THE INFLUENCE OF WIND SPEED PROBABILITY DISTRIBUTION PARAMETERS ON THE ENERGY EFFICIENCY OF COASTAL AND OFFSHORE WIND FARMS IN ENCLOSED SEAS: A CASE STUDY OF THE AZOV-BLACK SEA REGION OF UKRAINE
The problems of determining the main parameters of probability distributions of wind speed, assessing their impact on the energy efficiency of wind power plants and the characteristics of power generation variability are considered. Energy efficiency is characterized by the power factor. Its calcula...
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| author | Vasko , P. Mazurenko , I. Sysak , R. |
| author_facet | Vasko , P. Mazurenko , I. Sysak , R. |
| author_institution_txt_mv | [
{
"author": "P. Vasko ",
"institution": "Institute of Renewable Energy, NAS of Ukraine, Kyiv, Ukraine"
},
{
"author": "I. Mazurenko ",
"institution": "Institute of Renewable Energy, NAS of Ukraine, Kyiv, Ukraine"
},
{
"author": "R. Sysak ",
"institution": "Institute of Renewable Energy, NAS of Ukraine, Kyiv, Ukraine"
}
] |
| author_sort | Vasko , P. |
| baseUrl_str | https://ve.org.ua/index.php/journal/oai |
| collection | OJS |
| datestamp_date | 2026-07-18T06:32:22Z |
| description | The problems of determining the main parameters of probability distributions of wind speed, assessing their impact on the energy efficiency of wind power plants and the characteristics of power generation variability are considered. Energy efficiency is characterized by the power factor. Its calculation is performed using the Weibull distribution. The primary parameters are the average wind speed and the scale and shape parameters of the distribution. Possible limits of the ranges of parameter changes in the Azov-Black Sea region are determined. An analysis of current databases and available results of instrumental measurements of wind speed in the studied region is carried out to obtain quantitative estimates of the parameters. The need for additional short-term measurements of wind speed at the construction sites of wind power plants is justified to adjust information from multi-year databases. Mathematical models are developed, and a numerical experiment is conducted to determine the achievable annual value of capacity factor and characteristics of power generation variability by coastal and offshore plants during the year in the region. 
Bibl. 68, tables 4, fig. 4. 
  |
| doi_str_mv | 10.36296/1819-8058.2025.3(82).125-136 |
| first_indexed | 2025-10-01T01:30:53Z |
| format | Article |
| fulltext |
125
Відновлювана енергетика. № 3/2025 | Вітроенергетика
УДК 621.311.25: 628.165 https://doi.org/10.36296/1819-8058.2025.3(82).125-136
THE INFLUENCE OF WIND SPEED PROBABILITY DISTRIBUTION PARAMETERS ON THE ENERGY
EFFICIENCY OF COASTAL AND OFFSHORE WIND FARMS IN ENCLOSED SEAS: A CASE STUDY OF THE
AZOV-BLACK SEA REGION OF UKRAINE
Received Sept. 04, 2025; accepted Sept. 22, 2025
Available online Sept. 30, 2025
Vasko P.2, Mazurenko I.2, Sysak R.3
Author for correspondence: Mazurenko Iryna,
e-mail: irynalmazurenko@gmail.com
Abstract. The problems of determining the main parameters of
probability distributions of wind speed, assessing their impact
on the energy efficiency of wind power plants and the
characteristics of power generation variability are considered.
Energy efficiency is characterized by the power factor. Its calculation is performed using the Weibull distribution.
The primary parameters are the average wind speed and the scale and shape parameters of the distribution.
Possible limits of the ranges of parameter changes in the Azov-Black Sea region are determined. An analysis of
current databases and available results of instrumental measurements of wind speed in the studied region is
carried out to obtain quantitative estimates of the parameters. The need for additional short-term measurements
of wind speed at the construction sites of wind power plants is justified to adjust information from multi-year
databases. Mathematical models are developed, and a numerical experiment is conducted to determine the
achievable annual value of capacity factor and characteristics of power generation variability by coastal and
offshore plants during the year in the region.
Bibl. 68, tables 4, fig. 4.
Keywords: probability, distribution, wind speed, wind farm, offshore area, energy efficiency, power, variability.
ВПЛИВ ПАРАМЕТРІВ ІМОВІРНІСНИХ РОЗПОДІЛІВ ШВИДКОСТІ ВІТРУ НА ЕНЕРГЕТИЧНУ
ЕФЕКТИВНІСТЬ ПРИБЕРЕЖНИХ ТА ОФШОРНИХ ВІТРОЕЛЕКТРИЧНИХ СТАНЦІЙ
У ЗАКРИТИХ МОРЯХ НА ПРИКЛАДІ АЗОВО-ЧОРНОМОРСЬКОГО РЕГІОНУ УКРАЇНИ
Отримано 04 вер. 2025 р.; рекомендовано до публікації 22 вер. 2025 р.
Доступно онлайн 30 вер. 2025 р.
Васько П. Ф.1, Мазуренко І. Л.2, Сисак Р. М.3
Автор для кореспонденції: Мазуренко Ірина,
e-mail: irynalmazurenko@gmail.com
Анотація. Розглянуто питання визначення основних па-
раметрів імовірнісних розподілів швидкості вітру, оці-
нки їх впливу на енергетичну ефективність вітроелек-
тростанцій та характеристики нерівномірності
генерування потужності. Енергетичну ефективність характеризовано коефіцієнтом потужності.
Його розрахунок виконано з викорис-танням функції щільності розподілу Вейбула. Основними парамет-
рами слугують середнє значення швидкості вітру, параметри масштабу і форми розподілу. Визначено
можливі межі діапазонів зміни параметрів в Азово-Чорноморському регіоні. Проведено аналіз чинних баз
даних та наявних результатів інструментальних вимірювань швидкості вітру в досліджуваному регі-
оні для отримання кількісних оцінок параметрів. Обґрунтовано необхідність проведення додаткових
короткотривалих вимірювань швидкості вітру в місцях спорудження вітроелектростанцій для коре-
гування інформації багаторічних баз даних. Розроблено математичні моделі та проведено числовий
експеримент з визначення досяжного річного значення Capacity factor та характеристик нерівно-
1 Dr. of Science (Tech.)
https://orcid.org/0000-0001-8807-7173
2 Cand. of Science (Tech.)
https://orcid.org/0000-0002-0146-7396
3 Cand. of Science (Tech.)
https://orcid.org/0000-0003-4474-4776
1, 2, 3 Institute of Renewable Energy, NAS of
Ukraine, Kyiv, Ukraine
1 д-р. техн. наук
https://orcid.org/0000-0001-8807-7173
2 канд. техн. наук
https://orcid.org/0000-0002-0146-7396
3 канд. техн. наук
https://orcid.org/0000-0003-4474-4776
1, 2, 3 Інститут відновлюваної енергетики НАН
України, м. Київ, Україна
126
Відновлювана енергетика. № 3/2025 | Вітроенергетика
мірності генерування потужності прибережними та офшорними станціями протягом року в регіоні.
Бібл. 68, табл. 4, рис. 4.
Ключові слова: імовірність, розподіл, швидкість вітру, вітроелектростанція, морська акваторія, ене-
ргетична ефективність, потужність, нерівномірність.
Introduction. Over the past decades, the world community
has been rightly concerned about the energy future, which
has prompted scientists to focus on solving urgent issues
related to global warming, energy security strategies, and
dependence on fossil fuel energy consumption. The growth
of the share of renewable energy sources in the fuel and
energy balance of countries around the world contributes
to the conservation of energy resources and water, and the
improvement of the environment by reducing atmospheric
pollution.
Today, offshore wind energy (where wind turbines are sit-
uated in the sea), one of the sectors of renewable energy,
is experiencing rapid development in the world [1,2]. The
main reason for the emergence of the practice of placing
offshore wind power plants (WPP) is explained by the pres-
ence of stronger and longer-lasting winds in marine areas
and on the coast, compared to winds on land, as well as
fewer technological restrictions on the installation of wind
turbines. In addition, the construction of offshore WPPs en-
ables countries with limited land areas and the presence of
marine territorial waters to expand the production of envi-
ronmentally friendly energy. Another advantage is the pos-
sibility of locating an offshore WPP in the sea near a large
industrial facility, which enables reducing the cost of trans-
mitting electricity to the facility through centralized power
grids. The possibilities of creating marine energy islands [3–
5] and pumped storage plants, both land-based and sea-
based, for storing energy from renewable sources [6,7] and
smoothing power fluctuations [8] are being considered.
Offshore wind farms are built on the continental shelf at
depths of up to 40 m, or on floating platforms [9,10]. There
is a practice of effective use of offshore wind farms on flat
coastal areas [11,12].
On the European continent, offshore wind projects in the
open seas have long been considered competitive. Thus,
the installed capacity of offshore wind farms in the EU as of
2023 was 36.5 GW [13], and in 2024 it increased and
reached 39.1 GW [14]. Five European countries (Great Brit-
ain, the Netherlands, Denmark, Belgium, and Germany)
have a share of offshore wind energy of more than 10% of
the total capacity of wind farms. For the UK, it is about 50%.
Potential areas for the construction of offshore wind farms
are also closed sea areas (closed seas), the wind energy po-
tential of which is of significant importance, as confirmed
by the results of research on wind resources in the Medi-
terranean Sea [15,16], the Red Sea and the Persian Gulf
[17,18], the Black Sea and the Sea of Azov [19]. Offshore
wind farms in closed seas can solve a number of energy and
technological problems, such as generating electricity for
integration into power systems [20], supplying technologi-
cal systems for desalination of seawater [21,22] and
electrolyzers for the production of “green” hydrogen
[23,24]. Ukraine is involved in the implementation of the
European program “2×40 GW Green Hydrogen Initiative”
[25,26], which emphasizes the construction of 10 GW of
electrolyzer capacity for the production of “green” hydro-
gen using wind energy in the Azov-Black Sea region. The
program envisages the production of green hydrogen in
Ukraine in the amount of 1.65 million tons per year, with
the consumption of prepared fresh water of about 24 mil-
lion m3/year [27]. Achieving the specified volumes of
“green” hydrogen production requires the construction of
about 40 GW of installed capacity of renewable energy
sources [28,29]. The specified capacity is too high for paral-
lel operation with the country's power system, which
prompts the identification of ways to integrate stochastic
electricity generation from wind farms into technological
schemes for hydrogen production and seawater desalina-
tion.
Implementing large-scale wind power projects in the en-
closed seas of the Azov-Black Sea region of Ukraine requires
more accurate estimates of energy efficiency than those
specified in the available atlases of wind resource potential,
which can only serve as a basis for selecting potential terri-
tories and forming strategic plans for the development of
wind energy. A decrease in the wind power plant efficiency
by (20-25) % from the expected value may turn the project
into unattractive or uncompetitive. The result of a quanti-
tative assessment of energy efficiency depends mainly on
the accuracy of multi-year wind speed distributions at the
height of the rotation axis. For the specified region, wind
speed distributions can be obtained only by mathematical
modeling, since the results of multi-year instrumental
measurements are absent. As part of the Horizon Europe
program of the European Commission, a project to build a
floating wind turbine with a capacity of 5 MW [30] is being
implemented to directly determine the wind speed distri-
butions and energy efficiency of offshore WPPs in the Black
Sea. However, as of today, reliable determination of the en-
ergy efficiency of WPPs in the region at the stage of pre-
project studies is problematic due to the uncertainty in the
probability distribution parameters and the vertical wind
speed profile. The solution to this problem is also relevant
for other seas on the planet, which is covered in a signifi-
cant number of scientific publications, in particular [31–41].
The research task is to obtain a quantitative assessment of
the influence of the wind speed probability distribution pa-
rameters on the energy characteristics of coastal and off-
shore wind farms in the Azov-Black Sea region of Ukraine
by solving a number of problems regarding the selection of
wind speed databases at the operating heights of wind
127
Відновлювана енергетика. № 3/2025 | Вітроенергетика
turbines and taking into account the results of instrumental
measurements of wind speed in the open seas.
Theoretical provisions of the study. The energy efficiency
of wind power plants will be assessed by the value of the
Capacity factor (CF), which is calculated in relative units
(percentages) and serves as a universal indicator for power
plants of arbitrary capacity [42]:
( ) )(
InstSS PTECF = (1)
with: ES – the amount of electricity produced by the WPP at
the metering point during a given time interval; Т – dura-
tion of the given time interval; InstSP )( – installed capacity
of the WPP.
The components of equation (1) will be determined for a
multi-unit WPP built from the same type of horizontal-axis
wind turbines, taking into account the wind speed proba-
bility distribution at the height of the wind turbine's center
of rotation (hub height) as follows [43]:
=
0
)()()()()( dvvfvPNkNkNkNTE WTlagS , (2)
потWTInstS PNP )()( = (3)
with: N – number of wind turbines of the same type in the
WPP; kg – WPP’s technical readiness factor, determined by
the reliability of the equipment and shutdowns of wind tur-
bines due to ice accumulation on the blades in the periods
when there is suitable wind; ka – coefficient of aerody-
namic mutual shading of wind turbines in the WPP; kl – co-
efficient of electricity losses in the WPP scheme and in the
power transmission line to the destination facility [44,45];
v – wind speed at the wind turbine’s hub height; PWT(v) –
functional dependence of wind turbine power on wind
speed; f(v) – probability density of the wind speed at the
hub height during time T; (PWT)nom – nominal power of the
wind turbine.
Approximate values of the coefficients kg, ka, kl for successful
WPP projects are within the following limits:
kg (N) = 0.97,...,0.94; ka (N) = 1.0,...,0.9;
kl (N) = 0.97,...,0.9, (4)
where the left limits of the ranges correspond to N=1.
After substituting (2), (3) into (1), we obtain a universal ex-
pression for calculating the energy efficiency of a WPP pro-
ject of arbitrary capacity built from wind turbines of the
same type:
=
0
)()( dvvfvPCF S , (5)
)()()()()( vPNkNkNkvP WTlagS = ,
потWTWTWT PvPvP )()()( = ,
with: )(vPS , )(vPWT – functional dependences of WPP and
wind turbine power (in p.u.) on wind speed at hub height.
According to the obtained expressions (5) and (4), the de-
pendence of the power factor of a multi-unit WPP on the
number of units in the plant is minor. The main factors in-
fluencing the energy efficiency of the installed equipment
are the functional dependence of the wind turbine power
on the wind speed and its probability distribution at the
hub height in a given time interval at the plant location.
Since modern offshore wind turbines with a nominal power
of 3–6 MW and with a variable rotation frequency have
identical power characteristics )(vPWT [46,47], the choice of
a specific type of wind turbine also has little effect on the
energy efficiency of the WPP. Based on the analysis of 6
commercial installations with a nominal wind speed of 12–
13.5 m/s from 4 manufacturers, an averaged characteristic
of the wind turbine power was obtained for further use in
this study (Table 1).
Table 1. Averaged power characteristics of the offshore wind turbine
v, m/s )(vPWT , p.u. v, m/s )(vPWT , p.u. v, m/s )(vPWT , p.u.
3.5 0.0 9.0 0.55 12.5 0.99
4.5 0.05 10.0 0.75 13.0 0.995
5.0 0.08 10.5 0.85 13.5 1.0
6.0 0.15 11.0 0.92 14.0 1.0
7.0 0.26 11.5 0.97 25.0 1.0
8.0 0.40 12.0 0.98 >25.0 0.0
The hub height of most of the mentioned wind turbines is
in the range of 90–135 m above sea level. To perform a nu-
merical experiment to assess energy efficiency, we will also
use the following value of the product of the coefficients kg,
ka, kl in (5):
( ) ( ) ( ) 9.0= NkNkNk lag . (6)
Now let us consider the peculiarities of the wind speed
probability density function determined at the wind tur-
bine’s hub height f(v), which is included in the formula for
energy efficiency (5). The function f(v) is an analytical gen-
eralization of the series of real-time measurements (mod-
eling) of wind speed ordered in magnitude increasing [48].
For wind conditions in the Azov-Black Sea region of Ukraine
at heights above the earth's (sea) surface of about 100 m,
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Відновлювана енергетика. № 3/2025 | Вітроенергетика
it is advisable to use the analytical expression of the Weibull
distribution, which was substantiated in [43]:
−= − v
vvf exp)()( 1
, 0v ; (7)
with v – wind speed, – scale parameter; – shape pa-
rameter.
Evaluation of wind speed distribution parameters based on
measurement results
Jjv j ,,2,1, = , (8)
will be performed with the use of the maximum likelihood
method [49,50]. First, the value v of the average wind
speed is determined:
=
=
J
j
jv
J
v
1
1
, (9)
and then the following quantities are computed:
dispersion (D) –
=
−
−
=
J
j
j vv
J
D
1
2)(
1
1
, m2/s2 ; (10)
standard deviation (s) – Ds = , m/s; (11)
coefficient of variation (kv) – vskv = , (12)
shape parameter –
075.1−
= vk , (13)
scale parameter –
1
1
1
=
=
J
i
jv
J
, m/s. (14)
Taking into account that the average value of the wind
speed is equal to the first moment of the distribution (7),
we obtain:
( )
=
− ==
−
0 1
1 1
exp
J
j
jv
J
vdv
v
vv
. (15)
Equation (15) is a direct functional relationship between
the parameters of the probability density and the average
value of the wind speed computed on the measurement re-
sults.
Based on the analysis of (5), (15), it can be noted that aver-
age wind speed as well as scale and shape parameters are
sufficient to characterize the wind speed probability distri-
bution used to calculate the energy efficiency of a WPP:
,,v . (16)
Since the results of long-term measurements of wind speed
at the wind turbine’s hub height at the site of the WPP con-
struction are usually absent, the question arises of model-
ing the wind speed distribution based on generalized infor-
mation in available global databases. To take into account
the change in wind speed at different heights, we will use a
power function [51,52]:
vm
h
h
v
v
=
2
1
2
1 , (17)
with mv – a parameter of the vertical wind speed profile
that takes into account the influence of the surrounding
landscape on the wind speed at a given location, h1, h2
– height above sea (land) surface level, v1, v2 – wind speed
at the corresponding heights.
The peculiarities of using existing databases of wind speed
measurement (modeling) results to assess the energy effi-
ciency of wind farms for coastal and offshore areas of the
Azov-Black Sea region of Ukraine are considered in the fol-
lowing sections.
A brief review of existing offshore wind speed databases.
There are currently several global climate reanalysis data-
bases of wind speeds obtained by using a numerical atmos-
pheric model at regular spatial grid nodes based on ground-
based and satellite-based meteorological parameter meas-
urements: Era Retrospective analysis (ERA-Interim) [53],
Era Retrospective analysis fifth generation (ERA5) [54],
Modern-Era Retrospective analysis for Research and Appli-
cations Version 2 (MERRA-2) [55].
The ERA-Interim database has a spatial resolution of 79 km
and 6-hourly measurements at 10 m height for the period
from 1979 to 2019, but access via the developer website,
ECMWF, has been discontinued since June 2023. The ERA5
database contains hourly wind speed data at 100 m height
with a spatial grid size of 31 km. The National Renewable
Energy Laboratory (NREL) Web Platform for Geospatial
Analysis and Visualization of Renewable Energy Potential
[56], in particular, wind resources in North and Central
America, was created based on ERA5.
MERRA-2 provides access to monthly, daily, and hourly
wind direction and speed data at 10 and 50 m heights for
the period 1980–2021 with a spatial resolution of 50 km.
Based on MERRA-2, the National Aeronautics and Space
Administration Prediction of Worldwide Energy Resources
(NASA POWER) software package [57] was developed with
online visualization of meteorological datasets.
The suitability of satellite data for determining offshore
wind energy potential was assessed in [58] by comparing
ground-based observations with ERA5 and MERRA-2 rea-
nalysis data. It was noted that the reanalysis data for low
wind speeds (˂ 4 m/s) tend to overestimate and slightly un-
derestimate the values for areas with strong winds
(˃ 7 m/s). A comparative analysis of the power factors of
existing WPPs in Germany, Denmark, France, Sweden and
the USA with calculated values using wind speed data from
ERA5 and MERRA-2 showed [59] that the ERA5 information
is more accurate. In [60], ERA5 and MERRA-2 wind speed
data were used for a comparative analysis with actual wind
farm performance in the USA, Brazil and New Zealand. For
the United States and Brazil, the ERA5 data generally had
lower errors and higher average correlation coefficients
than MERRA-2. However, the MERRA-2 forecasts for New
Zealand were more accurate than those from ERA5.
Additional opportunities for obtaining information on wind
parameters are provided by the National Data Buoy Center
(NDBC) marine observation and monitoring infrastructure
[61], which covers water surfaces in the Pacific, Atlantic, and
Indian Oceans, open to them seas and bays. Drifting and
moored marine high-level buoys with installed anemome-
ters are used to measure wind speed. Wind speed measure-
ment data at the height of the buoy anemometer can be re-
trieved for any period up to a year with a ten-minute discrete
129
Відновлювана енергетика. № 3/2025 | Вітроенергетика
interval. However, not all stations indicated on the infra-
structure visualization map are available for data collection
due to their privacy or other reasons. Also, the operation of
many stations displayed on the map has been discontinued.
The values of the parameters of the wind speed probability
distributions of the open seas based on the results of instru-
mental studies. Let us determine the quantitative values of
the wind speed probability distribution parameters (16)
based on the available information in open sources. A long-
term assessment of onshore wind resources was performed
for the coastal area of Jeju Island in South Korea. During the
year, measurements were made at three heights using a ma-
rine weather mast installed at a distance of 1.5 km from the
coast [31]. To obtain the probability distribution of wind
speed, the measurement-correlation-prediction (MCP)
method was employed, utilizing MERRA-2 wind speed data
at a height of 50 m for a 30-year period, and then converted
to the wind turbine’s hub height using the power law. The
essence of the MCP method is that the correlation between
wind speed measurements using a marine weather mast at
the location of the future WPP and the MERRA-2 wind speed
data is checked. A sufficiently strong correlation indicates
that the method can be used to predict wind speed data at
the location of a new WPP. The MERRA-2 wind speed data
for a 30-year period are linearly transformed to the location
of the future station. The result is a data set of sufficient du-
ration for further calculations. The study used the maximum
likelihood method to determine the probability distribution
parameters. At a height of 100.5 m, the average wind speed
was 7.61 m/s, and the values of the scale and shape param-
eters were: 55.8= m/s, 89.1= , respectively.
The parameters of the wind speed probability distribution
near Taiwan Island [32] were obtained based on the results
of measurements using Light Detection and Ranging
(LiDAR) technology, which offers greater mobility of imple-
mentation compared to traditional data collection using a
weather mast. Based on the results of wind speed meas-
urements over a five-month period at a height of 100 m, a
correlation relationship was determined with the long-
term database of a coastal weather station and the follow-
ing values of the wind speed probability distribution param-
eters at the specified height at the location of the future
offshore WPP were obtained: 99.9=v m/s,
29.11= m/s, 84.1= .
For the design of offshore WPP near the eastern coast of
the Atlantic Ocean (USA), an analysis of wind speeds in the
ocean area was conducted [33]. At a wind turbine’s hub
height of 90 m, the values of the wind speed probability dis-
tribution parameters are equal to: 65.8=v m/s,
77.9= m/s, 12.2= .
The most extensive instrumental studies of wind speeds
were carried out in the North Sea near the Netherlands on
the Europlatform (EPL) [34,35] and Wintershall K13A
(K13A) [36,37] platforms. Measurements were carried out
using LiDAR technology at heights from 63 m to 291 m
above the average sea level. The probability distribution
parameters obtained from these results are given in Ta-
ble 2. An increase in the scale parameter and a decrease
in the shape parameter are observed with an increase in
the average wind speed and the height at which it was
measured. The ranges of change in the values of the
wind speed probability distribution parameters are:
( )95.1122.10 −= m/s, ( )32.295.1 −= . At heights
of 91–141 m, which correspond to the hub height of
most modern offshore wind turbines, the parameter
values are in the ranges: ( )25.1157.10 −= m/s,
( )29.205.2 −= .
Table 2. Parameters of wind speed probability distributions for locations in the North Sea
h, m 63.0 91.0 116.0 141.0 166.0 191.0 216.0 241.0 266.0 291.0
Europlatform (EPL) (51°59’ N; 3°16’ E) at a distance of 60 km from the coast for the period 2016-2020. [21]
v , m/s 9.37 9.66 9.84 9.98 10.10 10.19 10.27 10.33 10.39 10.44
α, m/s 10.58 10.91 11.11 11.27 11.40 11.51 11.60 11.67 11.73 11.79
2.24 2.20 2.16 2.13 2.09 2.07 2.05 2.03 2.02 2.01
Europlatform (EPL) (51°59’ N; 3°16’ E) at a distance of 60 km from the coast for the period 2016-2022 [22]
v , m/s 9.05 9.35 9.52 9.67 9.79 9.89 9.97 10.04 10.11 10.18
α, m/s 10.22 10.57 10.77 10.95 11.08 11.20 11.30 11.39 11.49 11.58
2.15 2.11 2.08 2.05 2.03 2.00 1.98 1.97 1.96 1.95
Wintershall K13A (K13A) (53°22’ N; 3°22’) at a distance of 101 km from the coast for the period 2016-2019 [23]
v , m/s 9.30 9.69 9.92 10.08 10.19 10.27 10.33 10.37 10.40 10.42
α, m/s 10.48 10.92 11.19 11.37 11.50 11.59 11.66 11.70 11.74 11.76
2.32 2.29 2.25 2.21 2.17 2.14 2.12 2.10 2.08 2.07
Wintershall K13A (K13A) (53°22’ N; 3°22’) at a distance of 101 km from the coast for the period 2016-2020 [24]
v , m/s 9.42 9.81 10.04 10.20 10.31 10.40 10.46 10.51 10.55 10.57
α, m/s 10.64 11.08 11.34 11.52 11.65 11.75 11.82 11.88 11.92 11.95
2.30 2.27 2.23 2.19 2.16 2.12 2.10 2.07 2.06 2.04
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Determination of the vertical wind profile parameter for
open sea based on the results of instrumental studies. The
availability of the results of synchronous instrumental meas-
urements of wind speed at 10 different heights in the North
Sea (Table 2) makes it possible to determine the dependence
of the vertical profile parameter vm of the power function
(17) on the height above the average sea surface level using
the following formula:
( )
( )
( )0
0
ln
ln
hh
vv
hm
i
i
v = , ihh 0 , ni ...2,1= , Nn , (18)
with 0h – the lowest height of the measuring device, 0v
– wind speed at a height ih , iv – wind speed at a height ih
, N – number of the measuring devices.
The calculated value of the vertical wind speed profile pa-
rameter according to (18) at a low height of the measuring
device is sensitive to errors in the measurements of 0v
caused by the orography of the terrain and shading of the
device, waves on the sea surface, and local atmospheric cir-
culation. This error affects the results of wind speed mod-
eling according to (17) at all predicted heights.
The vertical wind speed profile parameter values for loca-
tions in the North Sea, calculated by (18) with the data of
Table 2 at different heights, are given in Table 3. The lowest
height of the measuring device was 63 m, which eliminated
the influence of orography on the measurements. The vis-
ualization of the obtained functional dependences mv(h) is
presented in Fig. 1.
Functional dependencies for different locations have the
same tendency to decrease the parameter mv with increas-
ing height h, but they differ in the absolute values of the
parameter at low heights. In particular, at a height of 100 m
the difference is about 20% and decays to zero at heights
above 260 m. The duration of observations in the interval
of 5-7 years has practically no effect on mv(h).
Table 3. Vertical wind speed profile parameter values for locations in the North Sea
h, m
Europlatform (EPL) (51°59’ N; 3°16’ E) (60km) Wintershall K13A (K13A) (53°22’ N; 3°22’ E) (101km)
2016-2020 2016-2022 2016-2019 2016-2020
v , m/s mv v , m/s mv v , m/s mv v , m/s mv
63 9.37 0.0829 9.05 0.0887 9.30 0.112 9.42 0.11
91 9.66 0.0802 9.35 0.0841 9.69 0.106 9.81 0.104
116 9.84 0.0783 9.52 0.0823 9.92 0.0999 10.04 0.0987
141 9.98 0.0774 9.67 0.0811 10.08 0.0943 10.20 0.093
166 10.10 0.0756 9.79 0.08 10.19 0.0895 10.31 0.0892
191 10.19 0.0744 9.89 0.0786 10.27 0.0852 10.40 0.0849
216 10.27 0.0727 9.97 0.0774 10.33 0.0812 10.46 0.0816
241 10.33 0.0717 10.04 0.0769 10.37 0.0776 10.51 0.0787
266 10.39 0.0707 10.11 0.0768 10.40 0.0743 10.55 0.0753
291 10.44 – 10.18 – 10.42 – 10.57 –
Fig. 1. Dependences of the vertical wind profile parameter
on height above the average sea level at two locations in
the North Sea
Parameters of probability distributions of wind speed in
the Azov-Black Sea region of Ukraine.
Currently, there are no instrumental studies of offshore
wind speed in the Azov-Black Sea region in general, and in
Ukraine as well, at a height of about 100 m. However, there
are studies assessing the potential of wind energy re-
sources in the region, which have demonstrated the feasi-
bility of using wind energy on an industrial scale [10, 19, 62
– 64]. These studies employed various methods to model
wind speed at the wind turbine’s hub height.
For offshore areas of Romania, the parameters of the prob-
ability distribution of wind speeds at a distance of 20-60 km
from the coast at a height of 80 m were determined based
on ERA-Interim data for a 20-year period [39]. The wind
speed values were determined by extrapolating data from
a ground-based anemometer installed at a height of 10 m
and the following values of the probability distribution pa-
rameters were obtained: 3.7=v m/s, 19.8= m/s,
15.2= . Modeling and analysis of the energy perfor-
mance of a virtual offshore wind farm on the southwestern
Black Sea coast of Turkey [19] was carried out using hourly
NASA POWER wind speed data for 2022 at a height of 50 m
above sea level and the following values of the probability
distribution parameters were obtained: 76.5=v m/s,
49.6= m/s, 42.2= .
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The probability distribution of wind speeds for the southern
coast of the Sea of Azov at a height of 100 m was obtained
on the basis of modeling using the results of long-term wind
speed measurements at the Mysova weather station, which
is located on a cape between the Black and Azov seas [43].
The anemometer was installed at a height of 24 m above sea
level. The parameters of the wind speed probability distribu-
tion were calculated using the analytical method of mo-
ments. The average annual wind speed was estimated to be
approximately 7.5 m/s, and the values of the scale and shape
parameters were 8.45 m/s and 2.54, respectively. In [63], the
spatial distribution of the average annual wind speed over
the Sea of Azov at the heights of 50, 100 and 200 m was an-
alyzed using GIS modeling, and the amounts of potentially
generated electricity were calculated. It was found that maxi-
mum wind speeds are concentrated in the area of the north-
ern and northeastern coast of the Sea of Azov. The offshore
area of the sea is characterized by significant wind energy
potential. The parameters of the wind speed probability dis-
tributions were not determined. In [65], for the territory of
Ukraine and its sea areas, modeling of average annual wind
speeds and indicators of the technical potential of wind en-
ergy resources at heights of 10 and 100 m was carried out.
The results of observations at 70 weather stations and rea-
nalysis data for heights of 10 and 50 m were used as input
data. The parameters of the wind speed probability distribu-
tion were not determined.
In 2020, the World Bank assessed the technical potential of
stationary and floating offshore wind power in Ukraine
within 200 kilometers of the coastline [10]. Information
from the Global Wind Atlas [62] was used. The achievable
value of the installed capacity of offshore wind farms is 251
GW (183 GW – stationary, 68 GW – floating). The volumes
of electricity production by offshore wind farms and the pa-
rameters of the probability distribution of wind speeds
were not determined.
In 2023, the National Renewable Energy Laboratory (NREL,
USA), with financial support from the United States Agency
for International Development (USAID), completed the de-
velopment of an electronic database for Ukraine on the
country's wind resource at a height of 10 m to 200 m above
ground level, covering the period 2000-2022 and provided in
a high spatial resolution of 2 km and with a time interval of 5
minutes [66]. NREL provides the following options for data
retrieval: for single locations or small areas – through the RE
Data Explorer (https://www.re-explorer.org/) [67], for large
amounts of data – using application programming interface
(API) (https://developer.nrel.gov/docs/wind/wind-toolkit/
sup3rwind-ukraine-download), through the cloud-based
Highly Scalable Data Service (HSDS) on Amazon Web Services
(AWS) (https://github.com/NREL/sup3r/examples/sup3rwi
nd), directly from Open Energy Data Initiative (OEDI) cloud-
based storage [66] (nrel-pds-wtk/sup3rwind/ukraine/v1.0.0
/5min). The provided information about the wind resource
can serve as a basis for determining the parameters of the
wind speed probability distributions in the Azov-Black Sea re-
gion of Ukraine at the heights of the centers of rotation of
the rotors of modern wind turbines in the absence of instru-
mental research results.
While there are no instrumental measurements of wind
speed for the offshore waters of the Azov-Black Sea region
of Ukraine, for the northern coastal areas in Zaporizhia and
Mykolaiv regions, they were carried out at commissioned
industrial wind farms with heights of wind turbine centers
of rotation about 100 m. In accordance with the conditions
of martial law and for security reasons, let us mark the lo-
cations of wind farms by symbols LM and LZ, respectively.
Based on the results of many years of measurements, the
following quantities of the wind speeds probability distri-
butions parameters were obtained:
location LZ – 737.7=v m/s,
7325.8= m/s,
328.2= ; (19)
location LM – 584.7=v m/s,
5525.8= m/s,
445.2= . (20)
The numerical experiment and analysis of the results will be
carried out for two coastal locations on the Black and Azov
seas (19), (20) and at the location of a virtual offshore sta-
tionary wind farm in the Black Sea (LBS location) with a hub
height of 100 m above the average level of the Earth’s (sea)
surface. The location in the Black Sea was selected in accord-
ance with the offshore wind potential distribution schemes
for the maritime territory of Ukraine [10, 65] and is located
at a distance of 35.6 km from Odesa and 83.8 km from My-
kolaiv with the following geographical coordinates:
LBS (46°27’ N; 31°11’ E). (21)
Let us first determine the energy efficiency of wind farms
for coastal locations by calculating the Capacity factor using
the parameters of the wind speed probability distribution
obtained from the RE Data Explorer database for the period
2018-2022 and also from instrumental measurements (19),
(20). The input data and the obtained results are given in
Table 4 and Fig. 2.
Table 4. Parameters of probability distributions of wind speed at a height of 100 m for the period 2018-2022
Location LM Location LZ Location LBS (46°27’ N; 31°11’E)
Distribution parameters CF Distribution parameters CF Distribution parameters CF
v , m/s α, m/s v , m/s α, m/s v , m/s α, m/s
RE Data Explorer database
6.603 7.43 2.455 0.269 7.115 7.964 2.2 0.316 7.516 8.4845 2.2 0.356
Experimental data Assumption
7.584 8.553 2.445 0.362 7.737 8.733 2.328 0.375 8,0 9.0321 2.2498 0.397
https://github.com/NREL/sup3r/examples/sup3rwind
https://github.com/NREL/sup3r/examples/sup3rwind
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The obtained results show that for coastal areas, the achiev-
able CF value calculated according to the data of the RE Data
Explorer database is 26% lower than the results of instru-
mental measurements for the LM location and 16% lower for
the LZ location. Therefore, on the intended sites of new wind
power plants construction in this region, it is necessary to set
up short-term (one to two years) instrumental measuring of
wind speed to adjust long-term data from the RE Data Ex-
plorer database. It can also be noted that the contribution of
winds with a speed of 20 m/s and above to the energy effi-
ciency of wind power plants is not significant. In this range of
wind speed changes, wind turbines can be stopped to reduce
aeromechanical forces on the construction, in order to ex-
tend the operational life. It is worth noting that the Capacity
factor indicators for two coastal wind power plants obtained
during the study are consistent with the results of their op-
eration, presented in the annual reports of the respective
power companies, in particular [68].
Fig. 2. Capacity factor distribution by wind speeds for coastal wind farms in the Azov-Black Sea region of Ukraine
The results of CF calculation according to the RE Data Ex-
plorer database for the Black Sea location are shown in Fig.
3. The achievable CF value is even slightly lower than for
coastal wind farms according to the results of instrumental
measurements, which once again confirms that wind speed
values in the RE Data Explorer database for this location are
underestimated. There is an obvious need to conduct
short-term measurements in order to adjust long-term
data from the RE Data Explorer database for the sea areas
of the Azov-Black Sea region of Ukraine as well. The value
of the vertical wind profile parameter according to the RE
Data Explorer data does not change up to a height of 200 m
inclusive and is equal to 0.065, which does not coincide
with the results for the open sea (Fig. 1). Therefore, it is ad-
visable to conduct short-term measurements at no less
than two heights that cover the range of the wind turbine
hub heights.
Taking into account the obtained average annual wind
speed values for the considered wind farms based on the
measurement results and information from the database,
let us determine CF at a height of 100 m for the LBS location
under the assumption v = 8.0 m/s. Estimation of the scale
and shape parameters of the probability distribution under
this assumption can be found using the analytical depend-
ence [48, 50]:
( ) 11+=v , (22)
with Г – symbol of Gamma function.
Fig. 3. Capacity factor distribution by wind speed for a vir-
tual wind farm in the Black Sea
Equation (22) includes two unknown variables, so its approx-
imate solution was obtained with an assumption regarding
the value of . According to the results of the above analy-
sis of instrumental measurements of wind speed at a height
of 100 m in the North Sea and on the coast of the Azov-Black
Sea region, the numerical value of the parameter can be
in the range: 4.21.2 . Therefore, the estimate of the
distribution scale parameter as given in (22), obtained under
the condition 25.2= , is approximately 0321.9= m/s.
Then we can refine the value of using the method of suc-
cessive approximations from the equality:
( ) dv
v
vv
−=
−
0
1
0321.9
exp0321.9/0.8
, 2498.2= , (23)
that follows from (15).
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The results of the Capacity factor calculation under these
assumptions are also presented in Fig. 3. The achievable CF
value reaches 0.4, which is 12% higher than computed from
the data of RE Data Explorer. The limitation of the operating
wind speed range to 20 m/s can also be applied to offshore
wind farms.
The results obtained for different locations demonstrate
the key role of the parameter v in achieving the required
level of wind farm power efficiency. The values of the scale
and shape parameters of the probability affect only the in-
tensity of attaining the maximum CF value in the range of
operating wind speeds.
The intensity of CF change is characterized by the duration
of power generation at a certain level according to )(vPS .
We will calculate the duration of wind speed in an arbitrary
range of change during the year using the density function
(7):
( ) 8760expexp 21
212,1
−−
−=−=
vv
TzzT (hours), (24)
( )
−=−=
0
exp1
v
dvvfz , (25)
with 1v , 2v – limits of the wind speed range, z – proba-
bility that the random variable of wind speed will exceed
a certain given level (probabilistic assurance).
The results of computational studies of the probabilistic
assurance of the capacity )(zPS of coastal and virtual off-
shore plants, which characterize the variability of the
electricity generation process during the year, are pre-
sented in Fig. 4. They show that under the conditions of
the wind regime in the region, the stations will generate
electricity for 7780 hours per year. The duration of wind
farms’ operation with maximum capacity is expected for
coastal locations to be about 1000 hours, and for off-
shore locations to be about 1400 hours per year. The op-
eration of the plants at a capacity of more than 60% of
the nominal value will last for coastal locations LM and LZ
as well as offshore location LBS, 2300, 2500, 2800 hours
per year, respectively. The estimate of the duration for
other levels of power generation can be determined in a
similar way.
Fig. 4. Probabilistic assurance of coastal and offshore wind farms capacity on annual time interval
According to the results in Fig. 4, another characteristic of
the electricity generation process variability can be ob-
tained, which consists of estimating the duration of the
plant's operation in a specific range of power changes. In
particular, the station at the LM location will generate
power in the range from 40% to 60% of the nominal value
for 1100 hours per year (3400 – 2300 hours, Fig. 4). The du-
rations of other required power ranges are determined
similarly.
Conclusions.
1. The main parameters of the wind speed probability dis-
tribution used in wind power problems include the average
annual speed value, along with the scale and shape param-
eters. Results from different locations have shown that the
average annual wind speed value plays a crucial role in
achieving the desired level of energy efficiency in a wind
power plant. The scale and shape parameters of the distri-
bution influence only the speed at which the maximum Ca-
pacity factor is achieved within the operating wind speed
range and characterize the variability of power generation
throughout the year.
2. For the offshore territories of the Azov-Black Sea region,
there are no results of long-term instrumental studies of
wind speed. The main information for determining the pa-
rameters of the wind speed probability distribution in the
region can be RE Data Explorer, the long-term database of
renewable energy resources, which is a product of model-
ing using the results of satellite and remote sensing of the
atmosphere. However, for the development of offshore
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wind energy, it requires adjustment based on the results of
short-term (within one to two years) measurements of
wind speed at a minimum of two heights, covering the
range of the wind turbine hub heights.
3. Provided that wind energy projects for the Azov-Black
Sea region of Ukraine are implemented properly, the an-
nual Capacity factor values can reach up to 36% for coastal
wind farms and up to 40% for offshore wind farms. The pro-
cess of electricity generation will be characterized by signif-
icant variability. According to the wind regime in the region,
the plants will operate 7780 hours per year. The duration
of wind farm operation at maximum power is expected to
be about 1000 hours for coastal locations, and about 1400
hours for offshore locations, per year. The plant's operation
at over 60% of its capacity will last approximately 2400
hours per year for coastal locations and around 2800 hours
for offshore locations. Power generation in the range from
40% to 60% of the nominal value will be performed for
1100 hours per year. The developed mathematical models
enable determining the duration of wind power plant oper-
ation in arbitrary ranges of power production.
4. The Capacity factor indicators for two coastal wind farms
obtained during the study are consistent with the results of
their actual operation, as reflected in the annual reports of
the respective power companies, which confirms the feasi-
bility of applying the developed theoretical provisions in
harvesting the offshore wind energy resources of the Azov-
Black Sea region.
Funding. The study was carried out as part of the scientific
research work of the National Academy of Sciences of
Ukraine on the topic: "Scientific and technological princi-
ples of using renewable energy sources for desalination of
seawater" (Reg. No. 0123U100871).
REFERENCES
1. Offshore Magazine. – https://www.offshore-
mag.com/renewable-energy/article/14279106/dnv-
supporting-north- sea-energy-island-design [Accessed
– 4 June2025].
2. IRENA. Technical Webinar Series: Accelerating the de-
velopment of Offshore Renewables/Ocean Technolo-
gies in Small Island Developing States Webinar I: Off-
shore Wind and Floating Solar PV. 16 December 2021.
3. Rettig E., Fischhendler I., Schlecht, F. The Meaning of
Energy Islands: Towards a Theoretical Framework. En-
ergy Reviews. 2023. Vol. 187(C). DOI:
10.1016/j.rser.2023.113732
4. Marino E., Gkantou M., Malekjafarian A., Bali S. et al.
Offshore Renewable Energies: A Review towards Float-
ing Modular Energy Islands - Monitoring, Loads, Model-
ling and Control. Ocean engineering. 2024. 313:
119251. DOI: 10.1016/j.oceaneng.2024.119251
5. Bengtsson M. A Site Selection of an Energy Island in the
North Sea: Optimal Location in an Ecological and an Eco-
nomic Scenario Using a Multicriteria Decision Analysis
(MCDA). Sweden: Lund University, 2024. –
http://lup.lub.lu.se/student-papers/record/9173925
6. VaskoP.F., Verbovyi A.P., Ibrahimova M.R., Pazych S.T.
[Hydro-storage power plants are the technological basis
for integrating powerful wind and solar power plants
into the electricity system of Ukraine]. Hi-droener-
hetyka Ukrainy [Hydropower of Ukraine]. 2017. No. 1-2.
Pp. 20–25. (Ukr.)
7. Vasko P., Verbovij A., Moroz A., Pazych S., Ibragimova
M., Sahno L. Concept of accumulation of energy from
photovoltaic and wind power plants by means of sea-
water pumped hydroelectric energy storage. 2019 IEEE
6th International Conference on Energy Smart Systems
(2019 IEEE ESS). 17-19 April 2019. Kyiv. Ukraine.
Pp. 188-191. DOI: 10.1109/ESS.2019.8764167
8. Vasko P.F., Vasko V.P., Danilenko O.I., Dolyuk V.V. [Ex-
perimental studies of reactive power generation and
consumption modes by a serial wind power plant with
an asynchronous generator]. Vidnovliuvana ener-
hetyka. [Vidnovluvana energetika]. 2008. No.1. Pp. 34–
38. (Ukr.)
9. IRENA (2016). Floating Foundations: a Game Changer
for Offshore Wind Power. International Renewable En-
ergy Agency. Abu Dhabi. – https://www.irena.org/me-
dia/Files/IRENA/Agency/Publication/2016/IRENA_ Off-
shore_Wind_Floating_Foundations_2016.pdf
10. Going Global: Expanding Offshore Wind to Emerging
Markets (Vol. 42 of 66): Technical Potential for Off-
shore Wind in Ukraine - Map (English). Washington,
D.C.: World Bank Group. –https://docu-
ments1.worldbank.org/cu-
rated/en/709391586844502062/pdf/Technical-Poten-
tial-for-Offshore-Wind-in-Ukraine-Map.pdf
11. Raggovidda Vindkraftverk. – https://www.varanger-
kraft.no/lokal-kraft/vindkraft/raggovidda-
vindkraftverk/ [Accessed – June.09.2025]
12. Rusu E., Onea F. Joint Evaluation of the Wave and Off-
shore Wind Energy Resources in the Developing Coun-
tries. Energies. 2017. Vol.10. 1866. –
https://doi.org/10.3390/en10111866
13. Wind energy in Europe in 2023.
– https://windeurope.org/intelligence-platform/prod-
uct/wind-energy-in-europe-2023-statistics-and-the-
outlook-for-2024-2030/ [Accessed – 07 July2025].
14. Wind energy in Europe in 2024. – https://www.rinnova-
bili.it/wp-content/uploads/2025/02/Wind-energy-in-
Europe-2024.pdf [Accessed – 7 July2025].
15. Balog I., Ruti P.M., Tobin I., Armenio V., Vautard R. A
numerical approach for planning offshore wind farms
from regional to local scales over the Mediterranean.
Renew. Energy. 2016. Vol. 85. Pp. 395-405. –
http://dx.doi.org/10.1016/j.renene.2015.06.038
16. Onea F., Deleanu L., Rusu L., Georgescu C. Evaluation of
the wind energy potential along the Mediterranean Sea
coasts. Energy Explor. Exploit. 2016. Vol. 34. Pp. 766-
792. – http://dx.doi.org/10.1177/0144598716659592
17. Mahdy M., Bahaj A.S., Alghamdi A.S. Offshore Wind En-
ergy Potential Around the East Coast of the Red Sea,
KSA. ISES Conference Proceedings (2017). –
https://doi.org/10.1016/j.oceaneng.2024.119251
http://lup.lub.lu.se/student-papers/record/9173925
https://ieeexplore.ieee.org/abstract/document/8764167/
https://ieeexplore.ieee.org/abstract/document/8764167/
https://ieeexplore.ieee.org/abstract/document/8764167/
https://www.irena.org/media/Files/IRENA/Agency/Publication/2016/IRENA_%20Offshore_Wind_Floating_Foundations_2016.pdf
https://www.irena.org/media/Files/IRENA/Agency/Publication/2016/IRENA_%20Offshore_Wind_Floating_Foundations_2016.pdf
https://www.irena.org/media/Files/IRENA/Agency/Publication/2016/IRENA_%20Offshore_Wind_Floating_Foundations_2016.pdf
https://www.varanger-kraft.no/lokal-kraft/vindkraft/
https://www.varanger-kraft.no/lokal-kraft/vindkraft/
https://windeurope.org/intelligence-platform/product/wind-energy-in-europe-2023-statistics-and-the-outlook-for-2024-2030/
https://windeurope.org/intelligence-platform/product/wind-energy-in-europe-2023-statistics-and-the-outlook-for-2024-2030/
https://windeurope.org/intelligence-platform/product/wind-energy-in-europe-2023-statistics-and-the-outlook-for-2024-2030/
https://www.rinnovabili.it/wp-content/uploads/2025/02/Wind-energy-in-Europe-2024.pdf
https://www.rinnovabili.it/wp-content/uploads/2025/02/Wind-energy-in-Europe-2024.pdf
https://www.rinnovabili.it/wp-content/uploads/2025/02/Wind-energy-in-Europe-2024.pdf
135
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https://proceedings.ises.org/conference/swc2017/pa-
pers/swc2017-0247-Mahdy.pdf
18. Almasoudi M., Hemida H., Sharifi S. Offshore Energy Is-
land for Sustainable Water Desalination—Case Study
of KSA. Sustainability. 2025. 17. 6498. –
https://doi.org/10.3390/su17146498
19. Aydogan B. Offshore wind power atlas of the Black Sea
Region. Journal of Renewable and Sustainable Energy.
2017. 9(1):013305. DOI: 10.1063/1.4976968
20. Fernández-Guillamón A., Das K., Cutululis N.A., Molina-
García A. Offshore Wind Power Integration into Future
Power Systems: Overview and Trends. Journal of Ma-
rine Science and Engineering. 2019. 7(11):399.
DOI:10.3390/jmse7110399
21. Pietrasanta A.M., Shaaban M.F., Aguirre P.A., Mussati
S.F., Hamouda M.A. Simulation and Optimization of Re-
newable EnergyPowered Desalination: A Bibliometric
Analysis and Highlights of Recent Research. Sustainabil-
ity. 2023. 15(12):9180. DOI: 10.3390/su15129180
22. Zhu Z., Dingzhi Peng D., Wang H. Seawater desalination
in China: An overview. Journal of Water Reuse and De-
salination. 2018. Pp. 1-18. DOI :10.2166/wrd.2018.03.
23. Liu Z., Wang H., Zhou B., Yang D. at el. Optimal Opera-
tion Strategy for Wind-Hydrogen-Water Power Grids
Facing Offshore Wind Power Accommodation. Sustain-
ability. 2022. 14(11):6871. DOI: 10.3390/su14116871
24. Knysh K., Kudria S., Halabala M. White book 2021. [Off-
shore wind energy and "green" hydrogen: opening new
frontiers of Ukraine's energy capacity]. P. 14. –
https://www.asterslaw.com/ua/press_center/ publica-
tions/white_paper_2021_offshore_wind_energy.
(Ukr.) [Accessed – 4 July 2025].
25. Ad van Wijk, Jorgo Chatzimarkakisr. Green Hydrogen for
a European Green Deal A 2x40 GW Initiative. Brussels:
Hydrogen Europe. 40 p.
26. Memorandum of understanding between the European
Union and Ukraine on a Strategic Partnership on Bio-
methane, Hydrogen and other Synthetic Gases.
02.02.2023. P. 8. – https://energy.ec.europa.eu/ mem-
orandum-understanding-between-european-union-
and-ukraine-strategic-partnership-biomethane_en [Ac-
cessed – 4 June 2025].
27. Philip Woods, Heriberto Bustamante, Kondo-Francois
Aguey-Zinsou. The hydrogen economy –Where is the
water? Energy Nexus. Vol. 7. September 2022. 100123.
– https://doi.org/10.1016/j.nexus.2022.100123 [Ac-
cessed – June.06.2025].
28. Pidkomorna Yu. [The Ministry of Energy has completed
the discussion of the Hydrogen Strategy. 27 September
2022]. – https://mev.gov.ua/novyna/minenerho-
zavershylo-obhovorennya-vodnevoyi-stratehiyi-yuliya-
pidkomorna. (Ukr.) [Accessed – 4 July 2025].
29. [Hydrogen Strategy of Ukraine for the period until
2050. Project.] – https://www.mev.gov.ua/sites/de-
fault/files/field/file/vodneva-strategiya17.05.2024.pdf
(Ukr.) [Accessed – 7 July 2025].
30. BLOW project – pioneering 5 MW floating offshore
wind turbine in Black Sea. –
https://balkangreenenergynews.com/blow-project-pi-
oneering-5-mw-floating-offshore-wind-turbine-in-
black-sea/ [Accessed – 7 July 2025].
31. Kim B, Lee K, Ko K, Choi J. Offshore wind resource as-
sessment off the coast of Daejeong. Jeju Island using 30-
year wind estimates. Scientific Reports. 2022.
12(1):14179. DOI: 10.1038/s41598-022-18447-7
32. Hsu W.-K., Yeh C.-K. Offshore Wind Potential of West
Central Taiwan: A Case Study. Energies. 2021.
14(12):3702. DOI: 10.3390/en14123702
33. Stewart G.M, Robertson A., Jonkman J., Matthew A.,
Lackner M.A. The creation of a comprehensive
metocean data set for offshore wind turbine simula-
tions. Wind Energy. 2016. 19(6). P. 1151-1159. doi:
10.1002/we.1881.
34. Gonzalez-Aparicio I., Pian A., Verhoef J.P., Bergman G.,
Van der Werff P.A. Offshore wind energy deployment in
the North Sea by 2030: long-term measurement cam-
paign, EPL, 2016–2020. Petten: TNO PUBLIC, 2021. 45 р.
– https://publications.tno.nl›TNO-2021-R10919 [Ac-
cessed – 13 July 2025].
35. Vitulli J.A., Eeckels C., Bot E.T.G., Verhoef J.P., Bergman
G., Van der Werff P.A. Offshore wind energy deploy-
ment in the North Sea by 2030: long-term measurement
campaign. EPL, 2016-2022. Petten: TNO PUBLIC, 2023.
46 р. – https://publications.tno.nl›TNO-2022-R10578
[Accessed – 13 July 2025].
36. Gonzalez-Aparicio I., Verhoef J.P., Bergman G., Van der
Werff P.A. Offshore wind energy deployment in the
North Sea by 2030: long-term measurement campaign.
K13a, 2016-2019. Petten: TNO PUBLIC, 2020. 40 р. –
https://publications.tno.nl› TNO-2020- R11058 [Ac-
cessed – 13 July 2025].
37. Gonzalez-Aparicio I., Verhoef J.P., Bergman G., Van der
Werff P.A. Offshore wind energy deployment in the
North Sea by 2030: long-term measurement campaign.
K13a, 2016-2020. Petten: TNO PUBLIC, 2021. 45 р. –
https://publications.tno.nl› TNO-2020-R10371 [Ac-
cessed – 13 July 2025].
38. Rehman S., Irshad K., Ibrahim, N. I., AlShaikhi A., Mo-
handes M.A. Offshore Wind Power Resources Assess-
ment in the Gulf of North Suez. Sustainability. 2023. Vol.
15. 15257. DOI: 10.3390/su152115257
39. Onea F., Rusu L. A Study on the Wind Energy Potential
in the Romanian Coastal Environment. Journal of Ma-
rine Science and Engineering. 2019. 7(5):142. DOI:
10.3390/JMSE7050142
40. Hulio Z.H., et al. Techno-economic assessment of wind
power potential of Hawke’s Bay using Weibull parame-
ter: A review. Energy Strategy Reviews. 2019.
26:100375. DOI: 10.1016/j.esr.2019.100375
41. Mokhtare M.H., Keysan O. Optimal sizing of battery en-
ergy storage system for a large-scale offshore wind
power plant considering grid code constraints: A Turkish
Case Study. IET Renewable Power Generation.
18(15):3067-3076. DOI: 10.1049/rpg2.12970
42. Boccard N. Capacity factor of wind power realized val-
ues vs. estimates. SSRN Electronic Journal. Energy Pol-
icy. 37 (2009). 2679–2688. DOI: 10.2139/ssrn.1285435.
https://doi.org/10.1016/j.nexus.2022.100123
https://www.mev.gov.ua/sites/default/files/field/file/vodneva-strategiya17.05.2024.pdf
https://www.mev.gov.ua/sites/default/files/field/file/vodneva-strategiya17.05.2024.pdf
https://balkangreenenergynews.com/blow-project-pioneering-5-mw-floating-offshore-wind-turbine-in-black-sea/
https://balkangreenenergynews.com/blow-project-pioneering-5-mw-floating-offshore-wind-turbine-in-black-sea/
https://balkangreenenergynews.com/blow-project-pioneering-5-mw-floating-offshore-wind-turbine-in-black-sea/
https://doi.org/10.3390/en14123702
136
Відновлювана енергетика. № 3/2025 | Вітроенергетика
43. Vasko P.F., Mazurenko I.L. [Express analysis of probabil-
istic characteristics of wind power stations as a source
of energy for seawater desalination in the Azov-Black
Sea region of Ukraine]. Enerhotekhnolohii ta resur-
sozberezhennia [Energy Technologies & Resource Sav-
ing]. 2023. No. 4. Pp.42−56. (Ukr.)
DOI:10.33070/etars.4.2023.04
44. Rida D. Youssef. Integration of offshore wind Farms into
the local distribution Network. K/EL/00272/REP. URN
03/1641. Contractor IPSA Power Ltd, 2003. 103 р. –
https://www.osti.gov/etdeweb/
servlets/purl/20447428
45. Larsson J. Transmission Systems for Grid Connection of
Offshore Wind Farms. 2021. 63 р. – https:// www.diva-
portal.org/smash/get/ diva2:1561060/fulltext01.pdf
46. Hoeser T., Feuerstein S., Kuenzer C. DeepOWT: a global
offshore wind turbine data set derived with deep learn-
ing from Sentinel-1 data. – https://essd.coperni-
cus.org/articles/14/4251/2022/
[Accessed – 8 August 2025].
47. https://www.thewindpower.net/store_manufac-
turer_turbine_en.php?id_type=7
[Accessed – 8 August 2025].
48. Shi H., Dong Z., Xiao N., Huang Q. Wind Speed Distribu-
tions Used in Wind Energy Assessment: A Review. Fron-
tiers in Energy Research. 2021. 9:769–920. DOI:
10.3389/fenrg.2021.769920
49. IEC 61649:2008. Weibull analysis. 138 p. –
https://cdn.standards.iteh.ai/sam-
ples/12810/0145efdf64294eb0992614afdd7670d5/IEC
-61649-2008.pdf
50. Rinne H. The Weibull Distribution. A Handbook. New
York: Chapman and Hall/CR. 2008. 808 p. –
https://doi.org/ 10.1201/9781420087444
51. Manwell J.F., McGowan J.G., Rogers A.L. Wind energy
explained: theory, design and application. John Wiley &
Sons Ltd, 2009. 704 p. – http://ee.tlu.edu.vn/Por-
tals/0/2018/NLG/Sach_Tieng_Anh.pdf
52. GKD 341.003.003.006 – 2000. Sites for wind farms. Me-
teorological studies of wind characteristics. – Kyiv: Min-
istry of Fuel and Energy of Ukraine, 2001. 30 p.
53. https://www.ecmwf.int/en/forecasts/dataset/ecmwf-
reanalysis-interim [Accessed – July.14.2025].
54. http://climate.copernicus.eu/climate-reanalysis
[Accessed – July.14.2025].
55. https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/
[Accessed – July.14.2025].
56. https://wrdb.nrel.gov/ [Accessed – July.25.2025].
57. NASA Prediction of Worldwide Energy Resources
(POWER). https://power.larc.nasa.gov [Accessed –
July.14.2025].
58. Cowin E., Wang Ch., Walsh D. C. Assessing Predictions
of Australian Offshore Wind Energy Resources from Re-
analysis Datasets. Energies. 2023. 16(8):3404. DOI:
10.3390/en16083404
59. Olauson J. ERA5: The new champion of wind power
modelling? Renewable Energy. 2018. 126: 322–331.
DOI: 10.1016/j.renene.2018.03.056
60. Gruber K., Regner P., Wehrle S., Zeyringer M., Schmidt
J. Towards global validation of wind power simulations:
A multicountry assessment of wind power simulation
from MERRA-2 and ERA-5 reanalyses bias-corrected
with the global wind atlas. Energy. 2022. 238: 121520.
DOI: 10.1016/j.energy.2021.121520
61. http://www.ndbc.noaa.gov/ [Accessed – July.25.2025].
62. https://globalwindatlas.info./en/area/Ukraine [Ac-
cessed – August.15.2025].
63. Popovych N., Dzhym D., Agapova O. [Prospects for the
development of offshore wind power along the coast of
the sea of Azov]. Problemy bezperervnoi heohrafichnoi
osvity i kartohrafii. [Problems of continuous geograph-
ical education and cartography]. 2019. Issue 30. Pp. 6–
14. (Ukr.) DOI: 10.26565/2075-1893-2019-30-01
64. Petrenko K., Kuznietsov M., Ivanchenko I., Karmazin O.,
Borsuk A. [Assessment of the possibility of using wind
speed reanalysis data for wind energy calculations]. Vid-
novliuvana enerhetyka. [Vidnovluvana energetika].
2023. №3. Pp. 75–85. (Ukr.) DOI: 10.36296/1819-
8058.2023.3(74)75-85
65. [Atlas of the Energy Potential of Renewable Energy
Sources of Ukraine]. Kyiv: Instytut vidnovliuvanoi ener-
hetyky NAN Ukrainy. [Kyiv: Institute of Renewable En-
ergy of the NAS of Ukraine]. 2024. 56 p. (Ukr.)
DOI:10.36296/atlas-2024
66. Benton B.N., Buster G., Pinchuk P. et al. Super Resolu-
tion for Renewable Energy Resource Data with Wind
from Reanalysis Data (Sup3rWind) and Application to
Ukraine. –
https://docs.nrel.gov/docs/fy24osti/89241.pdf
67. https://www.re-explorer.org [Accessed – July.25.2025].
68. https://dtek.com/content/an-
nounces/pdf/dtek_ar_2020_ua_web_plus_s1179_t435
9_i6036.pdf
https://doi.org/
http://www.ndbc.noaa.gov/
https://dtek.com/content/announces/pdf/dtek_ar_2020_ua_web_plus_s1179_t4359_i6036.pdf
https://dtek.com/content/announces/pdf/dtek_ar_2020_ua_web_plus_s1179_t4359_i6036.pdf
https://dtek.com/content/announces/pdf/dtek_ar_2020_ua_web_plus_s1179_t4359_i6036.pdf
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| id | veorgua-article-557 |
| institution | Vidnovluvana energetika |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2026-07-19T01:16:53Z |
| publishDate | 2025 |
| publisher | Institute of Renewable Energy National Academy of Sciences of Ukraine |
| record_format | ojs |
| resource_txt_mv | veorgua/98/5110374a5128f7e26ad67d83de498398.pdf |
| spelling | veorgua-article-5572026-07-18T06:32:22Z THE INFLUENCE OF WIND SPEED PROBABILITY DISTRIBUTION PARAMETERS ON THE ENERGY EFFICIENCY OF COASTAL AND OFFSHORE WIND FARMS IN ENCLOSED SEAS: A CASE STUDY OF THE AZOV-BLACK SEA REGION OF UKRAINE ВПЛИВ ПАРАМЕТРІВ ІМОВІРНІСНИХ РОЗПОДІЛІВ ШВИДКОСТІ ВІТРУ НА ЕНЕРГЕТИЧНУ ЕФЕКТИВНІСТЬ ПРИБЕРЕЖНИХ ТА ОФШОРНИХ ВІТРОЕЛЕКТРИЧНИХ СТАНЦІЙ У ЗАКРИТИХ МОРЯХ НА ПРИКЛАДІ АЗОВО-ЧОРНОМОРСЬКОГО РЕГІОНУ УКРАЇНИ Vasko , P. Mazurenko , I. Sysak , R. probability, distribution, wind speed, wind farm, offshore area, energy efficiency, power, variability. імовірність, розподіл, швидкість вітру, вітроелектростанція, морська акваторія, енергетична ефективність, потужність, нерівномірність. The problems of determining the main parameters of probability distributions of wind speed, assessing their impact on the energy efficiency of wind power plants and the characteristics of power generation variability are considered. Energy efficiency is characterized by the power factor. Its calculation is performed using the Weibull distribution. The primary parameters are the average wind speed and the scale and shape parameters of the distribution. Possible limits of the ranges of parameter changes in the Azov-Black Sea region are determined. An analysis of current databases and available results of instrumental measurements of wind speed in the studied region is carried out to obtain quantitative estimates of the parameters. The need for additional short-term measurements of wind speed at the construction sites of wind power plants is justified to adjust information from multi-year databases. Mathematical models are developed, and a numerical experiment is conducted to determine the achievable annual value of capacity factor and characteristics of power generation variability by coastal and offshore plants during the year in the region.  Bibl. 68, tables 4, fig. 4.    Розглянуто питання визначення основних параметрів імовірнісних розподілів швидкості вітру, оцінки їх впливу на енергетичну ефективність вітроелектростанцій та характеристики нерівномірності генерування потужності. Енергетичну ефективність характеризовано коефіцієнтом потужності. Його розрахунок виконано з викорис-танням функції щільності розподілу Вейбула. Основними параметрами слугують середнє значення швидкості вітру, параметри масштабу і форми розподілу. Визначено можливі межі діапазонів зміни параметрів в Азово-Чорноморському регіоні. Проведено аналіз чинних баз даних та наявних результатів інструментальних вимірювань швидкості вітру в досліджуваному регіоні для отримання кількісних оцінок параметрів. Обґрунтовано необхідність проведення додаткових короткотривалих вимірювань швидкості вітру в місцях спорудження вітроелектростанцій для корегування інформації багаторічних баз даних. Розроблено математичні моделі та проведено числовий експеримент з визначення досяжного річного значення Capacity factor та характеристик нерівномірності генерування потужності прибережними та офшорними станціями протягом року в регіоні. Бібл. 68, табл. 4, рис. 4.      Institute of Renewable Energy National Academy of Sciences of Ukraine 2025-09-28 Article Article application/pdf https://ve.org.ua/index.php/journal/article/view/557 10.36296/1819-8058.2025.3(82).125-136 Vidnovluvana energetika ; No. 3(82) (2025): Scientific and applied Journal renewable energy ; 125-136 Возобновляемая энергетика; ##issue.no## 3(82) (2025): Scientific and applied Journal renewable energy ; 125-136 Відновлювана енергетика; № 3(82) (2025): Науково-прикладний журнал Відновлювана енергетика; 125-136 2664-8172 1819-8058 10.36296/1819-8058.2025.3(82) en https://ve.org.ua/index.php/journal/article/view/557/467 Copyright (c) 2025 P. Vasko , I. Mazurenko , R. Sysak https://creativecommons.org/licenses/by-nc-nd/4.0 |
| spellingShingle | probability distribution wind speed wind farm offshore area energy efficiency power variability. Vasko , P. Mazurenko , I. Sysak , R. THE INFLUENCE OF WIND SPEED PROBABILITY DISTRIBUTION PARAMETERS ON THE ENERGY EFFICIENCY OF COASTAL AND OFFSHORE WIND FARMS IN ENCLOSED SEAS: A CASE STUDY OF THE AZOV-BLACK SEA REGION OF UKRAINE |
| title | THE INFLUENCE OF WIND SPEED PROBABILITY DISTRIBUTION PARAMETERS ON THE ENERGY EFFICIENCY OF COASTAL AND OFFSHORE WIND FARMS IN ENCLOSED SEAS: A CASE STUDY OF THE AZOV-BLACK SEA REGION OF UKRAINE |
| title_alt | ВПЛИВ ПАРАМЕТРІВ ІМОВІРНІСНИХ РОЗПОДІЛІВ ШВИДКОСТІ ВІТРУ НА ЕНЕРГЕТИЧНУ ЕФЕКТИВНІСТЬ ПРИБЕРЕЖНИХ ТА ОФШОРНИХ ВІТРОЕЛЕКТРИЧНИХ СТАНЦІЙ У ЗАКРИТИХ МОРЯХ НА ПРИКЛАДІ АЗОВО-ЧОРНОМОРСЬКОГО РЕГІОНУ УКРАЇНИ |
| title_full | THE INFLUENCE OF WIND SPEED PROBABILITY DISTRIBUTION PARAMETERS ON THE ENERGY EFFICIENCY OF COASTAL AND OFFSHORE WIND FARMS IN ENCLOSED SEAS: A CASE STUDY OF THE AZOV-BLACK SEA REGION OF UKRAINE |
| title_fullStr | THE INFLUENCE OF WIND SPEED PROBABILITY DISTRIBUTION PARAMETERS ON THE ENERGY EFFICIENCY OF COASTAL AND OFFSHORE WIND FARMS IN ENCLOSED SEAS: A CASE STUDY OF THE AZOV-BLACK SEA REGION OF UKRAINE |
| title_full_unstemmed | THE INFLUENCE OF WIND SPEED PROBABILITY DISTRIBUTION PARAMETERS ON THE ENERGY EFFICIENCY OF COASTAL AND OFFSHORE WIND FARMS IN ENCLOSED SEAS: A CASE STUDY OF THE AZOV-BLACK SEA REGION OF UKRAINE |
| title_short | THE INFLUENCE OF WIND SPEED PROBABILITY DISTRIBUTION PARAMETERS ON THE ENERGY EFFICIENCY OF COASTAL AND OFFSHORE WIND FARMS IN ENCLOSED SEAS: A CASE STUDY OF THE AZOV-BLACK SEA REGION OF UKRAINE |
| title_sort | influence of wind speed probability distribution parameters on the energy efficiency of coastal and offshore wind farms in enclosed seas: a case study of the azov-black sea region of ukraine |
| topic | probability distribution wind speed wind farm offshore area energy efficiency power variability. |
| topic_facet | probability distribution wind speed wind farm offshore area energy efficiency power variability. імовірність розподіл швидкість вітру вітроелектростанція морська акваторія енергетична ефективність потужність нерівномірність. |
| url | https://ve.org.ua/index.php/journal/article/view/557 |
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