Turn off MathJax
Article Contents
Chongwei Zheng. A positive trend in the stability of global offshore wind energy[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-023-2187-5
Citation: Chongwei Zheng. A positive trend in the stability of global offshore wind energy[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-023-2187-5

A positive trend in the stability of global offshore wind energy

doi: 10.1007/s13131-023-2187-5
Funds:  The Open Fund Project of Shandong Provincial Key Laboratory of Ocean Engineering, Ocean University of China under contract No. kloe201901; the Open Research Fund of State Key Laboratory of Estuarine and Coastal Research under contract SKLEC-KF201707.
More Information
  • The recognition on the trend of wind energy stability is still extremely rare, although it is closely related to acquisition efficiency, grid connection, equipment lifetime, and costs of wind energy utilization. Using the 40-year (1979–2018) ERA-Interim data from the European Center for Medium-Range Weather Forecasts, this study presented the spatial-temporal distribution and climatic trend of the stability of global offshore wind energy as well as the abrupt phenomenon of wind energy stability in key regions over the past 40 years with the climatic analysis method and Mann-Kendall (M-K) test. The results show the following 5 points. (1) According to the coefficient of variation (Cv) of the wind power density, there are six permanent stable zones of global offshore wind energy: the southeast and northeast trade wind zones in the Indian, Pacific and Atlantic Oceans, the Southern Hemisphere westerly, and a semi-permanent stable zone (North Indian Ocean). (2) There are six low-value zones for both seasonal variability index (Sv) and monthly variability index (Mv) globally, with a similar spatial distribution as that of the six permanent stable zones. Mv and Sv in the Arabian Sea are the highest in the world. (3) After Cv, Mv and Sv are comprehensively considered, the six permanent stable zones have an obvious advantage in the stability of wind energy over other sea areas, with Cv below 0.8, Mv within 1.0, and Sv within 0.7 all the year round. (4) The global stability of offshore wind energy shows a positive climatic trend for the past four decades. Cv, Mv and Sv have not changed significantly or decreased in most of the global ocean during 1979 to 2018. That is, wind energy is flat or more stable, while the monthly and seasonal variabilities tend to shrink/smooth, which is beneficial for wind energy utilization. (5) Cv in the low-latitude Pacific and Mv and Sv in both the North Indian and the low-latitude Pacific have an obvious abrupt phenomenon at the end of the 20th century.
  • loading
  • Allen D J, Tomlin A S, Bale C S E, et al. 2017. A boundary layer scaling technique for estimating near-surface wind energy using numerical weather prediction and wind map data. Applied Energy, 208: 1246–1257. doi: 10.1016/j.apenergy.2017.09.029
    Allouhi A, Zamzoum O, Islam M R, et al. 2017. Evaluation of wind energy potential in Morocco’s coastal regions. Renewable and Sustainable Energy Reviews, 72: 311–324. doi: 10.1016/j.rser.2017.01.047
    Capps S B, Zender C S. 2010. Estimated global ocean wind power potential from QuikSCAT observations, accounting for turbine characteristics and siting. Journal of Geophysical Research: Atmospheres, 115(D9): D09101. doi: 10.1029/2009JD012679
    Carvalho D, Rocha A, Gómez-Gesteira M, et al. 2014. WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal. Applied Energy, 117: 116–126. doi: 10.1016/j.apenergy.2013.12.001
    Carvalho D, Rocha A, Gómez-Gesteira M, et al. 2017a. 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, 102: 433–444. doi: 10.1016/j.renene.2016.10.063
    Carvalho D, Rocha A, Gómez-Gesteira M, et al. 2017b. Potential impacts of climate change on European wind energy resource under the CMIP5 future climate projections. Renewable Energy, 101: 29–40. doi: 10.1016/j.renene.2016.08.036
    Carvalho D, Rocha A, Costoya X, et al. 2021. Wind energy resource over Europe under CMIP6 future climate projections: What changes from CMIP5 to CMIP6. Renewable and Sustainable Energy Reviews, 151: 111594. doi: 10.1016/j.rser.2021.111594
    Chadee X T, Clarke R M. 2014. Large-scale wind energy potential of the Caribbean region using near-surface reanalysis data. Renewable and Sustainable Energy Reviews, 30: 45–58. doi: 10.1016/j.rser.2013.09.018
    Cornett A M. 2008. A global wave energy resource assessment. In: Proceedings of the 18th International Offshore and Polar Engineering Conference. Vancouver, Canada: International Society of Offshore and Polar Engineers, 318–326
    Costoya X, deCastro M, Carvalho D, et al. 2021. Climate change impacts on the future offshore wind energy resource in China. Renewable Energy, 175: 731–747. doi: 10.1016/j.renene.2021.05.001
    Davy R, Gnatiuk N, Pettersson L, et al. 2018. Climate change impacts on wind energy potential in the European domain with a focus on the Black Sea. Renewable and Sustainable Energy Reviews, 81: 1652–1659. doi: 10.1016/j.rser.2017.05.253
    deCastro M, Costoya X, Salvador S, et al. 2019. An overview of offshore wind energy resources in Europe under present and future climate. Annals of the New York Academy of Sciences, 1436(1): 70–97. doi: 10.1111/nyas.13924
    Dee D P, Uppala S M, Simmons A J, et al. 2011. The ERA-Interim reanalysis: configuration and performance of the data assimilation system. Quarterly Journal of the Royal Meteorological Society, 137(656): 553–597. doi: 10.1002/qj.828
    Esteban M D, Espada J M, Ortega J M, et al. 2019. What about marine renewable energies in Spain?. Journal of Marine Science and Engineering, 7(8): 249. doi: 10.3390/jmse7080249
    González-Longatt F, González J S, Payán M B, et al. 2014. Wind-resource atlas of Venezuela based on on-site anemometry observation. Renewable and Sustainable Energy Reviews, 39: 898–911. doi: 10.1016/j.rser.2014.07.172
    Han Li, Romero C E, Yao Zheng. 2015. Wind power forecasting based on principle component phase space reconstruction. Renewable Energy, 81: 737–744. doi: 10.1016/j.renene.2015.03.037
    Jung C, Taubert D, Schindler D. 2019. The temporal variability of global wind energy—Long-term trends and inter-annual variability. Energy Conversion and Management, 188: 462–472. doi: 10.1016/j.enconman.2019.03.072
    Kumar B P, Vialard J, Lengaigne M, et al. 2013. TropFlux wind stresses over the tropical oceans: evaluation and comparison with other products. Climate Dynamics, 40(7): 2049–2071
    Langodan S, Cavaleri L, Viswanadhapalli Y, et al. 2014. The Red Sea: a natural laboratory for wind and wave modeling. Journal of Physical Oceanography, 44(12): 3139–3159. doi: 10.1175/JPO-D-13-0242.1
    Langodan S, Viswanadhapalli Y, Dasari H P, et al. 2016. A high-resolution assessment of wind and wave energy potentials in the Red Sea. Applied Energy, 181: 244–255. doi: 10.1016/j.apenergy.2016.08.076
    Liu Hui, Chen Chao. 2019. Data processing strategies in wind energy forecasting models and applications: A comprehensive review. Applied Energy, 249: 392–408. doi: 10.1016/j.apenergy.2019.04.188
    Liu Fa, Sun Fubao, Liu Wenbin, et al. 2019. On wind speed pattern and energy potential in China. Applied Energy, 236: 867–876. doi: 10.1016/j.apenergy.2018.12.056
    Marcos R, González-Reviriego N, Torralba V, et al. 2019. Characterization of the near surface wind speed distribution at global scale: ERA-Interim reanalysis and ECMWF seasonal forecasting system 4. Climate Dynamics, 52(5/6): 3307–3319. doi: 10.1007/s00382-018-4338-5
    Omrani H, Drobinski P, Arsouze T, et al. 2017. Spatial and temporal variability of wind energy resource and production over the North Western Mediterranean Sea: Sensitivity to air-sea interactions. Renewable Energy, 101: 680–689. doi: 10.1016/j.renene.2016.09.028
    Onea F, Deleanu L, Rusu L, et al. 2016. Evaluation of the wind energy potential along the Mediterranean Sea coasts. Energy Exploration & Exploitation, 34(5): 766–792
    Pryor S C, Barthelmie R J. 2011. Assessing climate change impacts on the near-term stability of the wind energy resource over the United States. Proceedings of the National Academy of Sciences of the United States of America, 108(20): 8167–8171. doi: 10.1073/pnas.1019388108
    Qian Zheng, Pei Yan, Zareipour H, et al. 2019. A review and discussion of decomposition-based hybrid models for wind energy forecasting applications. Applied Energy, 235: 939–953. doi: 10.1016/j.apenergy.2018.10.080
    Rivas M B, Stoffelen A. 2019. Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT. Ocean Science, 15(3): 831–852. doi: 10.5194/os-15-831-2019
    Rusu L, Ganea D, Mereuta E. 2018. A joint evaluation of wave and wind energy resources in the Black Sea based on 20-year hindcast information. Energy Exploration & Exploitation, 36(2): 335–351
    Rusu L, Onea F. 2017. The performance of some state-of-the-art wave energy converters in locations with the worldwide highest wave power. Renewable and Sustainable Energy Reviews, 75: 1348–1362. doi: 10.1016/j.rser.2016.11.123
    Song Lina, Liu Zhiliang, Wang Fan. 2015. Comparison of wind data from ERA-Interim and buoys in the Yellow and East China Seas. Chinese Journal of Oceanology and Limnology, 33(1): 282–288. doi: 10.1007/s00343-015-3326-4
    Soukissian T H, Denaxa D, Karathanasi F, et al. 2017. Marine renewable energy in the Mediterranean Sea: Status and perspectives. Energies, 10(10): 1512. doi: 10.3390/en10101512
    Soukissian T H, Karathanasi F E. 2016. On the use of robust regression methods in wind speed assessment. Renewable Energy, 99: 1287–1298. doi: 10.1016/j.renene.2016.08.009
    Thomas B R, Kent E C, Swail V R, et al. 2008. Trends in ship wind speeds adjusted for observation method and height. International Journal of Climatology, 28(6): 747–763. doi: 10.1002/joc.1570
    Ulazia A, Sáenz J, Ibarra-Berastegui G, et al. 2017. Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean. Applied Energy, 208: 1232–1245. doi: 10.1016/j.apenergy.2017.09.030
    Wan Yong, Fan Chenqing, Dai Yongshou, et al. 2018. Assessment of the joint development potential of wave and wind energy in the South China Sea. Energies, 11(2): 398. doi: 10.3390/en11020398
    Wan Yong, Zhang Jie, Meng Junmin, et al. 2015. Exploitable wave energy assessment based on ERA-Interim reanalysis data—A case study in the East China Sea and the South China Sea. Acta Oceanologica Sinica, 34(9): 143–155. doi: 10.1007/s13131-015-0641-8
    Wang Yihui, Walter R K, White C, et al. 2019. Assessment of surface wind datasets for estimating offshore wind energy along the Central California Coast. Renewable Energy, 133: 343–353. doi: 10.1016/j.renene.2018.10.008
    Wang Guosong, Wang Xidong, Wang Hui, et al. 2020. Evaluation on monthly sea surface wind speed of four reanalysis data sets over the China seas after 1988. Acta Oceanologica Sinica, 39(1): 83–90. doi: 10.1007/s13131-019-1525-0
    Xydis G. 2015. A wind energy integration analysis using wind resource assessment as a decision tool for promoting sustainable energy utilization in agriculture. Journal of Cleaner Production, 96: 476–485. doi: 10.1016/j.jclepro.2013.11.030
    Xydis G, Mihet-Popa L. 2017. Wind energy integration via residential appliances. Energy Efficiency, 10(2): 319–329. doi: 10.1007/s12053-016-9459-2
    Yan Jie, Zhang Hao, Liu Yongqian, et al. 2019. Uncertainty estimation for wind energy conversion by probabilistic wind turbine power curve modelling. Applied Energy, 239: 1356–1370. doi: 10.1016/j.apenergy.2019.01.180
    Yu Lejiang, Zhong Shiyuan. 2019. The Interannual variability of surface winds in Antarctica and the surrounding oceans: A climatological analysis using the ERA-Interim reanalysis data. Journal of Geophysical Research: Atmospheres, 124(16): 9046–9061. doi: 10.1029/2019JD030328
    Zheng Chongwei, Li Chongyin, Li Xin. 2017. Recent decadal trend in the North Atlantic wind energy resources. Advances in Meteorology, 2017: 7257492. doi: 10.1155/2017/7257492
    Zheng Chongwei, Li Xueyan, Luo Xia, et al. 2019a. Projection of future global offshore wind energy resources using CMIP data. Atmosphere: Ocean, 57(2): 134–148. doi: 10.1080/07055900.2019.1624497
    Zheng Chongwei, Li Chongyin, Xu Jianjun. 2019b. Micro-scale classification of offshore wind energy resource—A case study of the New Zealand. Journal of Cleaner Production, 226: 133–141. doi: 10.1016/j.jclepro.2019.04.082
    Zheng Chongwei, Xiao Ziniu, Peng Yuehua, et al. 2018. Rezoning global offshore wind energy resources. Renewable Energy, 129: 1–11. doi: 10.1016/j.renene.2018.05.090
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索


    Article Metrics

    Article views (82) PDF downloads(7) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint