Errors and uncertainties associated with missing wind data and short records

A near‐complete 4 year data set of 10 min average 80 m wind speeds is used to examine the impact of missing data on monthly and yearly estimates of mean wind speed and energy production from a generic wind turbine. Missing data is a source of uncertainty in wind energy resource assessment studies. Quantifying that uncertainty can improve the reliability of P90 and related wind farm energy production estimates. An empirical relationship between missing data percentage and relative uncertainty in monthly mean wind speed is derived. Relationships between uncertainties in monthly average wind speed and uncertainties in monthly energy production are also explored. In many cases with monthly data losses of 10% or less the contribution to the overall uncertainty in annual energy production will be small (<1%), but with substantial losses in cold winters, typically caused by icing; the uncertainties can become more significant. The data set is also used to indicate uncertainties associated with short data periods. Annual average wind speed estimates based on less than a complete year's data also add significant uncertainty to wind resource assessments. Copyright © 2013 John Wiley & Sons, Ltd.     

Assessment of wind characteristics and wind turbine characteristics in Taiwan

Wind characteristics and wind turbine characteristics in Taiwan have been thoughtfully analyzed based on a long-term measured data source (1961–1999) of hourly mean wind speed at 25 meteorological stations across Taiwan. A two-stage procedure for estimating wind resource is proposed. The yearly wind speed distribution and wind power density for the entire Taiwan is firstly evaluated to provide annually spatial mean information of wind energy potential. A mathematical formulation using a two-parameter Weibull wind speed distribution is further established to estimate the wind energy generated by an ideal turbine and the monthly actual wind energy generated by a wind turbine operated at cubic relation of power between cut-in and rated wind speed and constant power between rated and cut-out wind speed. Three types of wind turbine characteristics (the availability factor, the capacity factor and the wind turbine efficiency) are emphasized. The monthly wind characteristics and monthly wind turbine characteristics for four meteorological stations with high winds are investigated and compared with each other as well. The results show the general availability of wind energy potential across Taiwan.     

风切变指数在风电场风资源评估中的应用

以内蒙古地区3座70m高测风塔连续2年的实测数据来分析风切变指数的变化,结果表明:1)不同高度梯度的风切变指数受地面粗糙度及周围地形地貌的影响较大。2)计算相邻高度的风速时,采用相邻高度间的风切变指数计算得到的结果较好;计算相差较大的高度间风速时,采用拟合曲线得到的风切变指数计算得到的结果较好。3)利用3～25m/s的风切变指数计算各月风速及年均风速结果都与实测值最接近;而利用全部风速数据的风切变指数计算统计各月风速往往比实测值偏大;利用3～25m/s拟合曲线得到的风切变指数统计各月风速比实测值偏小。     

Wind shear coefficients and their effect on energy production

This paper provides realistic values of wind shear coefficients calculated using measured values of wind speed at 20, 30 and 40 m above the ground for the first time in Saudi Arabia in particular and, to the best of the authors’ knowledge, in the Gulf region in general. The paper also presents air density values calculated using the measured air temperature and surface pressure and the effects of wind shear factor on energy production from wind machines of different sizes. The measured data used in the study covered a period of almost three years between June 17, 1995 and December 1998. An overall mean value of wind shear coefficient of 0.194 can be used with confidence to calculate the wind speed at different heights if measured values are known at one height. The study showed that the wind shear coefficient is significantly influenced by seasonal and diurnal changes. Hence, for precise estimations of wind speed at a height, both monthly or seasonal and hourly or night time and day time average values of wind shear coefficient must be used. It is suggested that the wind shear coefficients must be calculated either (i) using long term average values of wind speed at different heights or (ii) using those half hourly mean values of wind speed for which the wind shear coefficient lies in the range ⩾0 and ⩽0.51. The air density, calculated using measured temperature and pressure was found to be 1.18 kg/m³. The air density values were also found to vary with the season of the year and hour of the day, and hence, care must be taken when precise calculations are to be made. The air density values, as shown in this paper, have no significant variation with height. The energy production analysis showed that the actual wind shear coefficient presented in this paper produced 6% more energy compared to that obtained using the 1/7 power law. Similarly, higher plant capacity factors were obtained with the wind shear factor of 0.194 compared to that with 0.143.     

Accounting for the speed shear in wind turbine power performance measurement

The current IEC standard for wind turbine power performance measurement only requires measurement of the wind speed at hub height assuming this wind speed to be representative for the whole rotor swept area. However, the power output of a wind turbine depends on the kinetic energy flux, which itself depends on the wind speed profile, especially for large turbines. Therefore, it is important to characterize the wind profile in front of the turbine, and this should be preferably achieved by measuring the wind speed over the vertical range between lower and higher rotor tips. In this paper, we describe an experiment in which wind speed profiles were measured in front of a multimegawatt turbine using a ground–based pulsed lidar. Ignoring the vertical shear was shown to overestimate the kinetic energy flux of these profiles, in particular for those deviating significantly from a power law profile. As a consequence, the power curve obtained for these deviant profiles was different from that obtained for the ‘near power law’ profiles. An equivalent wind speed based on the kinetic energy derived from the measured wind speed profile was then used to plot the performance curves. The curves obtained for the two kinds of profiles were very similar, corresponding to a significant reduction of the scatter for an undivided data set. This new method for power curve measurement results in a power curve less sensitive to shear. It is therefore expected to eventually reduce the power curve measurement uncertainty and improve the annual energy production estimation. Copyright © 2011 John Wiley & Sons, Ltd.     

Sensitivity of southern California wind energy to turbine characteristics

Using output from a high‐resolution meteorological simulation, we evaluate the sensitivity of southern California wind energy generation to variations in key characteristics of current wind turbines. These characteristics include hub height, rotor diameter and rated power, and depend on turbine make and model. They shape the turbine's power curve and thus have large implications for the energy generation capacity of wind farms. For each characteristic, we find complex and substantial geographical variations in the sensitivity of energy generation. However, the sensitivity associated with each characteristic can be predicted by a single corresponding climate statistic, greatly simplifying understanding of the relationship between climate and turbine optimization for energy production. In the case of the sensitivity to rotor diameter, the change in energy output per unit change in rotor diameter at any location is directly proportional to the weighted average wind speed between the cut‐in speed and the rated speed. The sensitivity to rated power variations is likewise captured by the percent of the wind speed distribution between the turbines rated and cut‐out speeds. Finally, the sensitivity to hub height is proportional to lower atmospheric wind shear. Using a wind turbine component cost model, we also evaluate energy output increase per dollar investment in each turbine characteristic. We find that rotor diameter increases typically provide a much larger wind energy boost per dollar invested, although there are some zones where investment in the other two characteristics is competitive. Our study underscores the need for joint analysis of regional climate, turbine engineering and economic modeling to optimize wind energy production. Copyright © 2012 John Wiley & Sons, Ltd.     

Can synoptic drivers explain rotor-area wind conditions and predict energy output?

Synoptic-scale weather patterns are an important driver of wind speed at turbine hub height, but wind energy generation is also affected by the wind profile across the rotor. In this research, we use a 6-year record of hourly profile measurements at the Eolos Wind Research Station in Minnesota, USA, to investigate whether synoptic weather patterns can provide information about rotor-area characteristics in addition to hub-height wind speed. We use sea level pressure data from the MERRA-2 reanalysis to classify synoptic patterns at the Eolos site into 15 synoptic types and use the Eolos wind profile data to create mean hourly and mean monthly values of wind speed and turbulence intensity at hub height (80 m), and wind speed shear, wind direction shear, and the potential temperature gradient across the rotor (30–129 m), for each synoptic type. Using a simple linear regression model, we find that, at monthly time scales, wind speed, turbulence intensity, and wind speed shear across the rotor are the most important variables for predicting monthly wind energy output from the Eolos turbine. Regression models using the original Eolos data and the derived synoptic types capture about 64% and 55% of the variance in monthly energy output, respectively. When fewer than the full 6 years of observations are used to fit the regression model, however, predictions using the synoptic types slightly outperform predictions using the Eolos observations. These results suggest that seasonal energy projections may be enhanced by incorporating wind profile measurements with synoptic-scale drivers.     

陕西省金润河北镇风电场风能资源开发评估

对陕西省宝鸡市陇县金润河北镇风电场气象条件、风功率密度、平均风速、主导风向等风能参数进行分析评价。结果表明,测风塔100 m高度月平均风速、月平均风功率密度最大均出现在4月,最小均出现在8月;测风塔100 m高度主导风向为SSW(南西南),主要风能方向为SSW(南西南),风电场风功率密度等级为1级。风电场安装20台2500 kW的风电机组,装机容量50 MW,年设计发电量1.33485×108 kW·h,年出厂电量9.5426×107 kW·h。结果可为其他风电场选址和发电量估算提供参考。     

Evaluation of monthly capacity factor of WECS using chronological and probabilistic wind speed data: A case study of Taiwan

Monthly energy outputs and capacity factors of an existing median-scale wind power station located in Mailiao, Taiwan, have been analyzed by using the measured half-hourly wind speed data over a period spanned between 2002 and 2005. The wind speed data in the chronological (time-series) format and probabilistic (Weibull) format are used to estimate the monthly capacity factors of Vestas V47–660 KW turbines in Mailiao. The monthly capacity factors calculated from the chronological and probabilistic approaches are compared with and validated by the measured capacity factors obtained in Mailiao. In addition, due to the climatic features of the Asia monsoon, there are two prominent wind periods within a year in Taiwan, i.e., the strong (October–March) and weak (April–September) wind periods. This study also investigates the differences of the measured and calculated capacity factors in the strong and weak wind periods of Mailiao. It is shown that the capacity factors calculated from the time-series approach have better agreement with the actual capacity factors than the Weibull approach. Both approaches have slightly underestimated the capacity factors in the strong wind period, but have significantly overestimated the capacity factors in the weak wind period.     

The influence of the Weibull assumption in monthly wind energy estimation

An estimation of the monthly wind energy output for the period 1999–2003 at five wind farms in northeastern Spain was evaluated. The methodology involved the calculation of wind speed histograms and the observed average wind power versus wind relation obtained from hourly data. The energy estimation was based on the cumulated contribution of the wind power from each wind speed interval. The impact of the Weibull distribution assumption as a substitute of the actual histogram in the wind energy estimation was evaluated. Results reveal that the use of a Weibull probability distribution has a moderate impact in the energy calculation as the largest estimation errors are, on average, no larger than 10% of the total monthly energy produced. However, the evaluation of the goodness of fit through the χ² statistics shows that the Weibull assumption is not strictly substantiated for most of the sites. This apparent discrepancy is based on the partial cancellation of the positive and negative departures of the Weibull fitted and the actual wind frequency distributions. Further investigation of the relation between the χ² and the error contribution exposes a tendency of the Weibull distribution to underestimate (overestimate) the observed histograms in the lower and upper (intermediate) wind speed intervals. This fact, together with the larger wind power weight over the highest winds, results in a systematic total wind energy underestimation. Copyright © 2008 John Wiley & Sons, Ltd.     

Offshore wind resource assessment with WAsP and MM5: comparative study for the German Bight

In this study, two different approaches to estimate the wind resource over the German Bight in the North Sea are compared: the mesoscale meteorological model MM5 and the wind resource assessment program WAsP. The dynamics of the atmosphere of the year 2004 was simulated with the MM5 model, with input from the NCEP global model, without directly utilizing measurement data. WAsP estimations were calculated on the basis of six measurement stations: three on islands, two offshore and one onshore. The annual mean wind speed at onshore, offshore and island sites is estimated by both models. The predictions are compared both with each other and with measured data. A spatial comparison of the wind resource calculated by the two models is made by means of a geographical information system. The results show that the accuracy of the WAsP predictions depends mainly on the measurement station used as input. Small differences are shown in the estimations performed by the three island stations, despite the large geographical distance between them. Compared with the measurements of the offshore sites, they seem to be suitable for estimating the offshore wind resource from measurements on land. The two offshore stations show differences when predicting each other's mean wind speed with the WAsP method, while the MM5 calculations show a similar deviation for both sites. The largest differences between the two models are found at distances of 5–50km from the coast. While in WAsP the increase occurs in the first 10km from the coast, MM5 models an increase due to coastal effects for at least 50km. Copyright © 2006 John Wiley &Sons, Ltd.     

Characteristics of wind variations on Jeju Island,Korea

In order to clarify the long‐term variability of the wind in complex terrain, an investigation was conducted on Jeju Island, Korea. The four coastal areas and the three mountainous areas were selected and wind data for 8–11 years from meteorological observatories were collected for this work. Inter‐annual variations, monthly variations and diurnal variations in wind characteristics were calculated from the long‐term wind data. As a result, it was found that wind speed is higher in the winter season while it is lower in the summer season. Wind at all sites blew strongly in the daytime and weakly at night. Also, wind energy and the range of variation in wind energy varied significantly from region to region on Jeju Island. Inter‐annual variations in wind energy on Jeju Island occurred a little greater than seen in the results of earlier works conducted in other place. Copyright © 2009 John Wiley & Sons, Ltd.     

Spatial estimation of wind speed

This study utilizes Abductory Induction Mechanism to estimate the mean monthly wind speed at some locations in Saudi Arabia based on wind data at other available recording stations in addition to some historical wind speed data at the target site. Wind speed data from 20 meteorological stations over a period of 16 years between 1990 and 2005 was used to accomplish the set objective. To validate the model, data from 19 stations was used to estimate the wind speed at the 20th location. Evaluation was performed for every one of the 20 available locations. Results show good agreement between estimated and measured monthly mean wind speed values. Copyright © 2010 John Wiley & Sons, Ltd.     

Designing an index for assessing wind energy potential

To meet the increasing global demand for renewable energy, such as wind energy, an increasing number of wind parks are being constructed worldwide. Finding a suitable location requires a detailed and often costly analysis of local wind conditions. Plain average wind speed maps cannot provide a precise forecast of wind power because of the non-linear relationship between wind speed and production. We suggest a new approach to assess the local wind energy potential. First, meteorological reanalysis data are applied to obtain long-term low-scale wind speed data at specific turbine locations and hub heights. Second, the relation between wind data and energy production is determined via a five parameter logistic function using actual high-frequency energy production data. The resulting wind energy index allows for a turbine-specific estimation of the expected wind power at an unobserved location. A map of the wind power potential for Germany exemplifies our approach.     

Wind turbine power curves incorporating turbulence intensity

The performance of a wind turbine in terms of power production (the power curve) is important to the wind energy industry. The current International Electrotechnical Commission 61400‐12‐1 standard for power curve evaluation recognizes only the mean wind speed at hub height and the air density as relevant to the power production. However, numerous studies have shown that the power production depends on several variables, in particular turbulence intensity. This paper presents a model and a method that are computationally tractable and able to account for some of the influence of turbulence intensity on the power production. The model and method are parsimonious in the sense that only a single function (the zero‐turbulence power curve) and a single auxiliary parameter (the equivalent turbulence factor) are needed to predict the mean power at any desired turbulence intensity. The method requires only 10 min statistics but can be applied to data of a higher temporal resolution as well. Copyright © 2012 John Wiley & Sons, Ltd.     

Impact of wind on secondary regulating power and energy in the Spanish system

The objective of this paper is to assess if high penetrations of wind power influence regulating secondary reserve requirements in the Spanish power system including three different topics: (i) needed ramps of variation of regulating power, (ii) amount of total regulating power and (iii) regulating energy. The results (both technical and economical) derived in this paper are based on the net load curve, defined as the difference between the load curve of the system and the wind generation curve. Since wind power does not provide yet secondary regulating reserve, net load represents the load that must be balanced by units providing the AGC service. Thus, the comparison of the three topics (ramps, regulating power and regulating energy) required by the net load with the ones required by the load curve represents the influence of wind on AGC requirements. Historical values of total demand and wind production with a 1 min resolution of the Spanish power system, collected for 2010 (when the wind share was close to 20%), are employed to derive significant conclusions. The analysis of this paper concludes that the main impact of wind in the Spanish system lies on the secondary regulating energy, while the required ramp rate and secondary reserve nearly not affected. Copyright © 2016 John Wiley & Sons, Ltd.     

Worldwide wind energy status and the characteristics of wind energy in Iran,case study: the province of Sistan and Baluchestan

In this paper, the recent trend of the worldwide wind energy utilisation is reviewed and the recent activities in using renewable energy sources in Iran are explained. As a case study, the wind characteristics of the province of Sistan and Baluchestan are statistically analysed. The wind characteristics such as the monthly mean wind speed and the wind power density of each station are presented. The monthly variation of the wind direction is presented and also the dominant wind direction is shown in a wind rose diagram. The values of turbulence intensity at different heights are calculated. The results show that the stations of Khash and Nosratabad are more suitable for limited off-grid utility applications. Lootak with the average annual wind power density of 388?W?m^?2 at the height of 40?m and constant wind direction is recommended for large-scale grid-connected wind turbines.     

The potential of electricity generation on the east coast of Red Sea in Egypt

Wind characteristics have been analyzed based on long-term measured data of monthly mean wind speed of seven meteorological stations along the east coast of Red Sea in Egypt. It was found that the windiest stations (Region A) namely (Zafarana, Abu Darag, Hurghada and Ras Benas) have annual mean wind speeds (7.3, 7.2, 6.4 and 5.5 m/s) at 10 m height, respectively.Numerical estimations using measured wind speeds and frequencies to calculate the two Weibull parameters were carried out and two methods were applied.The methodical analysis for the corrected monthly wind power density at a height of 10 m above ground level, over roughness class 0 (water), for each station was done. The recommended correlation equation was also stated for Red Sea zone in Egypt. Also the corrected annual wind power density at the heights (50–70) m was obtained for all stations. Moreover, calculations show that the four stations in (Region A) have a huge energy potential available (430–1000 W/m²) at 70 m height, while Quseir and Suez stations (Region B) have good wind power density (170–190 W/m²) at 50 m height.A technical and economic assessment has been made of electricity generation from two turbines machines having capacity of (1000 and 600 kW) considered in Regions A & B, respectively, using WASP program. The yearly energy output, capacity factor and the electrical energy cost of kWh produced by the two different turbines in each region were estimated. The production costs of four stations in Region A was found to be less than 2€ cent/kWh and compared with retail tariff.     

大型风电机组功率曲线的分析与修正

讨论了风电机组不同情况下的功率曲线定义,分析了功率曲线绘制过程中的风速处理方法,可以适用于绘制风力发电机组静、动态功率曲线;讨论了影响机组功率曲线的各种因素,并给出了影响因子,使得根据功率曲线进行风场发电量的计算可以取得更可靠的结果。     

Estimation of variability and predictability of large‐scale wind energy in The Netherlands

This paper presents a data‐driven approach for estimating the degree of variability and predictability associated with large‐scale wind energy production for a planned integration in a given geographical area, with an application to The Netherlands. A new method is presented for generating realistic time series of aggregated wind power realizations and forecasts. To this end, simultaneous wind speed time series—both actual and predicted—at planned wind farm locations are needed, but not always available. A 1‐year data set of 10‐min averaged wind speeds measured at several weather stations is used. The measurements are first transformed from sensor height to hub height, then spatially interpolated using multivariate normal theory, and finally averaged over the market resolution time interval. Day‐ahead wind speed forecast time series are created from the atmospheric model HiRLAM (High Resolution Limited Area Model). Actual and forecasted wind speeds are passed through multi‐turbine power curves and summed up to create time series of actual and forecasted wind power. Two insights are derived from the developed data set: the degree of long‐term variability and the degree of predictability when Dutch wind energy production is aggregated at the national or at the market participant level. For a 7.8 GW installed wind power scenario, at the system level, the imbalance energy requirements due to wind variations across 15‐min intervals are ±14% of the total installed capacity, while the imbalance due to forecast errors vary between 53% for down‐ and 56% for up‐regulation. When aggregating at the market participant level, the balancing energy requirements are 2–3% higher. Copyright © 2008 John Wiley & Sons, Ltd.