Average Wind Power Research Articles

The wind energy potential of Iceland

Downscaling simulations performed with the Weather Research and Forecasting (WRF) model were used to determine the large-scale wind energy potential of Iceland. Local wind speed distributions are represented by Weibull statistics. The shape parameter across Iceland varies between 1.2 and 3.6, with the lowest values indicative of near-exponential distributions at sheltered locations, and the highest values indicative of normal distributions at exposed locations in winter. Compared with summer, average power density in winter is increased throughout Iceland by a factor of 2.0–5.5. In any season, there are also considerable spatial differences in average wind power density. Relative to the average value within 10 km of the coast, power density across Iceland varies between 50 and 250%, excluding glaciers, or between 300 and 1500 W m−2 at 50 m above ground level in winter. At intermediate elevations of 500–1000 m above mean sea level, power density is independent of the distance to the coast. In addition to seasonal and spatial variability, differences in average wind speed and power density also exist for different wind directions. Along the coast in winter, power density of onshore winds is higher by 100–700 W m−2 than that of offshore winds. Based on these results, 14 test sites were selected for more detailed analyses using the Wind Atlas Analysis and Application Program (WAsP).

Wind energy potential assessment for the offshore areas of Taiwan west coast and Penghu Archipelago

The average wind speed and wind power density of Taiwan had been evaluated at 10 m, 30 m and 50 m by simulation of mesoscale numerical weather prediction model (MM5). The results showed that wind energy potential of this area is excellent. Taiwan has offered funds to encourage the founding of offshore wind farms in this area. The purpose of this study is to make a high resolution wind energy assessment for the offshore area of Taiwan west coast and Penghu archipelago by using WAsP. The result of this study has been used to the relative financial planning of offshore wind farm projects in Taiwan. The basic inputs of WAsP include wind weather data and terrain data. The wind weather data was from a monitoring station located on a remote island, Tongi, because that all of weather stations in the area of Taiwan west coast are affected by urbanization. SRTM was selected to be used as terrain data and downloaded from CGIAR-CSI for voids problem. The coverage of considered terrain area in this assessment work is about 300 km × 400 km that made some difficulties to run wind energy assessment of the whole area with a high resolution of 100 m. So the interested area of this study is divided into 19 areas for the wind energy assessment and mapping. The assessment results show the Changhua area has best wind energy potential in the area of Taiwan west coast which power density is above 1000 W/m2 height and the areas of Penghu archipelago are above 1300 W. These results are higher than the expected from NWP. 180 of 3 MW wind turbines were used in the study of micro sitting in the Changhua area.The type and number of the wind turbines and the layout of the wind farm is similar to the prior study of Taipower Company for demonstrating the reliability of this study. The assessment result of average net annual energy production (AEP) of the wind farm is about 11.3 GWh that is very close to the prior study. The terrain effect is also studied. The average net annual energy production will decrease about 0.7 GWh if the wind turbines were moved eastward 3600 m closer to the coast because of terrain effect. As the same reason, the average net annual energy production would be increased to 11.392 GWh if the wind farm is moved westward 3600 m away from the coast.

Long-Term Wind Resource Mapping of Korean West-South Offshore for the 2.5 GW Offshore Wind Power Project

A long-term wind resource map was made to provide the key design data for the 2.5 GW Korean West-South Offshore Wind Project, and its reliability was validated. A one-way dynamic downscaling of the MERRA reanalysis meteorological data of the Yeongwang-Gochang offshore was carried out using WindSim, a Computational Fluid Dynamics based wind resource mapping software, to establish a 33-year time series wind resource map of 100 m x 100 m spatial resolution and 1-hour interval temporal resolution from 1979 to 2012. The simulated wind resource map was validated by comparison with wind measurement data from the HeMOSU offshore meteorological tower, the Wangdeungdo Island meteorological tower, and the Gochang transmission tower on the nearby coastline, and the uncertainty due to long-term variability was analyzed. The long-term variability of the wind power was investigated in inter-annual, monthly, and daily units while the short-term variability was examined as the pattern of the coefficient of variation in hourly units. The results showed that the inter-annual variability had a maximum wind index variance of 22.3% while the short-term variability, i.e., the annual standard deviation of the hourly average wind power, was <TEX>$0.041{\pm}0.001$</TEX>, indicating steady variability.

China Wind Power Development Status and Trends

Compared to nuclear power, wind power has a short construction period, high security, rich in resources and other advantages; compared to photovoltaic power generation, wind power has the power and low cost advantages. Over the past five years, benefit from the support of the localization rate and other policies, the average annual wind power in China compound growth rate of 101%, wind power industry chain upstream parts manufacturing localization, spindle bearings, control systems also need to be imported; midstream machine manufacturing market dominated by domestic-funded enterprises; downstream wind power operators also see five power generation groups, led by the central enterprises the wind turbine increasing unit capacity of 1.5MW and above the level of wind turbine occupy a major share of the market, the development of wind power is inseparable from the support of the policy. Offshore wind power has high feed-in tariff, high throughput advantage of offshore wind power will become a growth point of the wind power industry in the future.

Study on the Maximum Entropy Principle applied to the annual wind speed probability distribution: A case study for observations of intertidal zone anemometer towers of Rudong in East China Sea

This paper is to study Weibull distribution and Maximum Entropy Principle applied to fit the wind speed frequency distribution of the intertidal zone anemometer towers. Comparisons of the results from Weibull distribution and Maximum Entropy distribution are made for the characteristics of the wind speed distribution and the variation with height in the offshore area. The Maximum Entropy distribution is found to perform adequately and accurately in fitting the wind speed frequency distribution with height. It is shown that the frequency peak value of Weibull distribution is lower than the measured maximum frequency at the same height, and the fitting accuracy of the Maximum Entropy Principle is significantly higher than that of Weibull distribution. Furthermore, the mean errors of average effective wind power density calculated from the five-parameter Maximum Entropy distribution as well as from Weibull distribution are 1.71Wm−2 and 7.48Wm−2 respectively. Except for these findings, limitations and problems existing in the procedure of fitting the annual wind speed probability distribution are also discussed.

Propulsive power contribution of a kite and a Flettner rotor on selected shipping routes

Wind is a renewable energy source that is freely available on the world’s oceans. As shipping faces the challenge of reducing its dependence on fossil fuels and cutting its carbon emissions this paper seeks to explore the potential for harnessing wind power for shipping. Numerical models of two wind power technologies, a Flettner rotor and a towing kite, are linked with wind data along a set of five trade routes. Wind-generated thrust and propulsive power are computed as a function of local wind and ship velocity. The average wind power contribution on a given route ranges between 193kW and 373kW for a single Flettner rotor and between 127kW and 461kW for the towing kite. The variability of the power output from the Flettner rotor is shown to be smaller than that from the towing kite while, due to the different dependencies on wind speed and direction, the average power contribution from a Flettner rotor is higher than that from the kite on some routes and lower on others. While for most forms of international cargo shipping wind may not be suitable as the sole source of propulsive energy, a comparison of average output to main engine power requirements of typical vessels serving the routes indicates that it could deliver a significant share. For instance, installing three Flettner rotors on a 5500dwt general cargo carrier could, on average, provide more than half of the power required by the main engine under typical slow steaming conditions. Uncertainties and simplifying assumptions underlying the model analysis are discussed and implications of the results are considered in light of the urgent need for decarbonisation. This paper demonstrates the significant opportunities for step jump emissions reductions that wind technologies have to offer. It outlines next steps towards realising the potential, highlighting a demand for more detailed studies on socio-economic and technical barriers to implementation, and providing a basis for research into step-change emissions reductions in the shipping sector.

Wind Energy Data Analysis and Resource Mapping of Geba Catchment, North Ethiopia

Wind energy potential in Ethiopia is estimated to be enormous due to local peculiar landscape situations. However, the country started to utilize the potential only very recently. One of the reasons for low utilization of wind energy in Ethiopia is the absence of reliable and accurate wind energy resource data. Development of reliable and accurate wind atlas helps to identify candidate sites for wind energy applications and facilitates the planning and implementation of wind energy projects. This paper presents wind energy data analysis and wind atlas of Geba catchment in North Ethiopia. The work reported in this paper is based on wind data collected over a period of one year from measuring masts in six different sites in Tigray Ethiopia. The data was analyzed using various statistical software to evaluate the wind energy potential of the area. Average wind speed and power density, distribution of the wind prevailing direction, turbulence intensity and wind shear profile of each site were determined. Wind Atlas Analysis and Application Programme (WAsP) was used to generate the wind atlas of the area and to develop the wind speed and power density maps. The data analysis indicates that the average wind speed at 10 m above ground level (a.g.l.) varies from 3.7 m/s to 6.64 m/s. The mean power density at 10 m a.g.l varies from 64 W/m2 to 301 W/m2. The prevailing wind directions are East and South East directions. The wind resource map developed by WAsP at 50 m indicated that the catchment has good wind power potential having mean wind speed and power density of 6.5 m/s and 288 W/m2, respectively.

Wind power variations under humid and arid meteorological conditions

The classical wind power per rotor area per time is given as the half product of the air density by third power of the wind velocity. This approach adopts the standard air density as constant (1.23g/cm3), which ignores the density dependence on air temperature and pressure. Weather conditions are not taken into consideration except the variations in wind velocity. In general, increase in pressure and decrease in temperature cause increase in the wind power generation. The rate of increase in the pressure has less effect on the wind power as compared with the temperature rate. This paper provides the wind power formulation based on three meteorological variables as the wind velocity, air temperature and air pressure. Furthermore, from the meteorology point of view any change in the wind power is expressed as a function of partial changes in these meteorological variables. Additionally, weather conditions in humid and arid regions differ from each other, and it is interesting to see possible differences between the two regions. The application of the methodology is presented for two meteorology stations in Istanbul, Turkey, as representative of the humid regions and Al-Madinah Al-Monawwarah, Kingdom of Saudi Arabia, for arid region, both on daily record bases for 2010. It is found that consideration of air temperature and pressure in the average wind power calculation gives about 1.3% decrease in Istanbul, whereas it is about 13.7% in Al-Madinah Al-Monawwarah. Hence, consideration of meteorological variables in wind power calculations becomes more significant in arid regions.

An Investigation of Wind Characteristics and Techno-Economic Evaluation of Wind Energy in Nigeria

In this study, wind characteristics and techno-economic analysis in six selected locations in the northern (Jos, Kano, Sokoto and Maiduguri) and southern (Lagos and Enugu) regions of Nigeria using wind speed data at 10m height collected over a period of seventeen years (1990-2006) were analyzed. The techno-economic evaluations of electricity generation from four commercial wind turbine models used for electricity generation located at these sites were evaluated. The wind speed data analysis shows that the sites evaluated are good locations for wind potential in electricity generation from wind. The yearly energy output, the capacity factor and the wind energy cost per unit of electricity generated by the selected wind turbines are calculated. In terms of energy production, the results show that Plateau is best location for harnessing wind power for electricity generation with an average wind power density of 713.95W/m2. The maximum energy output was obtained for De wind 48 turbine model. The capacity factor values are found to vary from a minimum of 21% to maximum of 28% for this research work. The results also shows that the cost per kWh of electricity generation using these turbines is between 0.493 – 0.606$kWh.

Analysis on the Characteristics of Wind Power Output in Hainan Power Grid

It is of great importance to study the characteristics of wind power output for the healthy and secure & stable of power grid. Based on the actual operating data, the probability distribution of the power fluctuations of the wind farm in Hainanand the variation of wind power annual, seasonal, daily active output is analyzed. The study showed thatHainanProvincehas obvious seasonal variation of wind power output characteristics, higher levels of output of the year generally in winter or summer, spring and autumn to contribute small. The average wind power output will contribute to “low day and high night”, with certain peaking capacity. Shorter time scales, changes in the wind power to smaller amount, not to bring too much impact on system operation, while a long time fluctuations affect the scheduling and running on the grid.

Assessment of wind power Potential at Zeway, Central Rift Valley

Wind is one of the renewable resources in Zeway which is unexploited. This paper presents a number of issues related to the Assessment of wind energy resource based on 16 years longterm measured data of daily wind speed and direction, 3-hourly measured wind data at 10 m height, which are obtained from meteorological station and Analysis carried out By using Rayleigh stastical Analysis for wind data analysis and Matlab software used for data acquisition. This study indicates that Zeway, has energy potential available with the maximum and minimum average wind speed respectively, 4.9 m/s and 1.5 m/s and average wind power density also varies with maximum and minimum values respectively, 68.6 w/m and 0.0 w/m and the Results suggested that wind energy must be encouraged, promoted, invested, implemented, and demonstrated, but especially for small Wind farm. Key Wordswind speed and direction, Rayleigh probability density, power density, wind speed probability curve, Zeway.

Statistical analysis of wind speed and wind power potential of Port Elizabeth using Weibull parameters

This paper analyses wind speed characteristics and wind power potential of Port Elizabeth using statistical Weibull parameters. A measured 5–minute time series average wind speed over a period of 5 years (2005 - 2009) was obtained from the South African Weather Service (SAWS). The results show that the shape parameter (k) ranges from 1.319 in April 2006 to 2.107 in November 2009, while the scale parameter (c) varies from 3.983m/s in May 2008 to 7.390 in November 2009.The average wind power density is highest during Spring (September–October), 256.505W/m2 and lowest during Autumn (April-May), 152.381W/m2. This paper is relevant to a decision-making process on significant investment in a wind power project.

Wind Energy Potential Assessment as Power Generation Source in Ubonratchathani Province, Thailand

In this paper, the hourly measured wind speed data for years 2008–2010 at 10 m, 30 m and 40 m height for Ubonratchathani province in kingdom of Thailand have been statically analyzed to determine the potential of wind power generation. Weibull distribution parameters have been estimated and compared annually and on monthly bases using two methods; the graphical method and the another method, designated in this paper as approximated method, which depends on the standard deviation and average wind speed. The average annual wind power density was found to be 19.13 W/m2 for 10 m height, 83.14 W/m2 for 30 m height and 91.45 W/m2 for 40 m height. Weibull probability function, using Weibull parameters estimated from the approximated method, has shown to provide more accurate prediction of average wind speed and average power density than the graphical method. In addition, the monthly and annual variations of capacity factors have been studied to ensure optimum selection of wind turbine generators.

Wind power characterization in the Lages city - SC, Brazil

The main objective of this work was to determinate the nominal wind and the wind power, based on mathematical adjustments in the Lages city, SC-Brazil. The study used data comprising the period between 1976 and 2006. The wind average velocity was osted by Lages Meteorological Station located at 27º49'S, 50º12' W, and 937 meters of altitude. For the estimated wind temporal distribution, a methodology based on wind velocity curves was used. The nominal velocity that made wind power maximum in Lages was 10.33 km h-1 77 days long. This velocity provides an average wind power 16.6 W m-2 annually. The results showed that wind power cow be characterized as a simple alternative to be used in agriculture.

Climatic conditions for operation of wind turbines in Hungary

According to wind-climatic requirement of wind farms wind speed should exceed the so called cut-in speed. If this inequality is realized then regarding the wind-climatic features of Hungary the following conditions may occur: the wind turbine is operating with high probability, energy is generated; it is working in a regulated mode with low probability; it is not working with very low probability. Therefore in terms of continuous energy production by wind one question arises: are there any temporal and/or orographic shifts in different heights compared to the wind-climatic condition mentioned above. In this paper this question is analyzed on the basis of seven Hungarian meteorological stations that have hourly measured wind speed data considering the period between 1991 and 2000. The probability of wind speeds exceeding 3 m/s, statistics of wind speed intervals higher and lower than 3 m/s and statistics of average hourly wind speed intervals higher than 3 m/s were analyzed at the heights of 10, 30 and 60 m. A statistical parameter that is proportional to the average specific wind power of a day in a time period was defined and, its connection to the average length of those intervals that have higher or equal hourly average wind speeds more than 3 m/s in a given month was investigated. With the help of such parameters the value of monthly average specific wind power can be estimated.

A statistical model for estimating electricity produced by wind energy

Potential wind power for a given period (e.g. a day) can be determined from wind speed data measured in certain hours of a period. Obviously, the sum of the cubes of wind speeds measured depends on the number of measurements. This dependence can be reduced in two ways: determining the average and the relative wind energy for a given time within a given period. The method of sliding averages uses both. Applying this method a given hourly average wind speed cube of a day is estimated on the basis of wind speeds measured in that hour of the day. Cubes of the wind speeds are in proportion with the total daily potential and produced wind energy. This model requires long-time series of wind speed data that are available only for weather stations in Hungary, where hourly average winds speeds are registered. For this reason, statistics required for the model were calculated from different subsets of ten-year-long hourly average wind speed time series of three Hungarian weather stations (Szombathely, Budapest-Lőrinc and Debrecen). Using the statistics and hourly wind speed data measured in the vicinity of the wind turbines/on the wind turbines themselves, the model is suitable for giving estimations hourly of the potential wind energy for the whole day in a particular season or circulation type group. A software for the model is also presented here. Considering the results the sliding average model (SLIDAV) makes it possible to forecast average daily wind power 6–9 h before the end of the day with an error of 20%. The magnitude of the error of estimation depends on the given season and/or synoptic type group. These results may provide important information for wind turbine owners: daily amount of wind energy can be determined in this way. Thus the owner can decide whether to operate the turbine whole day, or to stop it periodically for maintenance for example.

Sequential Reliability Forecasting for Wind Energy: Temperature Dependence and Probability Distributions

Sequential wind simulation models have been developed to forecast effective load-carrying capability (ELCC), but they produce data that follow a Gaussian distribution, which can be considerably different from real wind-speed distributions, and they do not explicitly model the influence of temperature on the evolution of wind speed. The latter issue is of significant importance considering the strength of correlation between electricity load and temperature and the influence electricity load shapes have on system reliability. This paper presents a new approach to reliability estimation of wind facilities that models wind-speed distributions nonparametrically and includes the effect of temperature on the evolution of wind speed. A relatively long tall tower anemometer dataset is then used to test whether or not the model output produces ELCC distributions that are statistically similar to observed distributions. Results indicate that, relative to temperature independent models, temperature-dependent time-series models are better at both short-term wind-speed forecasting and long-term reliability forecasting. Results also show that reliability forecasts are relatively unaffected by the shape of the wind-speed distribution. Finally, it is apparent that model performance is robust to variation in the average wind power in the years used for model parameterization.

Wind energy assessment and wind farm simulation in Triunfo – Pernambuco, Brazil

This paper presents a wind energy assessment and a wind farm simulation in the city of Triunfo in the state of Pernambuco in the northeast region of Brazil. The wind data were obtained from the SONDA (Sistema de Organização Nacional de Dados Ambientais) project’s meteor station (wind speed, wind direction and temperature) at both heights of 50 m during a period of time of 30 months. The Triunfo wind characterization and wind power potential assessment study shows an average wind speed ( V) of 11.27 m/s (predominant Southeast wind direction), an average wind power density ( P/ A T ) of 1.672 W/m 2 and Weibull parameters shape ( K) and scale ( A) respectively equal to 2.0 and 12.7 m/s. Those values demonstrate an important wind potential in this region for future wind farm prospection. The wind farm (TRI) was simulated by using 850 kW wind turbines given a total of 20 MW installed. The simulated results show(s) an AEP (annual energy produced) of 111.4 GWh, a capacity factor ( C f ) of 62% and a total of 5.462 h of operation by year (full load hours). The economical simulated results show(s) a Pay-back of 3 years Internal Rate of Return (IRR) of 47% and Net Present Value (NPV) of 85.506 k€ (both in a period of time of 20 years).

Dynamic economic load dispatch with wind energy using modified harmony search

The optimisation of fuel cost for short term economic dispatch of thermal generators along with renewable sources like wind generator is considered in this paper. The non-linear constraints considered are power generation limits, reserve limits and ramp rate limits. The optimal dispatch in the presence of the wind generation is obtained using modified harmony search (MHS) algorithm. The wind generator cost model is developed and used in this dynamic economic load dispatch (ELD) problem. The forecasted wind speed is utilised to calculate the average wind power output and this power is included in the economic dispatch model by means of negative load approach. Numerical results for the sample test system having five thermal units, one wind farm and one diesel unit have been presented to demonstrate the performance of the algorithm.

Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain

In this paper, the hourly measured wind speed data for years 2003–2005 at 10 m, 30 m and 60 m height for Kingdom of Bahrain have been statically analyzed to determine the potential of wind power generation. Extrapolation of the 10 m data, using the Power Law, has been used to determine the wind data at heights of 30 m and 60 m. Weibull distribution parameters have been estimated and compared annually and on monthly bases using two methods; the graphical method and the another method, designated in this paper as approximated method, which depends on the standard deviation and average wind speed. The maximum power density for 10 m, 30 m and 60 m heights were found to be 164.33 W/m 2, 624.17 W/m 2 and 1171.18 W/m 2 in February, respectively while the minimum power density were 65.33 W/m 2, 244.33 W/m 2 and 454.53 W/m 2 in October, respectively. The average annual wind power density was found to be 114.54 W/m 2 for 10 m height, 433.29 W/m 2 for 30 m height and 816.70 W/m 2 for 60 m height. Weibull probability function, using Weibull parameters estimated from the approximated method, has shown to provide more accurate prediction of average wind speed and average power density than the graphical method. In addition, the site matching of wind turbine generators at 30 m and 60 m heights has been investigated by estimating the capacity factors of various commercially available wind turbines generators. The monthly and annual variation of capacity factors have been studied to ensure optimum selection of wind turbine generators.