ASSESSMENT OF WIND SPEED PROJECTIONS CONSIDERING WIND POWER - - PowerPoint PPT Presentation
ASSESSMENT OF WIND SPEED PROJECTIONS CONSIDERING WIND POWER DEVELOPMENT IN RUSSIA Ekaterina Fedotova, Elena Luferova Global Energy Problems Laboratory, Moscow Power Engineering Institute 2 BACKGROUND How does the climate change impact the
Global Energy Problems Laboratory, Moscow Power Engineering Institute
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How does the climate change impact the power systems? What should be like an efficient energy system to meet the challenges of the future?
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Energy systems Climate Data analysis
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E, bln tce/year
non-fossil coal nonconvential gas natural gas
[Klimenko et al 2019]
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[Klimenko et al 2019]
Geothermal Solar Wind Biofuel
Renewable power Hydro Nuclear power kWh * 10 Bln t
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Hannover Messe 2017
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[Ermolenko et al 2017]
Russian wind resources are quite satisfactory
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EVF_present_CNN.key There is quite a strong decreasing trend of the wind speed Linear trend %/10 years for the seasonal wind speed for 1977-2011 spring summer autumn winter
[Second Assessment Report… 2014]
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The wind power per unit area
is air density, U is air velocity
E ¼ P A ¼ 1 2 rU3
r
Which means that 5% change of the wind speed may still be a lot
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The global climate models seem to heavily underestimate the decreasing tend of the wind speed
[Tian et al. 2019]
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Robust multidecadal regional projections of the surface wind speed in Russia are of interest to ensure integration of the wind power in the national power systems
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Regional downscaling Global climate modelling Calibration for the certain operation site Ensemble approach should be used
Roshydromet observations+ remote sensing data + monitoring
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CMIP5 simulation results were used to construct an ensemble estimation Original R-code was developed to facilitate ensemble calculations Ensemble optimisation was one of the main points of the
considers reproducibility of the daily wind speed distributions in European CORDEX domain [Carvalho et al. 2017]
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1995−2004 to 1951−1960: relative change 1995−2004 to 1911−1920: relative c1995−2004 to 1921−1930: relative c
1995−2004 to 1931−1940: relative change1995−2004 to 1941−1950: relative change 1995−2004 to 1961−1970: relative change −0.25 −0.2 −0.15 −0.1 −0.05 0.05 0.1 0.15 0.2 0.25
1995−2004 to 1971−1980: relative change 1995−2004 to 1977−1986: relative changeRelative change of the surface wind speed
Reanalysis 20Vc The long-term variability is of high interest for the considered problem
1995-2004 vs 1911-1920 1995-2004 vs 1921-1930 1995-2004 vs 1931-1940 1995-2004 vs 1941-1950 1995-2004 vs 1951-1960 1995-2004 vs 1961-1970 1995-2004 vs 1971-1980 1995-2004 vs 1977-1986
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45 50 55 60 65 70 50 100 150
0.00 0.02 0.04 0.06 0.08 45 50 55 60 65 70 50 100 150
0.00 0.01 0.02 0.03 0.04
40 60 80
−0.25 −0.2 −0.15 −0.1 −0.05 0.05 0.1 0.15 0.2 0.25
1995-2004 vs 1977-1986
8-models ensemble all models ensemble Reanalysis 20Vc
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45 50 55 60 65 70 50 100 150
0.00 0.01 0.02 0.03 45 50 55 60 65 70 50 100 150
0.00 0.01 0.02 0.03 0.04
1995-2004 vs 1951-1960 1995-2004 vs 1941-1950
−0.25 −0.2 −0.15 −0.1 −0.05 0.05 0.1 0.15 0.2 0.25 −0.25 −0.2 −0.15 −0.1 −0.05 0.05 0.1 0.15 0.2 0.25
8-models ensemble 8-models ensemble Reanalysis 20Vc Reanalysis 20Vc
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45 50 55 60 65 70 50 100 150
0.00 0.02 0.04 0.06 45 50 55 60 65 70
0.00 0.01 0.02 0.03 0.04 0.05 45 50 55 60 65 70 50 100 150
0.00 0.02 0.04 0.06
Relative change of the annual surface wind speed 2045-2054 vs 2007-2016 (rcp 4.5)
8-models ensemble 9-models ensemble all models ensemble
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Relative change of the annual surface wind speed 2065-2074 vs 2007-2016 (rcp 4.5)
8-models ensemble 9-models ensemble all models ensemble
45 50 55 60 65 70 50 100 150
0.00 0.02 0.04 0.06 0.08 45 50 55 60 65 70
0.00 0.02 0.04 0.06 45 50 55 60 65 70 50 100 150
0.00 0.02 0.04 0.06
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Relative change of the annual surface wind speed 2065-2074 vs 2007-2016 (rcp 4.5)
8-models ensemble 9-models ensemble all models ensemble
45 50 55 60 65 70 50 100 150
0.00 0.02 0.04 0.06 0.08 45 50 55 60 65 70 50 100 150
0.00 0.02 0.04 0.06 45 50 55 60 65 70 50 100 150
0.00 0.02 0.04 0.06
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45 50 55 60 65 70 50 100 150
0.00 0.02 0.04 0.06 45 50 55 60 65 70
0.00 0.01 0.02 0.03 0.04 0.05 45 50 55 60 65 70 50 100 150
0.00 0.02 0.04 0.06
8-models ensemble 9-models ensemble all models ensemble
Wind resources in Primorye seem to have better prospects as compared with European part of Russia
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1. The global climate models tend to underestimate the changes of the surface wind speed 2. The ensemble optimisation seems to ensure better reproducibility of the wind speed across Russia in the mid- term retrospective (up to 60 years) 3. The surface wind speed changes demonstrate non- monotonic features 4. The wind resources in the European part of Russia and in West Siberia are likely to have decreasing trend, in Primorye — an increasing one
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Long-term variability of the surface wind speed is of highest practical interest
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We are very grateful to V .V . Klimenko and A.G. Tereshin for inspiring discussions of the Russian energy policy. The work was supported by the Russian Science Foundation as a part of the project “Modernisation opportunities of the Russian power industry under the climate change” (grant 18-79-10255) We also highly acknowledge the CMIP5 modelling groups and the World Data Center for Climate in Hamburg for granted access to the CMIP5 simulation data.