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 power systems? What should be like an e ffi cient energy system to meet the challenges of the future?

3 BACKGROUND Data analysis Climate Energy systems

4 MOTIVATION: GLOBAL PICTURE E, bln tce/year non-fossil coal nonconvential gas natural gas oil [Klimenko et al 2019]

5 MOTIVATION: GLOBAL PICTURE Bln t kWh * 10 Renewable power Solar Wind Nuclear power Biofuel Hydro Geothermal [Klimenko et al 2019]

6 MOTIVATION Hannover Messe 2017

7 WIND POWER IN RUSSIA: GOOD NEWS Russian wind resources are quite satisfactory [Ermolenko et al 2017]

8 WIND POWER IN RUSSIA: BAD NEWS (1/2) spring summer autumn winter There is quite a strong decreasing trend of the wind speed EVF_present_CNN.key Linear trend %/10 years for the seasonal wind speed for 1977-2011 [Second Assessment Report… 2014]

9 WIND POWER IN RUSSIA: BAD NEWS (1/2) The wind power per unit area P A ¼ 1 2 r U 3 E ¼ is air density, U is air velocity r Which means that 5% change of the wind speed may still be a lot

10 WIND POWER IN RUSSIA: BAD NEWS (2/2) The global climate models seem to heavily underestimate the decreasing tend of the wind speed [Tian et al. 2019]

11 AIM OF THE WORK 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

12 WORKFLOW (1/2) Global climate modelling Ensemble approach should be used Regional downscaling Calibration for the certain operation site Roshydromet observations+ remote sensing data + monitoring

13 WORKFLOW (2/2) CMIP5 simulation results were used to construct an ensemble estimation Ensemble optimisation was one of the main points of the work. The CMIP5 quality ranking was used. The ranking considers reproducibility of the daily wind speed distributions in European CORDEX domain [Carvalho et al. 2017] Original R-code was developed to facilitate ensemble calculations

14 VALIDATION DATASET Reanalysis 20Vc 0.25 0.2 0.15 0.1 Relative change of the The long-term variability is of high interest 0.05 0 surface wind speed − 0.05 for the considered problem − 0.1 − 0.15 − 0.2 − 0.25 1995-2004 vs 1941-1950 1995-2004 vs 1911-1920 1995-2004 vs 1921-1930 1995-2004 vs 1931-1940 1995 − 2004 to 1911 − 1920: relative c 1995 − 2004 to 1931 − 1940: relative change 1995 − 2004 to 1941 − 1950: relative change 1995 − 2004 to 1921 − 1930: relative c 1995-2004 vs 1951-1960 1995-2004 vs 1961-1970 1995-2004 vs 1971-1980 1995-2004 vs 1977-1986 1995 − 2004 to 1951 − 1960: relative change 1995 − 2004 to 1961 − 1970: relative change 1995 − 2004 to 1971 − 1980: relative change 1995 − 2004 to 1977 − 1986: relative change

15 RESULTS 1995-2004 vs 1977-1986 8-models ensemble 0.04 70 0.03 65 0.02 60 0.01 55 0.00 Reanalysis 20Vc 50 -0.01 0.25 0.2 -0.02 45 80 0.15 -0.03 0.1 50 100 150 0.05 60 0 all models ensemble − 0.05 0.08 − 0.1 70 − 0.15 0.06 40 65 − 0.2 0.04 − 0.25 60 0.02 55 0.00 50 -0.02 45 -0.04 50 100 150

16 RESULTS 1995-2004 vs 1951-1960 8-models ensemble Reanalysis 20Vc 0.03 0.25 70 0.2 0.02 0.15 65 0.01 0.1 60 0.00 0.05 0 55 -0.01 − 0.05 50 -0.02 − 0.1 − 0.15 -0.03 45 − 0.2 -0.04 − 0.25 50 100 150 1995-2004 vs 1941-1950 8-models ensemble Reanalysis 20Vc 0.04 0.25 70 0.03 0.2 0.02 0.15 65 0.1 0.01 60 0.05 0.00 0 55 -0.01 − 0.05 50 -0.02 − 0.1 − 0.15 45 -0.03 − 0.2 -0.04 − 0.25 50 100 150

17 RESULTS Relative change of the annual surface wind speed 2045-2054 vs 2007-2016 ( rcp 4.5 ) 0.06 70 0.04 65 0.02 60 8-models ensemble 55 0.00 50 -0.02 45 -0.04 50 100 150 0.05 0.04 70 0.03 65 0.02 60 9-models ensemble 0.01 0.00 55 -0.01 50 -0.02 45 -0.03 -0.04 0.06 70 0.04 65 0.02 all models ensemble 60 0.00 55 -0.02 50 -0.04 45 -0.06 50 100 150

18 RESULTS Relative change of the annual surface wind speed 2065-2074 vs 2007-2016 ( rcp 4.5 ) 0.08 70 0.06 65 0.04 60 8-models ensemble 0.02 55 0.00 50 -0.02 45 -0.04 50 100 150 0.06 70 0.04 65 0.02 60 9-models ensemble 0.00 55 50 -0.02 45 -0.04 0.06 70 0.04 65 0.02 all models ensemble 60 0.00 55 -0.02 -0.04 50 -0.06 45 -0.08 50 100 150

19 RESULTS Relative change of the annual surface wind speed 2065-2074 vs 2007-2016 ( rcp 4.5 ) 0.08 70 0.06 65 0.04 60 8-models ensemble 0.02 55 0.00 50 -0.02 45 -0.04 50 100 150 0.06 70 0.04 65 0.02 60 9-models ensemble 0.00 55 -0.02 50 -0.04 45 -0.06 0.06 50 100 150 70 0.04 65 0.02 all models ensemble 60 0.00 -0.02 55 -0.04 50 -0.06 45 -0.08 50 100 150

20 RESULTS Wind resources in Primorye seem to have better prospects as compared with European part of Russia 0.06 70 0.04 65 0.02 60 8-models ensemble 55 0.00 50 -0.02 45 -0.04 50 100 150 0.05 0.04 70 0.03 65 0.02 60 9-models ensemble 0.01 0.00 55 -0.01 50 -0.02 45 -0.03 -0.04 0.06 70 0.04 65 0.02 all models ensemble 60 0.00 55 -0.02 50 -0.04 45 -0.06 50 100 150

21 SUMMARY 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

22 OPEN QUESTIONS Long-term variability of the surface wind speed is of highest practical interest ?

23 ACKNOWLEDGMENTS We are very grateful to V .V . Klimenko and A.G. Tereshin for inspiring discussions of the Russian energy policy. 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. 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)

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