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Wind projections for the territory of Russia considering the development
of wind power
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CITES 2019
IOP Conf. Series: Earth and Environmental Science 386 (2019) 012042
IOP Publishing
doi:10.1088/1755-1315/386/1/012042
1
Wind projections for the territory of Russia considering the
development of wind power
E V Fedotova
Moscow Power Engineering Institute, Krasnokazarmennaya 14, Moscow
ek.v.fedotova@gmail.com
Abstract. The aim of the present work is to obtain surface wind speed projections which could
be used as guidelines for long-term planning of wind power construction in Russia. A classical
multi-model ensemble approach is implemented by using CMIP5 simulation results. The
reliability of the ensemble estimation is assessed by a comparison of three different ensemble
versions, which are validated against reanalysis data for the whole 20th century and have been
found to give consistent results since 1950. Agreement between the results of all the
assembling approaches has been found to be quite good for the mid-twenty-first century. All
ensembles being considered agree that a considerable decrease in wind resources should be
expected in the European part of Russia and in the south of Western Siberia towards 2050.
Another robust output of the analysis is an increase in annual wind speed in the Southern
Russian Far East. The wind change during the considered 40-year period is in the range from -
6 to +6%, which means a -18 to +18% change in potential wind generation. The main output of
the present work is that climate change by no means can be seen to be an obstacle to the
development of renewable power in Russia. However, the climate change associated alteration
of wind regime should be necessarily taken into account when establishing long-term plans for
wind farm construction in Russia.
1. Introduction
The energy systems are in transition around the world. One of the largest drivers of this
transformation process are the renewable electricity generation technologies that seemed to be of a
purely academic interest only twenty years ago. Nowadays the development of wind generators is one
of the most impressive examples of rapid development of renewable technology. Wind generators
have operated since the first decades of the twentieth century, but have played a minor role in the
energy systems for many decades. Today wind power all over the world has become one of the leading
generation technologies as a part of real industrial power systems. The total wind power output is
approaching the nuclear power plants.
The wind generation is directly linked with the wind speed fluctuations, which mean that the wind
power economics is greatly influenced by the climatic conditions. Integration of an increasing wind
power share into the energy systems is possible only if the wind regime in a particular area is well
understood [1]-[3]. Multi-annual wind speed monitoring has become a common step of wind farm
procurement.
Wind energy in Russia is currently at the very early stage of development. The overall installed
wind power across the country is currently approaching 1 GW. Several new wind farms should be
launched across the country during the next decades, multiplying the installed capacity value.
CITES 2019
IOP Conf. Series: Earth and Environmental Science 386 (2019) 012042
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doi:10.1088/1755-1315/386/1/012042
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Assessment of the long-term wind speed dynamics across the country is becoming crucially important
for the support of this development process.
2. Related research
The long-term wind speed dynamics for different regions of the world is being intensively studied
during the last decade. A recent paper [4] has demonstrated that the modern climate models tend to
heavily underestimate the decreasing wind trends observed in the Northern Hemisphere during the
twentieth century. This makes evident the importance of careful consideration of the models selection
used to project the wind speed in a given world area.
2.1. Meteorological observations
A continuously decreasing wind speed trend in the surface has been observed in Russia since the
1970s [5]. A comprehensive meteorological analysis performed by Roshydromet experts has allowed
one to conclude that the reduction of the average wind speed is typical for all seasons and occurs
almost everywhere [6]. This effect is especially pronounced in the European territory of Russia, where
most wind farms to introduce to construction during the next decades should be located. The authors
of [6] definitely attribute the observed tendency to the modern climate change.
2.2. Projections
Comprehensive work on climate projections for the Russian territory was performed at the Voeikov
Main Geophysical Observatory [7] about five years ago. These calculations utilized the CMIP5
(Coupled Model Intercomparison Project) Phase5. The authors have made validation of the available
global climate circulation models to select sixteen models which reproduce seasonal variations of the
temperature, precipitation, and pressure in a way that is most consistent with the observations. The
authors have stated that the wind speed changes up to +/- 1 m/s are projected but do not provide any
robustness assessment of the projected wind speed fields.
The authors of the Roshydromet Assesment Report [5] make reference to the ensemble calculations
of the surface wind speed for the rcp 8.5 scenario, but the results of these calculations are not
presented. The authors mention that the wind speed change obtained with this assessment is in the
range of +/-1m/s, and believe this change to be insignificant not to discuss any further details.
Recently, paper [8] has presented a detailed analysis of the CMIP5 results for the Russian Arctic
territories with a careful consideration of the validation issues for the used models. However, the focus
of the authors was only on the Arctic seas. In addition, calculations have been made for the
catastrophic climatic scenario of rcp 8.5 which, fortunately, should be treated as a very likely one. This
does not allow us to utilize the results obtained in [8] even for the most preliminary conclusions about
the real wind conditions which will impact the operation of wind parks in Russia during the next
decades.
2.3. Technology status
It is difficult to agree with the conclusion about the insignificance of the observed wind speed
changes. The wind turbines are quite sensitive to the wind speed variations due to the fact that wind
turbine power generation depends on the kinetic energy of the wind flow Pwind, which is proportional
to the cube of the wind speed w:
, (1)
where ρ is the air density.
Wind speed changes of about 1 m/s mean a change in the wind turbine output by two to three times
at a typical surface wind speed of 3...5 m/s, which is usual on the Russian territory. Thus, the climate
change seems to inevitably affect the development of the wind energy in Russia. Nowadays, at the
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IOP Conf. Series: Earth and Environmental Science 386 (2019) 012042
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doi:10.1088/1755-1315/386/1/012042
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very beginning of this development process, there is an urgent need to obtain at least some robust
projections of the surface wind speed in Russia under realistic climate scenarios.
3. Motivation
The presented work is intended for obtaining projections of the surface wind speed, which could be
used as a guideline for long-term planning of the wind power construction in Russia, particularly when
assessing the future energy system options and control strategies. We will use a classical ensemble
approach considering different ensembling approaches.
4. Methods
The moderate climate scenario rcp 4.5 was taken for further calculations, as the closest one to the
likely real climate changes during the 21st century. The soft scenario rcp 2.6 and the dramatic 8.5 were
considered to estimate the robustness of the calculated projections. The historic climate experiments
were used in the validation procedure. Our calculations used the results of the CMIP5 project that up
to date provides access to the most comprehensive set of the global climate models simulation results.
We used the monthly output of 27 CMIP5 models available for the near-surface wind speed [9].
Table 1. List of CMIP5 models used for ensemble estimations.
Model Name
Institute
ACCESS 1.0
Commonwealth Scientific and Industrial Research
Organization/Bureau of Meteorology, Australia (CSIRO-BOM)
ACCESS 1.3
bcc-csm1-1-m
Beijing Climate Center, China (BCC)
bcc-csm1-1
BNU-ESM
Beijing Normal University, China(BNU)
CanESM2
Canadian Centre for Climate Modelling and Analysis, Canada
(CCCma)
CMCC-CESM
Centro Euro-Mediterraneo sui Cambiamenti Climatici,
Italy (CMCC)
CMCC-CMS
CMCC-CM
CNRM-CM5
Centre National de Recherches Météorologiques,
Centre Européen de Recherche et de Formation Avancée en Calcul
Scientifique, France (CNRM-CERFACS)
CSIRO-Mk 3.6.0
Commonwealth Scientific and Industrial Research
Organization/Queensland Climate Change Centre of Excellence,
Australia (CSIRO-QCCCE)
GFDL-CM3
Geophysical Fluid Dynamics Laboratory, USA (NOAA GFDL)
GFDL-ESM2G
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IOP Conf. Series: Earth and Environmental Science 386 (2019) 012042
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Model Name
Institute
GFDL-ESM2M
HadGEM2-CC
Met Office Hadley Centre, UK (MOHC)
HadGEM2-ES
inmcm4
Russian Academy of Sciences, Institute of Numerical Mathematics,
Russia (INM)
IPSL-CM5A-LR
Institut Pierre Simon Laplace, France (IPSL)
IPSL-CM5A-MR
IPSL-CM5B-LR
MIROC-ESM
Atmosphere and Ocean Research Institute (The University of
Tokyo), National Institute for Environmental Studies, and
Japan Agency for Marine-Earth Science and Technology, Japan
(MIROC)
MIROC-ESM-
CHEM
MIROC5
MPI-ESM-LR
Max Planck Institute for Meteorology, Germany (MPI-M)
MPI-ESM-MR
MRI-CGCM3
Meteorological Research Institute, Japan (MRI)
MRI-ESM1
4.1. Ensemble strategy
The used ensemble strategies accounted for [10] CMIP5 validation results on the high-resolution
reanalysis data. The essence of this validation procedure was the reproducibility testing of the daily
wind speed distributions in each computational grid cell by each of the CMIP5 models in the European
domain. The model quality metric was defined as a cell number where the Kolmogorov-Smirnov test
gave a positive result.
In our calculations we have considered three ensembles:
1) the ensemble recommended in [10] consisted of eight models which model the distribution of
wind speeds corresponding to the reanalysis data for 75% of the model cells in a considered area;
2) an ensemble of nine models which give 70% of the cells share with a correctly reproduced daily
wind speed distribution;
3) an ensemble of all available models.
The geographical restriction of [10] seems not to be critical for the considered problem, since most
wind farms in Russia planned for construction are located in the European part of the country. Besides,
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IOP Conf. Series: Earth and Environmental Science 386 (2019) 012042
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doi:10.1088/1755-1315/386/1/012042
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the global climate models ensemble estimations may be treated for the wind speed only as a quite
approximate assessment. Downscaling approaches should be applied to produce wind speed
projections suitable for use in the energy systems modeling and planning. What we are focused on is
obtaining a general picture of the muldidecadal wind speed trends.
4.2. Validation
A simplified validation procedure was performed to check the reproducibility of the long-term wind
speed dynamics across the country by the considered ensembles. The results of the ensemble
estimations for the historic CMIP5 experiment were compared with the 20th Century Reanalysis V2c
[11] reanalysis data for the whole twentieth century.
a
b
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IOP Conf. Series: Earth and Environmental Science 386 (2019) 012042
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doi:10.1088/1755-1315/386/1/012042
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c
Figure 1. Change in the annual surface wind speed in 1995–2004 compared with
1977–1986: a – according to 20Cv2 reanalysis, b – calculated by the eight-models
ensemble, c – calculated by the whole set of all available models.
It was found that the eight-model ensemble performs better as compared with the all-models
ensemble (Figure 1). The correspondence is more qualitative than quantitative and is satisfactory for
the whole Russian area only since 1940. The discrepancy for the earlier dates may obviously result
both from the ensemble uncertainties and flaws of the reanalysis data. A more detailed analysis seems
to be necessary to clarify the long-term trends of the wind speed during the twentieth century.
However, the ensemble estimations seem to be quite consistent with the reanalysis data on fifty-sixty
years’ time horizons, which is quite sufficient for the purpose of our work.
5. Results and discussion
The three above-considered ensembles give close results for the relative wind speed change across the
country during the twenty-first century. However, they differ significantly in details. The range of
changes in the territory is the same for all three cases varying from -6...-4% to +5...+6% (Fig. 2). The
ensembles also agree that the most pronounced decrease in wind speed is to be expected in the central
and southern parts of the European territory of Russia, as well as in the south of Western Siberia,
which is consistent with the observation data of [6].
A second conclusion which follows from this comparison is ae likely increase in the average wind
speed in Primorye and on the Sakhalin shelf. This effect is apparently linked to the atmospheric
circulation shift, in particular, to the weakening of the Siberian High and intensification of the
cyclonic activity over the Pacific observed during the last decades and attributed to climate change.
This result is remarkably stable for all ensembles considered. Moreover, this output is consistent even
with different climate scenarios. A control ensemble calculation run has given us qualitatively the
same wind speed increase in the Far East for rcp 2.6 and rcp 8.5 as well.
It should be emphasized again that the present work is intended to obtain robust large-scale
projections of long-term forecast changes in the wind potential. The effects of mesoscale atmospheric
hydrodynamics combined with local topography have a huge impact on wind farm operation. That is
why the application of dynamic or statistical downscaling procedures is a necessary step in using the
above-obtained national-scale results in energy system optimization models.
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doi:10.1088/1755-1315/386/1/012042
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Figure 2. Projection of the annual surface wind speed change in 2065–2074 as compared with 2007–
2016 eight-models ensemble.
What may be concluded right now is that the Russian Far East will definitely obtain a chance to
renovate the local energy systems on a much cleaner basis as compared to the coal power plants in
operation there today. Apart from the anticipated increase in wind potential, the Far East is one of the
Russian regions where a significant rise in bio-productivity is very likely during the 21st century [12].
Climate change seems to favor the renewable renovation plans whose realization is gradually starting
now in the energy systems of the Far East. However, the projections for the wind velocity changes in
Kamchatka and Chukotka areas are far less certain and require a more detailed analysis, since wind
power there is of highest interest for the regional remote energy systems.
There are some areas where the decrease in wind speed may lead up to a 12% decrease in wind
specific energy. Fortunately, it does not mean any serious risks for the development of national wind
power, since the areas of noticeable wind speed decrease are located in regions where no extensive
wind power development is under discussion. The south of European Russia, the Baltic Sea coast, and
the Kola Peninsula, where wind park constructions are planned, are likely to be subjected to relatively
low changes in surface wind speed, in the range of +/-2%. Climate change seems to provide more
possibilities than threats for the development of renewable power in Russia. It should be kept in mind,
however, that the integration of an intermittent wind generation into the energy systems requires
careful consideration of wind speed dynamics and a shift in the control paradigm of energy systems.
Acknowledgments
This work was supported by the Russian Science Foundation (project no. 18-79-10255) in part of
developing the methodology and by the Russian Foundation for Basic Research (grant no. 17-08-
00134) in part of making the calculations. The author highly acknowledges the participation of the
modeling groups of the CMIP5 project listed in Table 1 and the World Data Center for Climate in
Hamburg for providing access to the CMIP5 simulation data.
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