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Energy consumption in Bosnia and Herzegovina is constantly increasing, and such a trend is expected in the future. By international agreements, Bosnia and Herzegovina has committed itself to the implementation of decarbonization by 2050. This means that by then it should increase its share of energy from renewable sources, and by 2030 to over 40%. Bosnia and Herzegovina has a huge potential for the production of electricity from renewable sources, especially wind and solar energy. In this research, an analysis of the basic characteristics of the wind was performed in order to determine the energy resource of the wind at a given location and the possibility of electricity production. Measured data of wind velocities and wind directions for the time period from 1 January to the end of 2020, obtained from the Federal Hydrometeorological Institute of Bosnia and Herzegovina, were used for the analysis of wind potential in the Livno area. To determine Weibull's parameters, graphical method also known as Least Squares method (LSM) was used. The results of this investigation showed that wind energy in the analysed place can be used for the operation of small wind power plants, which have operating characteristics very similar to larger wind power plants, but the required wind speed for the production of electricity is significantly lower. For a more precise estimation of wind potential, it is necessary to perform measurements at several measuring locations in a small geographical area and in a longer time period.

An enhanced database (RetroDB) of the Spanish wind energy resources, derived from a high spatial resolution integration with the WRF model, is proposed and evaluated. RetroDB provides hourly capacity factor (CF) values for the Spanish regions, along the period of 2007–2020, with an unprecedented spatial resolution. RetroDB estimates were benchmarked based on the ERA5 global reanalysis. A comprehensive evaluation study of both RetroDB and ERA5 estimates was conducted using surface and tall mast measurements, along with actual CF values. The extent to which RetroDB and ERA5 reproduced the CF spatial variability, distribution, and ramp distribution were specifically addressed. The results showed no differences between the global and regional reanalysis performance regarding nationally aggregated wind energy estimates. Nevertheless, RetroDB clearly shows a superior performance reproducing the wind speeds' and CFs' spatial and temporal distributions. This was found to be related to the higher reliability of RetroDB reproducing the aloft winds in complex topographic areas. Overall, the results clearly indicate that, in areas such as the study region, where the wind resources are mostly associated with topographic enhancements, high spatial resolution regional reanalyses are preferable over relative coarse reanalyses (e.g., ERA5), particularly for wind energy integration studies. RetroDB database is made publicly available. [ABSTRACT FROM AUTHOR]

This study assesses the optimal levelized cost of hydrogen (LCOH) production from wind farms. The study investigates 84 locations in the south of Argentina with nodes spaced 100 km apart, as well as one location each in Chile, the USA, Spain, Japan, and Australia. A model is presented to optimize the design of the wind farm, electrolyzer plant, compressor station, and storage capacity (gaseous 160 bar), with different hydrogen demands (hydrogen dispatch constraints) and minimizing the LCOH. Hourly data for wind resources from NASA is used to define a characteristic year. The model involves energy and mass balances in hourly basis for the whole year. Sequential solution procedures with heuristic rules are implemented. The optimal costs and their relation to the main variables, as well as production and storage, are analyzed. By imposing dispatch constraints, increase in levelized cost ranges from 25 % to 40 %, and decrease in annual hydrogen production ranges from 5 % to 30 %. Best levelized cost without considering electric transport between wind farm and electrolyzer plant is 2700 USD/t. • Attain the optimal hydrogen cost from wind energy. • Analize 84 sites in a 100 km × 100 km grid covering the Argentina's south region. • Identify the representative year (2011–2020) for wind resources. • Determine the site-specific optimal wind farm-to-electrolyzer power ratio. • Investigate the influence of dispatch constraints on the optimal LCOH. [ABSTRACT FROM AUTHOR]

This paper evaluates the wind potential of some specified locations in Nigeria, and then examines the response of wind energy conversion systems (WECSs) to this potential. The study employs eight probability distribution (PD) functions such as Weibull (Wbl), Rayleigh (Ryh), Lognormal (Lgl), Gamma (Gma), Inverse Gaussian (IG), Normal (Nl), Maxwell (Mwl) and Gumbel (Gbl) distributions to fit the wind data for nine locations in Nigeria viz. Kano, Maiduguri, Jos, Abuja, Akure, Abeokuta, Uyo, Warri and Ikeja. The paper then uses the maximum likelihood (ML) method to obtain the parameters of the distributions and then evaluates the goodness of fit for the PD models to characterize the locations’ wind speeds using the minimum Root Mean Square Error (RMSE). The paper analyses the techno-economic aspect of the WECSs based on the daily average wind speed; it evaluates the performance of ten 25 kW pitch-controlled wind turbines (WT1 – WT10) with dissimilar characteristics for each location, including the cost/kWh of energy (COE) and the sensitivity analyses of the WECSs. Results reveal that Ryh distribution shows the best fit for Kano, Jos, Abeokuta, Uyo, Warri and Ikeja, while the Lgl distribution shows the best fit for Maiduguri, Abuja and Akure due to their minimum RMSE. WT7 achieves the least COE ranging from $0.0328 in Jos to $4.4922 in Uyo and WT5 has the highest COE ranging from $0.1380 in Ikeja to $53.371 in Uyo. The paper also details the sensitivity analysis for the technical and economic aspects. [ABSTRACT FROM AUTHOR]

Integration of intermittent renewable energy resources provides the potential to mitigate the impact of the variability of independent sources. The intermittent nature creates stability, reliability and power quality problems in power grids. Wind and solar energies are the most viable resources whose complementarity could be deployed in the development of hybrid renewable energy systems to enhance their performance. In this study, wind-solar resource complementarity is investigated to establish its viability in hybrid energy systems in Machakos, a rural-urban town whose geographical location is 1°31′S, 37,016′E in the Eastern region of Kenya. The study findings could be fundamental in energy planning and developments in the area to provide more flexible and dependable hybrid energy systems which would supply energy services under constantly changing conditions. Ground measurements included PVGIS simulation and experimental tools installed at a height of 20 m. Weibull's distribution model and energy potential of wind resource are investigated to characterize wind resource for energy generation. The Wind distribution revealed a positively skewed profile with scale (c) and shape (k) parameter values of 2.68 m/s (≈ 3.0 m/s) and 1.9 at a mean wind speed of 2.47 m/s, respectively. The findings revealed a wind power density of 17 W/m2 at an average wind speed of 3.0 m/s. Further, an annual solar insolation of 2130 kWh/m2 with monthly average of 178 kWh/m2 translating to daily insolation of 5.8 kWh/m2 at an installation capacity of 1 kWp is reported. The months with minimum and maximum solar insolation were July and March which recorded monthly averages of 145 kWh/m2 and 213 kWh/m2 translating to a daily average of 4.7 kWh/m2 and 6.9 kWh/m2, respectively. The understanding gained from this study could be useful to the renewable energy research community and can be extended to stakeholders in PV and wind energy systems for micro grids and utility applications.

Wind data for large areas and long time periods are for great interest in the field of wind resource assessment, mainly in regions with lack of measurement data. In the present study, the latest high-resolution ERA5 global reanalysis products of the European center for medium weather forecast is investigated for the first time over Algeria. The main goal is to assess its accuracy to reproduce wind field space-time features, as well as establishing wind maps at multi-temporal scales over the whole Algerian territory. 3-hourly wind field components at 10 m above ground level (AGL) derived from ERA5 reanalysis were extracted and analyzed for a study period of ten years (2009–2018). The extracted ERA5 wind fields were compared at daily basis against selected ground measurements data made over three selected potential locations of significant wind resource potential, namely Adrar, In-Salah and Hassi-R'mel. Comparison was conducted in terms of statistical metrics: mean bias error, root mean square error, standard deviation error and correlation coefficient (R). In addition, wind rose diagrams and Weibull probability density functions were also evaluated. The study reveals a good agreement between ERA5 reanalysis and measurement data in reproducing the wind regime for all timescales, despite a slight underestimation of the observed wind speed. Also, analysis of wind field maps at 10 m AGL shows a significant improvement of wind speed assessment along the coastal regions in comparison with earlier study using ERA-interim data. Furthermore, the area with high and permanent wind resource throughout the year was identified in central Sahara geographically bounded by coordinates (26 ∘ N, 1 ∘ E, 29 ∘ N, 5 ∘ E) and characterized by an average wind speed reaching 5.5 m / s at 10 m AGL. Extrapolated wind at 50 m and 80 m follows the same pattern, with a mean annual wind speed reaching 7 m / s and 8 m / s , respectively, over central Sahara. [ABSTRACT FROM AUTHOR]

As centralized wind power construction approaches saturation, the demand for decentralized wind power in cities, enterprises, and village is gradually increasing. Unlike the construction period of traditional large centralized wind farms, rapid, flexible, and low-cost wind resource assessment is key to the application of decentralized wind power. This paper proposes a method for assessing high-altitude wind resources, which can support the construction of decentralized wind power. The method includes unmanned aerial vehicle (UAV) and wind measuring points on the ground. The correlation is established between the intermittent short-term and high-altitude wind speed measured by UAV and the continuous long-term ground wind speed, obtaining the high-altitude and long-term wind resource situation. We build upon the original linear regression method to improve the correlation coefficient. Through gradual calculation, the correlation coefficient between the wind speed measured by UAV at and the wind speed measured by ground measuring points is increased from 0.5 to 0.7. The correlation of 5m can reach 0.85, 10m can reach 0.741 and 40m can reach 0.625. The strong correlation proves the effectiveness of this method, which can successfully use ground wind measuring point to restore wind speed at different heights in a wind field, effectively supporting the construction of decentralized wind power. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract The need for high‐resolution wind resource maps is increasing with the increase in the supply and development of wind power. Many physical downscaling models have been developed and applied to make these maps. However, as the existing models require extensive computations and time, statistical models with higher efficiency are being studied. Statistical models such as regression and machine learning models can quickly calculate wind resource maps, but they have a problem of low accuracy. This study proposes a machine learning model with new topography‐derived variables to interpret the physical characteristics of the wind. As the shape of topography, which was unable to be interpreted in previous studies, can be considered with new derived variables, a significant performance improvement was identified. The analysis was conducted using 1 km Weather Research and Forecasting (WRF) results and ERA5 reanalysis data from South Korea. Two Weibull distribution parameter maps were calculated and used as input and output data. Three collections of derived variables were devised and compared. Therefore, the multi‐resolution topography data showed the highest improvements with approximately 15% reduction in root mean square error (RMSE) for both the linear regression and machine learning models. In particular, the land area showed a decrease of 20%. The best proposed models showed an RMSE of 7% and 8% for two Weibull parameters. The results are expected to serve as a reference for continuing research and utilization of statistical models.

Summary A decade of research has shown that numerical weather prediction (NWP)‐modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high‐quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP‐modeled wind speeds in the absence of observations are needed. We address this need in the present study and propose best practices for quantifying the spread in NWP‐modeled wind speeds. We implement an ensemble approach in which we consider 24 different setups to the Weather Research and Forecasting (WRF) model. We construct the ensemble by considering variations in WRF version, WRF namelist, atmospheric forcing, and sea surface temperature (SST) forcing. Our analysis finds that the standard deviation produces more consistent estimates compared to the interquartile range and tends to be the more conservative estimator for ensemble variability. We further find that model spread increases closer to the surface and on shorter time scales. Finally, we explore methods to attribute total ensemble variability to the different ensemble components (e.g., atmospheric forcing and SST product) and find that contributions by components also vary depending on time scale. We anticipate that the methods and results presented in this paper will provide a reasonable foundation for further research into ensemble‐based wind resource data sets.

In response to the international agreement on the climate change and the energy supply diversification, the Taiwan government actively promoted the use of wind power since 2000. This paper highlighted the Taiwan's wind resources, and interactively analyzed the regulatory strategies for promoting wind power and trend analysis of wind power over the two past decades (2000–2019). In the first development stage, the electricity generation by onshore wind power rapidly grew from 1.38 gigawatt-hours (GW-h) in 2000 to 1,492.7 GW-h in 2011 because of the implementations of promotional measures and official subsidies. However, its growth rate became slow due to the restrictions on available land for the onshore wind power farms. With the promulgation of the Renewable Energy Development Act in 2009, the government not only declared preferable feed-in-tariff (FIT) rates, but also announced some incentive and promotional plans, focusing on offshore wind power farms from the early 2010s. Therefore, the electricity generation by offshore wind power significantly increased from 26.7 GW-h in 2018 to 175.4 GW-h in 2019. Using the data on the electricity generation by wind power in 2019 (i.e., 1,871.3 × 106 kW-h) and the average default value (i.e., 0.539 kg CO2/kW-h), the equivalent mitigation of CO2 emission from the wind power systems in Taiwan was thus estimated to be about one million metric tons. Based on the progressive development of wind power and other renewable resources over the past two decades, one of the sustainable development goals (SDGs) for nuclear-free homeland and 20% renewable electricity generation may be prospective in the energy transition by 2025. However, this goal has faced serious challenges because the corona virus disease 2019 (COVID-19) has caused the delays in the Taiwanese wind power industry.

Trends in wind resources are a decisive factor for wind turbine site selection. Past studies assessed the long-term wind resource trends independent of wind direction. They enable the selection of the wind farm site but not the optimum design of wind turbines within the wind farm. However, possible trends in the wind direction sector-dependent wind resource would influence the optimum wind farm design. Thus, the hypothesis that wind direction sector-dependent wind resource trends exist is tested. This study applies zonal and meridional wind vector components from 1973 to 2022 on a 0.25° × 0.25° global grid at 100 m above ground level. Directional wind speed and a generic wind turbine power curve were applied to assess the trends of (1) annual energy yield, (2) capacity factor, and (3) wind direction share. The results indicate remarkable differences between the various regions and wind direction sectors. In Western and Central Europe, annual energy yield related to seven wind direction sectors decreased within the investigation period. In South Asia, declines in annual energy yields of more than −0.20 GWh related to the southwest and west sectors were found. Thus, this study concludes that more attention should be paid to wind direction sector-dependent wind resource changes. • Regional and global wind resource trends depend on the wind direction sector. • Considerable spatial variability of sector-dependent energy yield trends. • Changes of wind direction share mainly drive annual energy yield. • Trends of directional wind potential will influence wind farm layouts. [ABSTRACT FROM AUTHOR]

Assessment of the wind turbine output power (WTG OP) during the operation and maintenance is one of the key indicators of operation quality evaluation. It is often carried out in the form of the wind speed-power curve. This form only considers the wind speed, and it is usually measured according to relevant IEC standards, e.g., IEC 61400-12, which has problems such as long measurement duration and harsh conditions. This study proposes a WTG OP assessment method based on SCADA data by using the regression-kriging algorithm. The influences of wind shear, turbulence intensity, and air density on the WTG OP were analyzed. Two regression-kriging output power models were built based on SCADA data (i.e., SCADA2power model) and wind resource parameters from met mast (i.e., wind2power model). According to the evaluation of the simulation result, it was found that the results of the two models are basically consistent. Based on the evaluation of historical data under normal operating conditions, the goodness of fitting output power of the two models is 99.9%. This shows that the regression-kriging-based wind turbine power performance assessment method based on SCADA data has an accurate prediction and the potential of general application in WTG OP evaluation. [ABSTRACT FROM AUTHOR]

The need for high‐resolution wind resource maps is increasing with the increase in the supply and development of wind power. Many physical downscaling models have been developed and applied to make these maps. However, as the existing models require extensive computations and time, statistical models with higher efficiency are being studied. Statistical models such as regression and machine learning models can quickly calculate wind resource maps, but they have a problem of low accuracy. This study proposes a machine learning model with new topography‐derived variables to interpret the physical characteristics of the wind. As the shape of topography, which was unable to be interpreted in previous studies, can be considered with new derived variables, a significant performance improvement was identified. The analysis was conducted using 1 km Weather Research and Forecasting (WRF) results and ERA5 reanalysis data from South Korea. Two Weibull distribution parameter maps were calculated and used as input and output data. Three collections of derived variables were devised and compared. Therefore, the multi‐resolution topography data showed the highest improvements with approximately 15% reduction in root mean square error (RMSE) for both the linear regression and machine learning models. In particular, the land area showed a decrease of 20%. The best proposed models showed an RMSE of 7% and 8% for two Weibull parameters. The results are expected to serve as a reference for continuing research and utilization of statistical models. [ABSTRACT FROM AUTHOR]

Currently, climate change and population growth have a significant impact on human beings. Furthermore, these factors are generating a great need for energy to sustain present-day pace of life. For this reason, this research aims to determine the wind potential of the city of Chachapoyas, through the study of wind direction and speed using a wind rose. Data were obtained from the Metrological Station located on the University Campus of the National University Toribio Rodríguez de Mendoza (UNTRM) for developing this diagram. The mentioned station had 15% of missing data, therefore a quality control of the data and multiple imputations was carried out to fill in any missing data. The results obtained show that the winds in this area are mostly weak (from 0 to 3 m/s) with an East-Northeast (ENE) and Northeast (NE) direction for both the studied period and for each year of the period. It has also been determined that there is a difference between the wet and dry seasons in terms of wind frequency. Finally, we conclude that, in the city of Chachapoyas, it is possible to generate wind energy by using low-power vertical axis wind turbines. [ABSTRACT FROM AUTHOR]

Summary: A decade of research has shown that numerical weather prediction (NWP)‐modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high‐quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP‐modeled wind speeds in the absence of observations are needed. We address this need in the present study and propose best practices for quantifying the spread in NWP‐modeled wind speeds. We implement an ensemble approach in which we consider 24 different setups to the Weather Research and Forecasting (WRF) model. We construct the ensemble by considering variations in WRF version, WRF namelist, atmospheric forcing, and sea surface temperature (SST) forcing. Our analysis finds that the standard deviation produces more consistent estimates compared to the interquartile range and tends to be the more conservative estimator for ensemble variability. We further find that model spread increases closer to the surface and on shorter time scales. Finally, we explore methods to attribute total ensemble variability to the different ensemble components (e.g., atmospheric forcing and SST product) and find that contributions by components also vary depending on time scale. We anticipate that the methods and results presented in this paper will provide a reasonable foundation for further research into ensemble‐based wind resource data sets. [ABSTRACT FROM AUTHOR]

The aim of this paper is to answer the question: Are the Łódź Hills useful for electrical energy production from wind energy or not? Due to access to short-term data related to wind measurements (the period of 2008 and 2009) from a local meteorological station, the measure – correlate – predict approach have been applied. Long-term (1979‒2016) reference data were obtained from ECWMF ERA-40 Reanalysis. Artificial neural networks were used to calculate predicted wind speed. The obtained average wind speed and wind power density was 4.21 ms⁻¹ and 70 Wm⁻¹, respectively, at 10 m above ground level (5.51 ms⁻¹, 170 Wm⁻¹ at 50 m). From the point of view of Polish wind conditions, Łódź Hills may be considered useful for wind power engineering.

Summary: The power law is most often applied to extrapolate the near‐surface wind speed to the wind turbine hub height. Due to variations of the meteorological conditions, the power law exponent varies over time. Usually, no long‐term wind speed measurements from multiple heights are available which would allow time‐dependent and spatially explicit power law exponent estimations. Instead, often the mean of the power law exponent or a constant value of 0.14 is assumed. The goal of this study was to quantify the error in wind potential assessments resulting from applying the mean of the power law exponent or a value of 0.14. The data base for this study are the hourly wind speed time series at 10 and 100 m above ground available for the period 2007 to 2018 from the ERA5 reanalysis project at a global 0.25° × 0.25° grid. The errors in the estimation of the wind power density and the capacity factor were calculated. It was found that, onshore, the global median of the absolute percentage error related to the wind power density using the mean of the power law exponent is 7.5%. Assuming a constant value of 0.14, the power law is less accurate (absolute percentage error: 37.1%). For the estimation of the capacity factor the absolute percentage errors are 5.5% and 36.9%. Based on the results of this study, the use of time‐dependent and spatially explicit power law exponents is suggested. In the absence of long‐term wind speed measurements from multiple heights, the results provide a comprehensive global overview of the errors to be expected from using the mean of the power law exponent or assuming a value of 0.14. In many regions where the wind resource is abundant, using the mean of the power law exponent only leads to minor errors in capacity factor estimation. There, the assessment of wind resources with small errors is possible, even in the absence of long‐term wind speed measurements at different heights. [ABSTRACT FROM AUTHOR]

While wind assessment periods commonly range from 1 to several years, this is typically based on experience and industry norms. In this investigation, we perform an analysis of the error that can be expected in a wind resource assessment of various lengths of time. In contrast to earlier work measuring the uncertainty of predicted wind speeds, the uncertainty in this evaluation is measured directly in terms of energy and revenue production. As the wind assessment period increased from 30 days to 1 year, the average error increased slightly. However, when the wind assessment period was increased to 2 years, the average error decreased significantly. Simultaneously, the standard deviation of the error distributions decreased and the magnitude of the maximum experimentally obtained error decreased. By understanding how the energy production uncertainty decreases with increasing assessment time, the length of the assessment period can be tailored to match a developer's risk tolerance. [ABSTRACT FROM AUTHOR]

The reliability of ERA5 reanalyses for directly predicting wind resources and energy production has been assessed against observations from six tall towers installed over very heterogeneous sites around the world. Scores were acceptable at the FINO3 (Germany) offshore platform for both wind speed (bias within 1%, r = 0.95−0.96) and capacity factor (CF, at worst biased by 6.70%) and at the flat and sea-level site of Cabauw (Netherlands) for both wind speed (bias within 7%, r = 0.93−0.94) and CF (bias within 6.82%). Conversely, due to the ERA5 limited resolution (~31 km), large under-predictions were found at the Boulder (US) and Ghoroghchi (Iran) mountain sites, and large over-predictions were found at the Wallaby Creek (Australia) forested site. Therefore, using ERA5 in place of higher-resolution regional reanalysis products or numerical weather prediction models should be avoided when addressing sites with high variation of topography and, in particular, land use. ERA5 scores at the Humansdorp (South Africa) coastal location were generally acceptable, at least for wind speed (bias of 14%, r = 0.84) if not for CF (biased by 20.84%). However, due to the inherent sea–land discontinuity resulting in large differences in both surface roughness and solar irradiation (and thus stability conditions), a particular caution should be paid when applying ERA5 over coastal locations.

Wind turbines are often placed in complex terrains, where benefits from orography-related speed up can be capitalized. However, accurately modeling the wind resource over the extended areas covered by a typical wind farm is still challenging over a flat terrain, and over a complex terrain, the challenge can be even be greater. Here, a novel approach for wind resource modeling is proposed, where a linearized flow model is combined with a machine learning approach based on the k-nearest neighbor (k-NN) method. Model predictors include combinations of distance, vertical shear exponent, a measure of the terrain complexity and speedup. The method was tested by performing cross-validations on a complex site using the measurements of five tall meteorological towers. All versions of the k-NN approach yield significant improvements over the predictions obtained using the linearized model alone; they also outperform the predictions of non-linear flow models. The new method improves the capabilities of current wind resource modeling approaches, and it is easily implemented.

We compare the available wind resources for conventional wind turbines and for airborne wind energy systems. Accessing higher altitudes and continuously adjusting the harvesting operation to the wind resource substantially increases the potential energy yield. The study is based on the ERA5 reanalysis data which covers a period of 7 years with hourly estimates at a surface resolution of 31 × 31 km and a vertical resolution of 137 barometric altitude levels. We present detailed wind statistics for a location in the English Channel and then expand the analysis to a surface grid of Western and Central Europe with a resolution of 110 × 110 km. Over the land mass and coastal areas of Europe we find that compared to a fixed harvesting height at the approximate hub height of wind turbines, the wind power density which is available for 95% of the time increases by a factor of two. [ABSTRACT FROM AUTHOR]

More wind and solar power will be integrated into future power systems in China. Assessing the complementarity of wind and solar resources helps to mitigate the fluctuation of renewable power output effectively. This study comprehensively assesses the complementarity of wind and solar resources over China based on MERRA-2 reanalysis dataset. The results show that the complementarity between two resources and that between different regions for a single resource are significantly affected by the time scales. The complementarity between wind and PV power plants is superior to that between wind or solar power plants in different regions. Furthermore, the complementarity between wind and PV power can be further improved by dispersing the locations of wind farms, especially when the PV power is significantly positively correlated with wind power at the same location. However, dispersing the locations of PV plants may not work since the diurnal and seasonal solar radiation are highly similar in different regions. Extreme value analysis is employed to assess the benefits obtained from the wind-solar complementarity. The duration of no power output and the hourly negative fluctuation in power output are significantly reduced due to the complementarity. • The complementarity is more superior when two renewable resources are combined. • Dispersing the locations of wind resource can further improve the complementarity. • The complementarity between wind farms is superior to it between PV power plants. • Extreme value analysis is used to assess the benefits of wind-PV complementarity. [ABSTRACT FROM AUTHOR]

The aim of this paper is to evaluate the wind potential in southwestern Algeria, according to months, seasons and entire years and contribute to the updating of the wind map in Algeria at 10 m from the ground, using hourly data for wind collected over a period of more than 30 years. The wind data analysis was done using the Weibull function at 10 m from the ground. Then we did the statistical analysis, which includes several fundamental properties, such as Weibull parameters, mean wind speed and average power density. The results give the city of Tindouf as the one with the highest annual average speed with 5.39 m/s at 10 m from the ground. As for the temporal study, it gives that spring is the best windy period. [ABSTRACT FROM AUTHOR]

Highlights • Long-term (1971–2010) reconstruction of national and global wind energy potential. • Significant increase of wind energy potential in 37 countries. • Significant decrease of wind energy potential in 10 countries. • Inter-annual variability can be minimized by an optimized wind turbine density. Abstract In a large number of previous studies, the technical wind energy potential was estimated from national to global scale. Usually, it was assumed that the underlying meteorological potential remains constant over time. However, the wind resource greatly varies on different temporal scales including inter-annual and multi-decadal scales. In this study, the long-term variability of national and global technical wind energy potentials was assessed for the period 1971–2010 based on wind speed data from the coupled atmosphere/land-surface/ocean-wave model ERA-20C. Within this period, the annual national and global wind energy generation was reconstructed assuming an average number of 0.01–0.25 wind turbines per km2 sited on geographically non-restricted areas. The applied wind turbines have an average rated power of 3.67 MW and a hub height of 100 m. While no significant trend in technical wind energy potential was found in the majority of the countries studied, the Mann-Kendall and Cox-Stuart trend tests revealed significantly increasing trends in 37 countries and significantly decreasing trends in 10 countries. In addition, the results show that the inter-annual variability of the wind energy potential is influenced not only by the wind resource itself, but also by the rate of wind turbine expansion. From the presented results it is clear that the quantification of the long-term variability of the wind energy potential is an important prerequisite for controlling and adapting the expansion of wind energy on national and global scales to future electricity consumption. [ABSTRACT FROM AUTHOR]

Small Wind Turbines (SWTs) are promissory for distributed generation using renewable energy sources; however, their deployment in a broad sense requires to address topics related to their cost-efficiency. This paper aims to survey recent developments about SWTs holistically, focusing on multidisciplinary aspects such as wind resource assessment, rotor aerodynamics, rotor manufacturing, control systems, and hybrid micro-grid integration. Wind resource produces inputs for the rotor’s aerodynamic design that, in turn, defines a blade shape that needs to be achieved by a manufacturing technique while ensuring structural integrity. A control system may account for the rotor’s aerodynamic performance interacting with an ever-varying wind resource. At the end, the concept of integration with other renewable source is justified, according to the inherent variability of wind generation. Several commercially available SWTs are compared to study how some of the previously mentioned aspects impact performance and Cost of Electricity (CoE). Understanding these topics in the whole view may permit to identify both tendencies and unexplored topics to continue expanding SWTs market.