Wind Capacity Factor Research Articles

Murmansk Marine Biological Institute of Kola Scientific Center of the Russian Academy of Sciences

The authors assess the wind potential indicators and the scale of its use for power supply of hard-to-reach off-grid settlements in the eastern Russian Arctic. The primary challenge facing autonomous power plants is fuel delivery due to long distances and underdeveloped transport infrastructure. The use of wind power plants will reduce fuel consumption and cut the cost of energy production. The researchers use ground-based measurement data and the characteristics of existing autonomous power plants as initial data when assessing the rational capacity of wind power plants. They employ authoring research methods. Via Wind-MCA program they estimate wind potential indicators: average wind speed, capacity factor, and coefficient of wind speed variation. The research results are presented in cartographic form. The rational capacities of wind power plants as part of hybrid energy systems are determined using the generalized dependencies method based on the maximum load of a populated area and calculated wind potential indicators, taking into account the characteristic intra-annual distribution of wind speeds. The best conditions for the development of wind energy are observed on the coast of the Arctic seas in the Krasnoyarsk Territory and the Chukot Autonomous Area. The rational capacity of wind power plants in zones of high wind potential in hard-to-reach areas of eastern Russian Arctic is estimated at 12,5 MW.

Spatial integration for firm and load-following wind generation

Abstract Wind power has considerable potential to decarbonise electricity systems due to its low cost and wide availability. However, its variability is one factor limiting uptake. We propose a simple analytical framework to optimise the distribution of wind capacity across regions to achieve a maximally firm or load-following profile. We develop a novel dataset of simulated hourly wind capacity factors (CFs) with bias correction for 111 Chinese provinces, European countries and US states spanning ten years (∼10 million observations). This flexible framework allows for near-optimal analysis, integration of demand, and consideration of additional decision criteria without additional modelling. We find that spatial integration of wind resources optimising the distribution of capacities provides significant benefits in terms of higher CF or lower residual load and lower variability at sub-, quasi- and inter-continental levels. We employ the concept of firmness as achieving a reliable and certain generation profile and show that, in the best case, the intercontinental interconnection between China, Europe and the US could restrict wind CFs to within the range of 15%–40% for 99% of the time. Smaller configurations corresponding to existing electricity markets also provide more certain and reliable generation profiles than isolated individual regions.

Atmospheric mesoscale modeling to simulate annual and seasonal wind speeds for wind energy production in Mexico

Numerical models have been used widely to reproduce wind resources around the globe. Mexico’s vast territory has a wide range of geographical characteristics with abundant wind potential. This work explores WRF simulations applied to reproduce the wind speed and capacity factor (CF) of 22 wind masts, organized into seven regions delimited by geographic conditions and consisting of 33 years of data. Biases, correlations, dispersion indexes and terrain gradient are selected to study the model and experimental data annually and seasonally. Results indicate that WRF simulations show a persistent positive bias in all regions, leading to overestimating CF. In a seasonal analysis, 86% of the CF data falls between the -0.1 and 0.1 bias range. Bias is not related to a physical seasonal phenomenon; instead, it appears to be related to geographic conditions. The findings indicate that different combinations of settings should be chosen to better reflect the geographical conditions and physical phenomena that affect the intricate Mexican landscape for wind energy production. This research identify regions with best reproducibility and suggests potential areas for future research on wind energy forecasting.

Should Small Wind Pursue Traditional Region 3 Control?

A strategy to eliminate Region 3 control for small, fixed pitch turbines and utilize an extension of Region 2 is presented. Variations in wind probability distribution, capacity factor, and percent overshoot were examined. The relative performance of different control objectives was compared based on projected annual energy production. The results indicated that the Region 2 only control objective can produce energy equivalent to that of the currently employed Region 3 control objective over a range of wind regime-turbine combinations and capacity factors. It is suggested that the additional complexity required to provide Region 3 control for small turbines may not be warranted by the energy extraction benefits, particularly when factoring in additional equipment costs.

Decarbonising mining of Australia's critical mineral deposits: Opportunities for sustainable mining through solar photovoltaics and wind energy integration

Critical minerals are essential in numerous industrial applications, particularly those related to green energy production. Nonetheless, the mining and processing of these minerals are severely energy-intensive, relying predominantly on diesel, coal, and gas as sources of power in Australia. Consequently, the use of diesel, coal, and gas as power sources results in a substantial carbon footprint. An important research gap that remains to be explored in the Australian mining sector is investigating the potential for decarbonising the mining of critical minerals through the implementation of renewable energy to attain sustainable mining. This study examines the potential of using solar photovoltaics (PV) and wind energy towards the decarbonisation of critical mineral deposits, taking into account both the intrinsic mineral characteristics such as ore grade, ore depth, and co-existing mineral clusters, as well as external factors such as the capacity factor (CF) and the periods of low energy generation (lull times) of solar PV and wind, which are crucial in assessing the availability of the renewable energy sources explored. The results from this study indicate that a considerable number of critical mineral deposits in Australia are remotely located from the existing national transmission line infrastructure. These deposits often form geographical clusters and typically have favourable conditions for solar PV and wind energy implementation, characterised by high capacity factors and minimal lull times in solar PV/wind. This clustering presents a promising opportunity for powering mining activities with solar PV and wind energy, thus facilitating the decarbonisation of mining operations in Australia through the utilisation of shared renewable energy infrastructures. Moreover, the study identifies specific mineral deposits that are most suitable for decarbonisation with solar PV and wind energy integration. The research method used in this study proposes an evaluative framework that correlates the capacity factor of solar PV and wind with lull times from these sources, as well as with mineral intrinsic characteristics within deposits. This framework enables a comparative analysis of different deposits, assessing their relative suitability for the integration of solar PV and wind energy technologies. This framework is versatile and can be employed to assess different renewable energy sources, making it applicable to various global contexts. The findings from this study underscore the transformative potential of renewable energy sources, indicating that solar PV and wind energy offer new opportunities for attaining decarbonised and sustainable mining in Australia.

Spatio-temporal smoothing and dynamics of different electricity flexibility options for highly renewable energy systems—Case study for Norway

In this article, we investigate the mismatch of renewable electricity production to demand and how flexibility options enabling spatial and temporal smoothing can reduce risks of variability. As a case study we pick a simplified (partial) 2-region representation of the Norwegian electricity system and focus on wind power. We represent regional electricity production and demand through two stochastic processes: the wind capacity factors are modelled as a two-dimensional Ornstein–Uhlenbeck process and electricity demand consists of realistic base load and temperature-induced load coming from a deseasonalised autoregressive process. We validate these processes, that we have trained on historical data, through Monte Carlo simulations allowing us to generate many statistically representative weather years. For the investigated realisations (weather years) we study deviations of production from demand under different wind capacities, and introduce different scenarios where flexibility options like storage and transmission are available. Our analysis shows that simulated loss values are reduced significantly by cooperation between regions and either mode of flexibility. Combining storage and transmission leads to even more synergies and helps to stabilise production levels and thus reduces likelihoods of inadequacy of renewable power systems.

Assessment of urban wind energy resource in Hong Kong based on multi-instrument observations

Urban wind power is an appealing alternative for electricity supply. Comprehensive urban wind resource assessment is a prerequisite for cost-efficient deployment of wind turbines. Based on observations from multiple instruments, including a Doppler lidar (light detection and ranging) system, a microwave radiometer, and a cup anemometer/wind vane set, this study investigates the temporal (interannual, seasonal, and diurnal) variations of wind resources at various heights in a metropolitan city, Hong Kong. The long-term statistical distributions of wind speed, wind shear coefficient, and air density are analyzed, and their variations with height, wind direction, season, and time of day are thoroughly examined. Moreover, the wind power density and capacity factor of prototype wind turbines are estimated. It is observed that the statistical distributions of wind speed and air density are best represented by the Kappa and Normal-Weibull mixture distributions, respectively. There is a significant diurnal variation of wind shear coefficient, from 0.1 in the daytime to 0.4 at night. Moreover, a moderate potential for wind energy harvesting with wind power density of 110 W/m2 is found at the height of 120 m. This is the first known study that jointly uses lidar, microwave radiometer, and anemometer/wind vane for urban wind resource assessment. These instruments enable the investigation of the temporal variations of wind shear coefficient, air density, and wind power density at multiple heights, which were rarely examined in urban wind energy studies before.

Can offshore wind energy help to attain carbon neutrality amid climate change? A GIS-MCDM based analysis to unravel the facts using CORDEX-SA

Harnessing offshore wind energy helps to achieve carbon neutrality. However, the availability of wind resources is sensitive to climate change and also depends on the available foundation technologies of wind turbines. Investigating annual energy production (AEP) and CO2 equivalent emission avoidance using offshore wind farms helps to make appropriate energy strategies. This study uses an ensemble developed using CORDEX-South Asia regional climate models by assigning weights derived from the CRITIC multi-criteria technique to estimate AEP under two representative concentration pathways (RCP), i.e., RCP4.5 and RCP8.5 scenarios in the North Indian Ocean. To account for the impact of climate change, inter and intra-annual variations in the wind power density (WPD), capacity factor (CF), and AEP are estimated. Estimates based on the feasibility of foundation technology show that the cumulative AEP obtained from the 240 MW wind farm in historic, near- and far-future scenarios are 357.91 TWh, 808.6 TWh, and 4888.78 TWh, respectively. In the near future, harnessing offshore wind energy can reduce CO2 emissions by 4500 million tons annually. The findings of this study suggest that harnessing offshore wind energy by installing farms within the study area could help in the massive reduction of CO2 emissions leading to carbon neutrality.

Wind resource droughts in China

With the rising share of wind energy in power generation, the occurrence of low-wind-power events (termed ‘wind resource droughts’) are becoming critical in understanding the national electricity supply and the security of power systems. We use hourly wind speed data (2428 meteorological stations; in the years 2010–2020) to analyze the occurrence of wind resource droughts in seven onshore wind energy planning regions over China. We find that wind resource droughts tend to occur in warm season (summer and autumn) in most regions (i.e., Northeast China, East China, Central China, South China and Tibetan Plateau). In these regions, the number of moderate wind resource drought events (wind capacity factor below 10%) in the warm season (summer and autumn) was about 3–13 times higher than in the cold season (spring and winter). By contrast, for North China and Northwest China, the wind resource droughts mainly occurred in the autumn and winter. Averaged over 11 years, Northeast China experienced the most moderate wind resource droughts with 30 d yr−1, while Northwest China had only 1 d yr−1. Some of these wind resource drought events occurred consecutively, in which Northeast China experienced nine wind resource drought events that lasted for at least five days (where the longest reached 9 d) across the 11 years from 2010 to 2020; North China and South China exhibited one five-day wind resource drought events; while Northwest China, East China, Central China and Tibetan Plateau had none. Moreover, we found that increasing the aggregated area of wind resource can reduce the volatilities of wind energy. Therefore, improving the cross-regional transmission capacity can substantially help reduce the number of wind resource drought events. These findings should assist decision-makers to establish the counterplan to mitigate the energy shortages and instability in power supply caused by the uncertainty of wind resource droughts.

Transient modeling of a green ammonia production system to support sustainable development

Wind power intermittency makes it imperative to conduct transient modeling to highlight and address the challenges related to longer-term dynamics prior to demonstration projects. This paper presents the transient modeling and simulation of offshore wind energy integrated with a “green hydrogen” and “green ammonia” synthesis system. Since the transmission of electricity produced from far offshore sites to onshore points is challenging and submarine power cables are a significant cost, a fully off-grid offshore location is proposed for green ammonia production (power-to-ammonia) in contrast to previous studies that have examined grid-tied approaches. This approach can utilize the massive unexploited potential of remote offshore locations where wind velocities and capacity factors are comparatively higher, with produced green ammonia transported to onshore demand points via pipelines or ships. The offshore wind energy can directly be converted into ammonia using water and air as input materials, starting with the generated wind power used in the water electrolysis system to split water into hydrogen and oxygen. Nitrogen is separated by an air separation unit and mixed with the hydrogen to produce ammonia through an all-electric ammonia synthesis system. The transient analysis results include ammonia production at each hour of the year and an integrated hydrogen storage system to buffer production for nominally steady ammonia ouput throughout the year. An economic analysis is also conducted that reveals offshore wind and electrolysis as the leading cost drivers.

Evaluating emission reduction potential at the “30-60 Dual Carbon targets” over China from a view of wind power under climate change

Large-scale wind energy development is one of the main paths to achieving China's carbon peak and neutrality goals. How will the wind power and corresponding carbon abatement potential (CAP) in China change when China reaches the timing of its reduction carbon targets? This issue has not been well addressed. In this paper, a weighted multi-model ensemble with 14 global climate models from Phase 6 of the Coupled Model Intercomparison Project (CMIP6) is used to evaluate the spatio-temporal characteristics of wind speed over China during the baseline period (2004–2014). Then, we further analyze the changes in wind power and corresponding CAP due to the climate change over China in the two-level years (2030 and 2060) under the SSP2-4.5 and SSP5-8.5 scenarios. The results show that the wind capacity factor over China will have a trend of decreasing in most regions of China and increasing in the southeast in 2060. Overall, climate change will have a slight impact on the CAP of wind power in 2030, with an increase in some southern provinces. However, the CAP of wind power will decrease significantly in most regions of China in 2060 under the SSP2-4.5 scenario, especially in Shanxi, Inner Mongolia, Ningxia, and Liaoning, by more than 5 %. Under the SSP5-8.5 scenario, the CAP will decrease significantly in the southwest and northwest regions, such as Sichuan and Qinghai, by 9.86 % and 8.19 % respectively. Central and South provinces such as Hunan and Hubei will increase by about 5 %. In terms of seasonal changes, the CAP of wind power will decrease significantly in summer under the SSP2-4.5 scenario (about −5.24 %) and SSP5-8.5 scenario (about −6.50 %).These findings can help policymakers make decisions as they establish plans for wind power expansion while taking the effects of climate change into account as they work toward China's carbon neutrality goal.

Reasons for the Recent Onshore Wind Capacity Factor Increase

Increasing wind capacity and capacity factors (CF) are essential for achieving the goals set by the Paris Climate Agreement. From 2010–2012 to 2018–2020, the 3-year mean CF of the global onshore wind turbine fleet rose from 0.22 to 0.25. Wind turbine siting, wind turbine technology, hub height, and curtailed wind energy are well-known CF drivers. However, the extent of these drivers for CF is unknown. Thus, the goal is to quantify the shares of the four drivers in CF development in Germany as a case. Newly developed national power curves from high-resolution wind speed models and hourly energy market data are the basis for the study. We created four scenarios, each with one driver kept constant at the 2010–2012 level, in order to quantify the share of a driver for CF change between 2010–2012 and 2019–2021. The results indicated that rising hub heights increased CF by 10.4%. Improved wind turbine technology caused 7.3% higher CF. However, the absolute CF increase amounted to only 11.9%. It is because less favorable wind turbine sites and curtailment in the later period moderated the CF increase by 2.1% and 3.6%, respectively. The drivers are mainly responsible for perennial CF development. In contrast, variations in wind resource availability drive the enormous CF inter-annual variability. No multi-year wind resource change was detected.

Highlighting regional decarbonization challenges with novel capacity expansion model

This paper highlights the importance of regionally tailored decarbonization strategies to reach emissions intensity targets. The presented Ideal Grid model was used to compare and contrast decarbonization strategies for 9 regions of the continental US. For each of these regions, techno-economic analysis (TEA) and life-cycle assessment (LCA) are completed to track emissions intensity and electricity cost based on system installations. Ten technologies are included in this analysis: nuclear, wind, solar, natural gas (3 types), coal (3 types), and energy storage (lithium-ion batteries). The impact of carbon ceilings and carbon taxes are explored. It is shown that a carbon tax can linearly incentivize decarbonization in certain regions and exponentially incentivize decarbonization in other regions. It is shown that wind capacity factors can be used to indicate decarbonization strategies due to a strong correlation that is explored. At deep decarbonization levels (25 gCO2/kWh), regions have a varying reliance on nuclear. Regions source anywhere from 27-72% of their electricity from nuclear, with electricity costs ranging from $112/MWh to $137/MWh. At lenient decarbonization targets (100 gCO2/kWh), electricity costs range from $93/MWh to $112/MWh.

The role of electricity market design for energy storage in cost-efficient decarbonization

The role of electricity market design for energy storage in cost-efficient decarbonization

Techno-economic assessment of offshore wind energy potential at selected sites in the Gulf of Guinea

Offshore wind power has been found to stand out among the most dynamic renewable energy technologies. With its long coastal line, Nigeria has an overwhelming advantage in developing marine energy resources to relieve the power crisis effectively. This work analyzed and characterized observation data of sea-surface wind speed and direction at 30-minute intervals between 1979 and 2015 at five synoptic offshore stations in the Gulf of Guinea. The seasonal variations in hourly surface wind speed and directions as well as the Weibull distribution of wind speed and wind power at 100 m hub height were examined. The wind shears, capacity factors, and accumulated energy outputs for seven offshore wind turbine types were determined for the selected locations. In addition, the economic analysis of the selected offshore turbines using levelized cost of energy was carried out, while sensitivity analysis of the total levelized cost of energy to key input parameters was further determined. The results revealed large spatial and temporal variations in wind speed and wind power in the Gulf of Guinea. The most viable offshore site for wind energy exploitation was Agbami (the deepest offshore site), while Bonny (the shallow coastal site) had the least. The findings established very good fits (having mean bias (between −0.08 ms−1 and −2.44 ms−1), percentage bias (between −0.47% and −13.98%), correlation coefficients (between 0.97 and 0.98), Chi-square (between 0.2 and 1.2), and root mean square error (between 1.2 ms−1 and 3.1 ms−1)) between Weibull distribution and the actual wind data. The wind turbines with the highest and the lowest wind power densities, capacity factors, and power outputs across the seasons and sites were V236-15.0 MW and Siemens SWT113, respectively. The levelized cost of energy was considered for the deep waters due to the moderately high-capacity factors. The highest values ranged between 101.48 and 137.12 USD/MWh at Sea Eagle with V236-15 MW and V117-4.2 MW, respectively, while the lowest ranged from 52.29 to 69.66 USD/MWh at Agbami with V236-15 MW and Siemens (SWT113), respectively. The exploitation of Nigeria’s offshore wind resources could also be dedicated to producing renewable hydrogen and can serve to meet the country’s ambitious targets set for carbon neutrality by 2060.

Assessing Predictions of Australian Offshore Wind Energy Resources from Reanalysis Datasets

Offshore wind farms are a current area of interest in Australia due to their ability to support its transition to renewable energy. Climate reanalysis datasets that provide simulated wind speed data are frequently used to evaluate the potential of proposed offshore wind farm locations. However, there has been a lack of comparative studies of the accuracy of wind speed predictions from different reanalysis datasets for offshore wind farms in Australian waters. This paper assesses wind speed distribution accuracy and compares predictions of offshore wind turbine power output in Australia from three international reanalysis datasets: BARRA, ERA5, and MERRA-2. Pressure level data were used to determine wind speeds and capacity factors were calculated using a turbine bounding curve. Predictions across the datasets show consistent spatial and temporal variations in the predicted plant capacity factors, but the magnitudes differ substantially. Compared to weather station data, wind speed predictions from the BARRA dataset were found to be the most accurate, with a higher correlation and lower average error than ERA5 and MERRA-2. Significant variation was seen in predictions and there was a lack of similarity with weather station measurements, which highlights the need for additional site-based measurements.

Investment in wind-based hydrogen production under economic and physical uncertainties

This paper evaluates the economic viability of a combined wind-based green-hydrogen facility from an investor’s viewpoint. The paper introduces a theoretical model and demonstrates it by example. The valuation model assumes that both the spot price of electricity and wind capacity factor evolve stochastically over time; these state variables can in principle be correlated. Besides, it explicitly considers the possibility to use curtailed wind energy for producing hydrogen. The model derives the investment project’s net present value (NPV) as a function of hydrogen price and conversion capacity. Thus, the NPV is computed for a given price and a range of capacities. The one that leads to the maximum NPV is the ‘optimal’ capacity (for the given price). Next, the authors estimate the parameters underlying the two stochastic processes from Spanish hourly data. These numerical estimates allow simulate hourly paths of both variables over the facility’s expected useful lifetime (30 years). According to the results, green hydrogen production starts becoming economically viable above 3 €/kg. Besides, it takes a hydrogen price of 4.7 €/kg to reach an optimal conversion capacity half the capacity of the wind park. The authors develop sensitivity analyses with respect to wind capacity factor, curtailment rate, and discount rate.

Resource assessment for green hydrogen production in Kazakhstan

Kazakhstan has long been regarded as a major exporter of fossil fuel energy. As the global energy sector is undergoing an unprecedented transition to low-carbon solutions, new emerging energy technologies, such as hydrogen production, require more different resource bases than present energy technologies. Kazakhstan needs to consider whether it has enough resources to stay competitive in energy markets undergoing an energy transition. Green hydrogen can be made from water electrolysis powered by low-carbon electricity sources such as wind turbines and solar panels. We provided the first resource assessment for green hydrogen production in Kazakhstan by focusing on three essential resources: water, renewable electricity, and critical raw materials. Our estimations showed that with the current plan of Kazakhstan to keep its water budget constant in the future, producing 2–10 Mt green hydrogen would require reducing the water use of industry in Kazakhstan by 0.6–3% or 0.036–0.18 km3/year. This could be implemented by increasing the share of renewables in electricity generation and phasing out some of the water- and carbon-intensive industries. Renewable electricity potential in South and West Kazakhstan is sufficient to run electrolyzers up to 5700 and 1600 h/year for wind turbines and solar panels, respectively. In our base case scenario, 5 Mt green hydrogen production would require 50 GW solar and 67 GW wind capacity, considering Kazakhstan's wind and solar capacity factors. This could convert into 28,652 tons of nickel, 15,832 tons of titanium, and many other critical raw materials. Although our estimations for critical raw materials were based on limited geological data, Kazakhstan has access to the most critical raw materials to support original equipment manufacturers of low-carbon technologies in Kazakhstan and other countries. As new geologic exploration kicks off in Kazakhstan, it is expected that more deposits of critical raw materials will be discovered to respond to their potential future needs for green hydrogen production.

Historical wind power in Karnataka differs from predictive models: A granular analysis of performance across climatology, technology, and location

Wind power constitutes one of the largest forms of renewable energy in India. Many operational models have predicted wind energy in India, but most rely on satellite reanalysis data. In contrast, this paper examines historical generation data at an unprecedented granularity to understand its potential and variance. We find actual wind capacity factor (CF) during the Indian Summer Monsoon to be less than half that predicted in NREL’s recent landmark production cost modeling study. Further, we find that the yearly moving average of aggregated CF exhibits a +0.640 correlation with the ENSO SOI, a widely-used climatological parameter. At the feeder level, we confirm an expected exponential relationship between average CF and turbine hub height. However, this effect is 261% more pronounced during the monsoon, a finding with troublesome implications for grid planning. We also test the assumption that higher wind turbines perform proportionately better outside of the monsoon and find no evidence to support it. Finally, we present a new method to measure the geographic smoothing effect that reduces inherent variability by 18% when compared to normalized standard deviation. We find limits to the effect of decreasing variability through geographic diversity; the effect mostly disappears at about 125 km.

Wind speed Weibull distribution and wind energy potential of Chandpur, Bangladesh

Weibull distribution goes particularly well with wind speed data and is necessary to assess wind energy harnessing potential at a site. For the best estimation of the statistical distribution parameters at Chandpur (23.21116° N; 90.64237° E), located at the southern part of Bangladesh, six models: graphical method, method of moments, power density method, Justus method, Lysen method and maximum likelihood method have been analyzed for 18.8 m, 40.2 m and 59.9 m above ground level (AGL) using measured wind speed data for 2014-2017. Out of the six models, Justus method has showed the best result for estimation of Weibull parameters at all heights AGL with average RMSE of 0.0082 and R2 of 0.9650. Further more to evaluate the wind energy potential at modern wind turbine hub height of 100 m, firstly the wind shear exponent at the site (the average value is 0.37 considering the 10 m elevation of the site above sea level) is estimated from the wind data of 3 heights; secondly average wind speed (4.88 m/s), wind power density (108.8 Wm-2), Weibull distribution parameters (shape factor 2.29 and scale factor 5.33 m/s) and wind turbine power curve related wind speeds (cut in 2.28 m/s, rated 9.33 m/s and cut out 21.03 m/s)have been extrapolated at the hub height. The average wind speed and wind power density values show that the class of the site is 1 in the worldwide accepted wind power classification, based on long term practical wind farm installation experiences; the low cut in speed (<3 m/s) shows that suitable wind turbines for the site are commercially unavailable for the site and turbines with 3 m/s cut in speed have average annual capacity factor of 16.62 %, less than the commercially viable capacity factor >25 %. The wind power class (< class 3) and capacity factor (<25 %) strictly reveals that Chandpur is not suitable for wind energy exploration in near future until a high technical improvement of wind turbines and reduction of relevant costs. DUJASE Vol. 6 (2) 99-108, 2021 (July)