A, Houti N (2007). The potential of QuikSCAT and WindSat observations for the estimation of sea surface wind vector under severe weather conditions. J Geophys Res Oceans, 112(C9): 1–18Google Scholar
  33. Ramachandra T, Shruthi B (2005). Wind energy potential mapping in Karnataka, India, using GIS. Energy Convers Manage, 46(9): 1561–1578Google Scholar
  34. Risien C M, Chelton D B (2008). A global climatology of surface wind and wind stress fields from eight years of QuikSCAT scatterometer data. J Phys Oceanogr, 38(11): 2379–2413Google Scholar
  35. Shen G, Xu B, Jin Y X, Chen S, Zhang W B, Guo J, Liu H, Zhang Y J, Yang X C (2017). Monitoring wind farms occupying grasslands based on remote-sensing data from China’s GF-2 HD satellite—A case study of Jiuquan city, Gansu Province, China. Resour Conserv Recycling, 121: 128–136Google Scholar
  36. Shimada S, Ohsawa T (2011). Accuracy and characteristics of offshore wind speeds simulated by WRF. Scientific Online Letters on the Atmosphere Sola, 7(1): 21–24Google Scholar
  37. Sliz-Szkliniarz B, Vogt J (2011). GIS-based approach for the evaluation of wind energy potential: a case study for the Kujawsko-Pomorskie Voivodeship. Renew Sustain Energy Rev, 15(3): 1696–1707Google Scholar
  38. Tang W, Liu W T, Stiles B W (2004). Evaluation of high-resolution ocean surface vector winds measured by QuikSCAT scatterometer in coastal regions. IEEE Trans Geosci Remote Sens, 42(8): 1762–1769Google Scholar
  39. Thornton H E, Scaife A A, Hoskins B J, Brayshaw D J (2017). The relationship between wind power, electricity demand and winter weather patterns in Great Britain. Environ Res Lett, 12(6): 064017Google Scholar
  40. Troen I, Petersen E L (1989). European wind atlas.Roskilde: Riso National LaboratoryGoogle Scholar
  41. Truewind Solutions L (2001). Wind energy resource atlas of Southeast Asia. The World BankGoogle Scholar
  42. Xie X, Wei J, Huang L (2014). Comparisons of ASCATwind vectors and buoy wind data in China’s coastal waters. Journal of Applied Meteorological Science, 25(4): 445–453 (in Chinese)Google Scholar
  43. Yu W, Benoit R, Girard C, Glazer A, Lemarquis D, Salmon J R, Pinard J P (2006). Wind energy simulation toolkit (WEST): a wind mapping system for use by the wind energy industry. Wind Eng, 4034(1): 15–33Google Scholar
  44. Zhao S S, Liu Y X, Li M C, Sun C, Zhou M X, Zhang H X (2015). Analysis of Jiangsu tidal flats reclamation from 1974 to 2012 using remote sensing. China Ocean Eng, 29(1): 143–154Google Scholar
  45. Zhou Y, Wu W X, Liu G X (2011). Assessment of onshore wind energy resource and wind-generated electricity potential in Jiangsu, China. Energy Procedia, 5(1): 418–422Google Scholar
  46. Zhu R, Xue H (1983). Division of wind energy in China. Acta Energies Solaris Sinica, 4(2): 123–132Google Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Xianglin Wei
    • 1
    • 2
  • Yuewei Duan
    • 3
  • Yongxue Liu
    • 1
    • 2
    • 4
    Email author
  • Song Jin
    • 1
  • Chao Sun
    • 1
  1. 1.Department of Geographic Information ScienceNanjing UniversityNanjingChina
  2. 2.Key Laboratory of Coastal Zone Development and ProtectionMinistry of Land and Resources of ChinaNanjingChina
  3. 3.Yunnan Transportation Planning and Design InstituteKunmingChina
  4. 4.Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationNanjingChina

Personalised recommendations