Surface wind characteristics and variability in Hong Kong

Wind speed is the most critical factor that determines wind power potential and generation. In this paper, the wind speed data of multiple years from various observation sites in North Dakota, U.S. was analyzed to assess the wind power potential. The study first applied probability density functions (PDFs) to characterize the wind speed data and fit the distributions at various heights for each observation site. The fitted distributions were then used to estimate the wind power potential based on the theoretical cubic power relationship between energy potential and wind speed. Due to the complexity of functions, the numerical integration approach was employed. The following major findings were obtained from this empirical study: (1) Weibull distribution is not always the best function to fit wind speed data, while gamma and lognormal distributions produce better fitting in many occasions; (2) For different height levels at one observation site, the best performing distributions may be different; (3) The estimation accuracies of wind energy potential based on the fitted wind speed distributions range from -4% to 3.8%; (4) The rank of energy potential estimation accuracies is not always consistent with that of goodness-of-fit for wind speed distributions. In addition, a simplified approach that only relies on the hourly mean wind speed to estimate wind power potential is evaluated. Based on the theoretical cubic relationship for wind power estimation, it was found that the simplified approach may provide significantly lower estimates of wind power potential by 42-54%. As such, this approach will become more practical if this amount of difference is to be compensated. Key words: Wind speed; Distribution; Goodness-of-fit; Wind power potential; North Dakota

With the increasing consumption of fossil fuel, reducing greenhouse gas (GHG) emissions has become a serious issue that has attracted worldwide attention. Therefore, Lebanon is currently interested in utilizing renewable energy technologies to reduce energy dependence on oil reserves and GHG emissions. The present study is focused on solar and wind power potential and the economic viability of wind/solar systems for the Rayak region in Lebanon for the first time. The input data sources for the study including the Meteorological data for wind speed and NASA database for solar energy. In the assessment of wind energy, a two-parameter Weibull distribution function was used to analyze the characteristics of wind speed. Yearly and seasonal Weibull parameters were calculated for 10 m height using the Maximum likelihood method. In addition, yearly and seasonal wind power density values were calculated. The results showed that the mean annual wind speed and wind power density values were 5.884 m/s and 124.534 W/m2, respectively, during the investigation period. It can be concluded that the value of wind power density at the region was classified as marginal wind power potential and high-scale wind turbines can be used to gather wind energy potential in the region. Furthermore, predicting wind speed depends on various atmospheric factors and random variables. Therefore, 63 ANN models are developed by varying the meteorological parameters to predict the daily wind speed in the selected region. All the models with various combinations are validated and the performances of the models are analyzed using root mean squared error. The results demonstrated that the most relevant input variables for predicting the daily wind speed were found to be temperature, pressure, and relative humidity. In the assessment of wind energy, average monthly global solar radiation data were evaluated. It is found that the annual global solar radiation was 1877.41 kWh/m2, which indicates the selected region has high solar resources and is categorized as an excellent potential class. Moreover, this study provides a comprehensive and integrated feasibility analysis of 100 MW grid-connected wind and solar projects economic projects that can be developed in the country to reduce the electricity crisis and GHG emissions. Several different economic and financial indicators were calculated. The results indicate that the wind farm is a more economical option than the solar plant because of the higher values of NPV, BCR, ALCS, and IRR as well as the lower values of EB, SB, and LCOE.

The imminent exhaustion of fossil energy resources and the increasing demand for energy were the motives for those reasonable in world to put into practice an energy policy based on rational use of energy; and on exploitation of new and renewable energy sources. After 1980, as the supply of conventional energy has not been able to follow the tremendous increase of the production demand in rural areas of the world, a renewed interest for the application of solar and wind energy has shown in many places. Therefore, the researchers and engineers began to pay more attention to wind and solar energy utilisation in rural areas. Because the wind energy resource in many rural areas is sufficient for attractive application of wind pumps, and as fuel is insufficient, the wind pumps will be spread on a rather large-scale in the near future. Wind is a form of renewable energy, which is always in a non-steady state due to the wide temporal and spatial variations of wind velocity. The need for the provision of new data stations in order to enable a complete and reliable assessment of the overall wind power potential of the country is to be identified and specific locations suggested. This article presents the background and ideas of the development of the concept as well as the main results and experience gained during ongoing projects up to now. In the world, various designs of wind machines for water pumping have been developed and some designs are presently manufactured commercially. Results suggest that wind power would be more profitably used for local and small-scale applications especially for remote rural areas. It is concluded that many parts of the world is enjoyed with abundant wind and solar energy resources.

Wind power potential assessment of 12 locations in western Himalayan region of India

Актуальность исследования. Среди наиболее острых проблем, волнующих человечество, следует выделить энергетические проблемы. Они влияют непосредственно или опосредованно на человека, общество и окружающую среду. Человеческая деятельность связана с беспрерывным использованием природных энергоресурсов для обеспечения своей жизнедеятельности: теплом и светом в домах, продуктами питания, транспортом, одеждой и т. д. До настоящего времени во многих районах Сибири отсутствует централизованное энергоснабжение. Важной особенностью горных стран является большое рассредоточение потребителей электрической и тепловой энергии, неравномерная нагрузка в течение года и отсутствие централизованных источников энергии. Основным видом энергоисточников в горных районах являются дизельные электростанции. Стоимость получаемой энергии очень велика, особенно в отдалённых районах. Решение энергетической проблемы станет возможным лишь благодаря концепции освоения горных регионов с использованием возобновляемых источников энергии. В работе рассмотрены основные закономерности ветроэнергетического потенциала центральных районов Республики Алтай. Для выбора оптимального режима работы ветроэнергетических установок исходя из мощности электрических нагрузок, а также данных, необходимых для конструирования, расчётов, оценки прочности необходимы дополнительные исследования, в том числе микроклиматические наблюдения за ветровым режимом. Цель: оценить возможный вклад энергии ветра в энергоэффективность и улучшение экологических условий в горных районах Республики Алтай при интенсивном развитии туристско-рекреационной деятельности. Объекты: ветровой режим в нижней тропосфере над югом Сибири; орографические условия и их влияние на ветровой режим; ветроэнергетический потенциал среднегорных районов Республики Алтай. Методы: температурно-ветровое зондирование атмосферы; построение математической модели поля скорости ветра в нижней тропосфере на различных высотных уровнях; оценка и анализ влияния различных форм рельефа на скорость ветра. Результаты. Показано, что в условиях межгорных котловин и широких долин скорости ветра малы для развития ветроэнергетики. Анализ аэроклиматических показателей скорости ветра и данные высокогорных метеорологических станций на вершинах и водоразделах показали, что в условиях открытости горизонта ветроэнергетический потенциал достаточен для эффективной работы ветроэнергетических установок различной мощности. Для водораздельных участков выше 1500 м удельная мощность ветрового потока составляет от 150 до 850 Вт/м2, что позволяет эксплуатировать ветроэнергетические установки малой и средней мощности. За зону большой энергетики могут быть приняты районы с удельной мощностью ветрового потока более 500 Вт/м2. Такая зона может быть выделена в центральной части Горного Алтая, начиная примерно с 2200 м.

A critical parameter in modeling studies of future decarbonized energy systems is the potential future capacity for onshore wind power. Wind power potential in energy system models is subject to assumptions regarding: (i) constraints on land availability for wind deployment; (ii) how densely wind turbines may be placed over larger areas, and (iii) allocation of capacity with respect to wind speed. By analyzing comprehensive databases of wind turbine locations and other GIS data in eleven countries and seventeen states in Australia, Canada, and the US; all with high penetration levels of wind power, we find that: i) large wind turbines are installed on most land types, even protected areas and land areas with high population density; ii) it is not uncommon with a deployment density up to 0.5 MW/km2 on municipality or county level, with rare outlier municipalities reaching up to 1.5 MW/km2 installed capacity; and iii) wind power has historically been allocated to relatively windy sites with average wind speed above 6 m/s. In many cases, allocation methods used in energy system models do not consistently reflect actual installations. For instance, we find no evidence of concentration of installations at the windiest sites, as is frequently assumed in energy system models. We conclude that assumptions made in models regarding wind power potentials are poorly reflective of historical installation patterns, and we provide new data to enable assumptions that have a more robust empirical foundation.

This study combines convolutional neural network (CNN) recognition technology, Greenwich engineering software, and statistical yearbook methods to evaluate rural solar, wind, and biomass energy resources in pilot cities in China, respectively. The CNN method enables the rapid identification of the available roof area, and Greenwich software provides wind resource simulation with local terrain adaptability. The results show that the capacity of photovoltaic power generation reaches approximately 15.63 GW, the potential of wind power is 458.3 MW, and the equivalent of agricultural waste is 433,900 tons of standard coal. The city is rich in wind, solar, and biomass resources. By optimizing the hybrid power generation system through genetic algorithms, wind energy, solar energy, biomass energy, and coal power are combined to balance the annual electricity demand in rural areas. The energy trends under different demand growth rates were predicted through the LEAP model, revealing that in the clean coal scenario of carbon capture (WSBC-CCS), clean coal power and renewable energy will dominate by 2030. Carbon dioxide emissions will peak in 2024 and return to the 2020 level between 2028 and 2029. Under the scenario of pure renewable energy (H_WSB), SO2/NOx will be reduced by 23–25%, and carbon dioxide emissions will approach zero. This study evaluates the renewable energy potential, power system capacity optimization, and carbon emission characteristics of pilot cities at a macro scale. Future work should further analyze the impact mechanisms of data sensitivity on these assessment results.

Wind energy is a clean and renewable energy source with great potential for development, but the intermittent and stochastic characteristics of wind speed have brought great challenges to the effective development and utilisation of wind energy resources, resulting in high development costs. Therefore, how to accurately assess the wind energy resources and effectively predict the wind speed has become a key issue to be solved in the current wind energy field. In view of this, the study proposes the Weibull model to model the wind speed data, and then introduces the wolf pack intelligent optimisation algorithm and improves it through the pollination mechanism to improve the accuracy of wind energy resource assessment. Secondly, considering the complexity and diversity of wind speed data characteristics, data decomposition technique, autoregressive moving average (ARIMA) model and cuckoo search algorithm are used to achieve data preprocessing, serial data modelling and hybrid prediction. The experimental results show that the Weibull model has good fitting accuracy for wind speed data, with residual sum of squares, RMSE, and average coefficient of determination of 0.05, 0.014, and 0.96, respectively, accurately reflecting the statistical characteristics of wind speed data. The wind speed prediction performance of the hybrid prediction model is good, with a maximum deviation of no more than 3% from the true value, which is significantly better than the compared VMD-ISOA-KELM model and CNN-BLSTM model, and its prediction error is relatively small. The hybrid prediction model has a smaller relative error value compared to a single algorithm, with a maximum value of less than 0.2. It has better prediction performance than the combination model, with a coefficient of determination approaching 1.0, a fitting accuracy of 0.994, a mean square error of 0.1947, a root mean square error of 0.3847, and an average absolute percentage error of 15.23%. And the research method can effectively evaluate the status of wind energy resources, with low time complexity at different data scales, taking no more than 5 seconds, and improving operational efficiency. This research method can provide strong technical support and reference basis for the development and utilisation of wind energy resources, and help to promote the sustainable development of wind energy industry.

The intent of the study reported in this paper is to make analysis of the wind speed data and annual wind energy potential at Abomsa Metehara and Ziway in Ethiopia. The wind speed data was collected from National Metrology Agency of Ethiopia, and then monthly and annual mean wind speed, wind power potential, wind energy potential and Weibull distribution parameters for these three sites have been analyzed and assessed. In the results, the monthly mean wind speeds, the measured and Weibull estimated of most probable monthly wind velocity, wind velocity carrying maximum energy, monthly wind power and wind energy density for these three sites at site locations have been estimated. Based on monthly and annually mean wind speeds at stations, annually and monthly mean wind velocities at 40 m, 60 m, 80 m, 100 m and 120 m heights via wind shear law have been extrapolated. At these heights, annual mean wind velocity carrying maximum energy, annual most probable wind velocity, annual mean wind power and annual wind energy density for these sites have been estimated. Additionally, the annual cumulative distribution function and probability density function of wind speed for the selected sites have been obtained using the Weibull distribution functions.KeywordsMean wind speedWind power densityWeibull parametersWind-driven water pump

Energy supply in Nigeria is a major problem for both large and small-scale purposes. The potential of wind power for generation and sustainable electricity supply is yet to be adequately harnessed. Therefore, this study empirically investigated wind characteristics and potentials over four meteorological stations (Calabar, Uyo, Warri, and Ikeja) within the coastal region of Nigeria using 4-year (2008–2011) wind speed data obtained from the archive of Nigerian Meteorological Agency (NiMET). The wind characteristics were evaluated using descriptive analysis. The Weibull and Rayleigh probability density function and cumulative distribution function were fitted with the actual observed wind frequencies at 10 m, and accuracy assessment of the goodness of fit was carried out using some selected error metrics. The Weibull shape factor (k) and scale parameter (c) were calculated and used to estimate Weibull wind speeds and wind power density at 10 m and hub height 70 m using power law. Results revealed that, the maximum mean monthly wind speeds of 3.88 m/s in December, 4.73 m/s in January, 3.98 m/s in April, and 8.37 m/s in August, were observed over Calabar, Uyo, Warri, and Ikeja respectively. The performance evaluation of the distribution fit revealed that RMSE (ranging from 0.01 to 0.06) and χ2 (ranging from 0.001 to 0.23) are very low for Weibull and high RMSE (0.03-0.33) and χ2(0.003-13.91) for Rayleigh, while r (>0.9) and COE (>0.8) were both high over all the stations and for the periods of study. The study concludes that Weibull model could be adopted as the best over Rayleigh for representing wind speed distributions over the coastal region and also Ikeja area can be considered as very suitable for wind turbine applications thus, the wind power potential in this area should be adequately harnessed in order to increase the power supply and reduce carbon dioxide emissions.

An extensive evaluation of wind resource using new methods and strategies for development and utilizing wind power in Mah-shahr station in Iran

The objective of this research work is to analyze wind characteristics and to assess wind power potential by selecting the best fit probability distribution function of Jhimpir Sindh Pakistan. This type of detailed investigation helps wind power generation companies in selecting suitable wind turbine and provides information of wind characteristics of potential site. Eight probability distribution functions are tested on the wind speed data from January 2015 to July 2018. Frequency bins of Weibull and Rayleigh distribution with maximum probabilities of 0.1210 and 0.1143 are most closest representation of our data. In order to, quantitatively analysis which distribution function is best fitting the local wind regime, we have applied the coefficient-of-determination, Kolmogorov-Smirnov, Chi square, Cramer-von Mises, Anderson-Darling tests along with Akaike information and Bayesian information criterion. These statistical test are used to rank the empirical distribution functions in order to identify two distribution function better fitting the actual wind speed data. After selecting two best fitted distribution functions, we analyze wind power potential and compare the error of wind power density based on these distribution functions (Weibull and Rayleigh). The power densities reported varied from 73.67 to 648.73W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . Results indicate that power densities of Weibull and Rayleigh for the candidate site are 84.67-698.65W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 83.67-1021.4W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. The highest error for Weibull and Rayleigh are 0.1850 and 0.5745, respectively. Whereas lowest error are 0.0178 and 0.0180, respectively. Complete analysis suggested that Weibull distribution function is the most suitable for Jhimpir Sindh Pakistan and the studied site is suitable for wind power production. In addition, comprehensive analysis of wind direction at the candidate site suggested that Eastern and Southeastern wind directions are predominant with 38.52% and 33.24% of the total time.

The wind energy is one of the prominent renewable energy source in India. In this research paper, monthly and annual wind power density (WPD) have been estimated based on the statistical approaches known as Weibull and Rayleigh distribution functions (WDF & RDF) for the hourly time series wind data measured during the year 2013. The main objective of this research has been to estimate the wind power potential at NIT Hamirpur wind monitoring facility of Himachal Pradesh in India and to propose feasibility of wind power plant. The proposed methodology is a new contribution for small and micro power generation for this region. Modeling of wind speed deviation is the most necessary obligation for the design of wind turbines. The variations in monthly and annual mean wind speed, standard deviation, and Weibull parameters along with the frequency distribution format of measured wind speeds have been presented in this paper. The monthly and annual probability density and cumulative distribution function's (CDF) have been derived from the frequency distribution. The probability density of WDF and RDF has been well fitted with the measured probability functions on monthly and yearly basis. The investigation shows that though the particular site is not suitable for grid connected applications yet available wind potential is suitable for isolated standalone systems like rural electrification, house hold electric appliances like battery charging, mechanical applications like water pumping for irrigation etc.

This study examines the crucial role of wind energy in mitigating global warming and promoting sustainable energy development, with a focus on the impact of climate change on wind power potential. While technological progress has facilitated the expansion of the industry, it is crucial to continue making advancements to reduce the life-cycle emissions of wind turbines and ensure their long-term sustainability. Temporal discontinuities present a significant challenge for renewable energy sources. This study highlights the potential of hybrid systems to provide consistent energy output from wind sources. It also examines the variability in wind patterns caused by climate change, acknowledging that outcomes vary depending on geographic contexts, modeling approaches, and climate projections. Notably, inconsistencies in wind speed projections from downscaled general circulation models introduce uncertainties. While specific regions, such as North America, project an increase in wind speeds, others, such as the Mediterranean, face a potential decrease. Of particular note is the forecast for a potential long-term increase in wind speeds in Northern Europe. In conclusion, the wind energy industry displays considerable potential for growth, driven by technological advancements. However, the complexities resulting from climate change necessitate further research. Such insights are crucial for informed energy policy formulation and sustainable industry progress.

There is considerable interest in the potential impact of climate change on the feasibility and predictability of renewable energy sources including wind energy. This paper presents dynamically downscaled near-surface wind fields and examines the impact of climate change on near-surface flow and hence wind energy density across northern Europe. It is shown that: Simulated wind fields from the Rossby Centre coupled Regional Climate Model (RCM) (RCAO) with boundary conditions derived from ECHAM4/OPYC3 AOGCM and the HadAM3H atmosphere-only GCM exhibit reasonable and realistic features as documented in reanalysis data products during the control period (1961–1990). The near-surface wind speeds calculated for a climate change projection period of 2071–2100 are higher than during the control run for two IPCC emission scenarios (A2, B2) for simulations conducted using boundary conditions from ECHAM4/OPYC3. The RCAO simulations conducted using boundary conditions from ECHAM4/OPYC3 indicate evidence for a small increase in the annual wind energy resource over northern Europe between the control run and climate change projection period and for more substantial increases in energy density during the winter season. However, the differences between the RCAO simulations for the climate projection period and the control run are of similar magnitude to differences between the RCAO fields in the control period and the NCEP/NCAR reanalysis data. Additionally, the simulations show a high degree of sensitivity to the boundary conditions, and simulations conducted using boundary conditions from HadAM3H exhibit evidence of slight declines or no change in wind speed and energy density between 1961–1990 and 2071–2100. Hence, the uncertainty of the projected wind changes is relatively high.