EXPLORING WIND ENERGY POTENTIAL IN KPK-PAKISTAN BY USING MULTI CRITERIA APPROACH

So far in the literature, a number of probability distributions have been successfully implemented for analyzing the wind speed and energy data sets. However, there is no published work on modeling and analyzing the wind speed and energy data sets with probability distributions that are introduced using trigonometric functions. In the existing literature, there is also a lack of studies on implementing the bivariate trigonometric-based probability distributions for modeling the wind speed and energy data sets. In this paper, we take up a meaningful effort to cover these interesting research gaps. Thus, we first incorporate a cosine function and introduce a new univariate probability distributional method, namely, a univariate modified cosine-G (UMC-G) family. Using the UMC-G method, a new probability distribution called a univariate modified cosine-Weibull (UMC-Weibull) distribution is studied. We apply the UMC-Weibull distribution for analyzing the wind energy data set taken from the weather station at Sotavento Galicia, Spain. Furthermore, we also introduce a bivariate version of the UMC-G method using the Farlie–Gumble–Morgenstern copula approach. The proposed bivariate distributional method is called a bivariate modified cosine-G (BMC-G) family. A special member of the BMC-G distributions called a bivariate modified cosine-Weibull (BMC-Weibull) distribution is introduced. We apply the BMC-Weibull distribution for analyzing the bivariate data set representing the wind speed and energy taken from the weather station at Sotavento Galicia. Using different statistical tools, we observe that the UMC-Weibull and BMC-Weibull are the best-suited models for analyzing the wind speed and energy data sets.

Increased utilization of renewable energy (RE) resources is critical in achieving key climate goals by 2050. The intermittent nature of RE, especially solar and wind, however, poses reliability concerns to the utility grid. One way to address this problem is to harmonize the RE resources using spatio-temporal complementarity analysis. Two RE resources are said to be complementary if the lack of one is balanced by the abundance of the other, and vice versa. In this work, solar–wind complementarity was analyzed across the provinces of Kalinga and Apayao, Philippines, which are potential locations for harvesting RE as suggested by the Philippine Department of Energy. Global horizontal irradiance (GHI) and wind speed data sets were obtained from the NASA POWER database and then studied using canonical correlation analysis (CCA), a multivariate statistical technique that finds maximum correlations between time series data. We modified the standard CCA to identify pairs of locations within the region of study with the highest solar–wind complementarity. Results show that the two RE resources exhibit balancing in the resulting locations. By identifying these locations, solar and wind resources in the Philippine islands can be integrated optimally and sustainably, leading to a more stable power and increased utility grid reliability.

A wind resource assessment was conducted for Phases I and II wind farms in Santa Vitoria do Palmar, Brazil. The data used for wind analysis was taken from April 2007 to March 2008 at a meteorological mast height of 101 m. A 10-minute data set of wind speed, standard deviation, wind direction, relative humidity, environmental temperature, and pressure were measured. Analytical estimation models were used to determine the Weibull parameters required for calculating the annual average wind speed, mean power density, cumulative distribution, and probability density functions of the wind regime. The probability density function statistical results were analyzed at two different heights – the reference height of 101 m and the hub height of 80 m. The outcomes were then compared with the WAsP numerical output. The analysis showed that the energy pattern factor method has a confidence level above 97.5% with a correlation coefficient of 100%. Calculation results showed that the annual mean wind speed was 8.10 m/s at 80 m hub height, recommending the site is appropriate for GL Wind 2010 and IEC 61400-1 Ed.2 Class II wind turbine generators. The characteristic wind turbulence at 101 m reference height was IEC Subclass B. Out of the selected wind turbine generators, the HAWT-5/2.50 MW has the highest capacity factor of 44.17% but the lowest levelized energy cost of US$ 44.36/MWh. WAsP was also used to numerically calculate the given locality’s net annual energy production. Based on the proposed wind turbine arrangement, the predicted net annual energy production with wake loss effect for Phases I and II wind farms are 104.92 GWh/year and 456.89 GWh/year, respectively.

Stochastic modelling of wind speeds based on turbulence intensity

ABSTRACTSeveral known statistical distributions can describe wind speed data, the most commonly used being the Weibull family. In this paper, a new law, called ‘M‐Rice’, is proposed for modeling wind speed frequency distributions. Inspired by recent empirical findings that suggest the existence of some cascading process in the mesoscale range, we consider that wind speed can be described by a seasonal AutoRegressive Moving Average (ARMA) model where the noise term is ‘multifractal’, i.e. associated with a random cascade. This leads to the distribution of wind speeds according to the M‐Rice probability distribution function, i.e. a Rice distribution multiplicatively convolved with a normal law. A comparison based on the estimation of the mean wind speed and power density values as well as on the different goodness‐of‐fit tests (the Kolmogorov–Smirnov test, the Kuiper test and the quantile–quantile plot) was made between this new distribution and the Weibull distribution for 35 data sets of wind speed from the Netherlands and Corsica (France) sites. Accordingly, the M‐Rice and Weibull distributions provided comparable performances; however, the quantile–quantile plots suggest that the M‐Rice distribution provides a better fit of extreme wind speed data. Beyond these good results, our approach allows one to interpret the observed values of Weibull parameters. Copyright © 2011 John Wiley & Sons, Ltd.

Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output

The reconstruction models of hourly 10‐m wind speeds were developed for each of 2384 stations over China using stepwise regression, random forest and XGBoost machine learning approaches based on hourly observed and ERA5 reanalysis data from 2005 to 2021. The reconstruction procedures applied observed hourly data to reduce the systematic biases of reanalysis data sets. Furthermore, the procedures employed the past dynamically consistent states of the atmosphere simulated by ERA5 reanalysis techniques to reduce/remove wind‐observed data impacts of long‐term non‐meteorological condition changes over time (e.g., urbanization around weather stations), which provide homogenous hourly wind speed data sets from 1959 to 2021. The systematic errors of the models' simulations derived from three approaches are similarly small, with almost two orders of magnitude smaller than ERA5 original data sets. The systematic errors of reconstructed data sets derived from stepwise regression are similar to its simulation; however, the biases from two machine learning methods are even much greater than ERA5 original data sets. This result implies that machine learning methods are not suitable for such typical time‐series predictions using the previous‐hour wind speed as a predictor to reconstruct wind speed data for the next hour. Therefore, stepwise regression was selected to reconstruct hourly wind speed data sets, which have much better quality than ERA5 reanalysis data with the median correctness increased by >50% and the median rRMSE decreased by 25%–50%. Consequently, the reconstructed wind speed data sets have great potential to be useful for more precisely assessing the characteristics/trends of wind energy resources in the past 60 years over China.

Short-term wind speed forecasting based on a hybrid model

Predicting wind speed and direction is one of the most important and critic tasks in a wind farm, since wind turbine blades motion and thus energy production is closely related to wind behaviour. Machine learning techniques are often used to predict the non-linear wind evolution. In this context, this paper proposes a short term wind data prediction model based on support vector machines in their regression mode, which have the advantage of being simple, fast and well adapted for the short term. This research tries also to prove how wind direction may influence power generation, and why it is important to predict it. A real data set of wind speed and direction historical values is used, from Sidi Daoud wind farm, north-eastern Tunisia, in order to evaluate the proposed model. This forecasting system predicts wind speed and direction for the short term, from one to 10 hours in advance, using a set of past samples.

A 10 year data set of wind speed and precipitation recorded in two Sahelian stations located in Niger and Mali is used to investigate the duration and the diurnal and seasonal cycles of high wind speeds and Dust Uplift Potential (DUP). The results indicate that high wind speeds, those greater than the threshold wind velocity required to initiate wind erosion (TWV) over a bare soil occurred in the middle and late morning during the dry and wet seasons but also at nighttime during the wet season. However, the morning wind speeds are only slightly greater than TWV leading to low DUP. On the opposite, the high wind velocities associated to the nocturnal mesoscale convective systems crossing the Sahel during the wet season are responsible for the highest potential wind erosion events. This leads to a strong seasonality of DUP with more than 70% occurring in less than 90 days, from mid‐April to mid‐July. The duration of the high wind speed events is very short since more than 80% last for less than 3 h, suggesting that the frequency of the observations performed in SYNOP meteorological stations is not sufficient to correctly quantify the contribution of such events to DUP. Finally, by combining precipitation and DUP, we estimated that precipitation should have a relatively limited role in terms of inhibition of wind erosion in this region with precipitation only affecting 25% of total DUP.

Wind speed changes impact society and have important implications for climate change studies. Thus, high‐resolution and high‐quality wind speed datasets are necessary for environmental monitoring and ecosystem research. However, there is no complete set of high spatial and temporal resolution wind speed datasets for China. Additionally, it is extremely challenging to produce wind speed data at high spatial and temporal resolution for large‐scale regions with diverse climate types and complex topographies, such as China. In this study, we used multisource remote sensing images, obtained data on various environmental factors through the Google Earth Engine and Evapotranspiration (ET) Watch Cloud platforms, and combined machine learning algorithms to downscale the ERA5 reanalysis wind speed data, and finally obtained the daily wind speed datasets with 1 km spatial resolution for China in 2015. To verify the accuracy of the model and data products, we selected several metrics to evaluate in conjunction with the actual site observed data. The results show that the multifactor combination model of artificial neural network combining land surface temperature, sunshine durations and roughness factors outperforms a single‐factor combination model, and the results were in good agreement with the original data (R2 of 0.95 and RMSE of 0.40 m·s−1). The final wind speed data products were also in good agreement with the observed meteorological data (R2 range of 0.86–0.95 and RMSE range of 0.33–0.44 m·s−1); moreover, the accuracy and precision were greatly improved over the original data. This study provided a dataset that has potential applications in future climate change and ecosystem studies.

Impact of Probabilistic Modelling of Wind Speed on Power System Voltage Profile and Voltage Stability Analysis

The Tibetan Plateau (TP) has an average elevation of over 4000 m and with its surrounding mountains is regarded as Earth’s “third pole.” As a result of its size and height, climate change in the TP has its own unique characteristics that include a proposed positive correlation between the surface temperature and pressure. This study examines the trends and relationships between the surface pressure and temperature in the TP through the examination of monthly mean data from 71 stations during 1961–2013. On annual, seasonal, and monthly time scales, the TP exhibits a statistically significant warming trend that attains a rate of 0.30°C decade−1 for annual means over the period 1961–2013. The most pronounced warming occurs in winter, in agreement with previous studies, with evidence of acceleration in the rate after the mid-1980s and the global warming slowdown period. For the entire period of 1961–2013, the surface pressure in the TP has a positive trend of 0.08 hPa decade−1 on an annual basis, again with the largest trends occurring in winter. However, unlike what occurred with the surface temperature, the trend in surface pressure, in most cases, reversed sign after the mid-1980s. The trend in the geopotential height at 500 hPa from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis is consistent with the observed surface pressure trends. Over the period 1961–2013, there is a seasonal shift in the nature of the relationship between the surface temperature and pressure with a negative correlation during summer and autumn, and a positive correlation during winter. This suggests that the nature of the relationship between these two climate elements reflects the changing nature of the seasonal snow cover (land surface property) and cloud in the region.

In the presence of renewable energy resources in the power system, most grid management applications make use of hourly or even minutely models for solar irradiance and wind speed. However, studies on the grid impact and controllability of these resources sometimes require focusing on shorter duration timeframes, namely secondly to minutely models. Detailed studies on power and voltage quality impacts, dynamic stability analysis, and transient capabilities of controllers are among those that can benefit from such short-term models. This paper presents a new method of creating random solar and wind samples, which utilizes a short-term (secondly to minutely) component and a longer-term component in order to capture both the overall statistical description of the energy resource, as well as the time-correlated values observed in wind speed and solar irradiance data sets. Models developed in this paper can generate wind and solar energy trajectories at very high resolutions to be used in suitable system-level studies.

The focus of this paper is to estimate parameters of the best distribution for modelling wind speed data, real-life data sets of wind speed of Maiduguri, the biggest city in the North Eastern, Nigeria were adopted for application purposes. Six (6) probability density functions, specifically, Weibull, Gamma, Lognormal, Pareto, Burr and Log-Logistic are considered for modelling the wind speed data. In selecting the model of best fit for the variability of the wind speed data, five (5) methods of estimating parameter, such as; Maximum Likelihood Estimation (MLE), Matching Quantiles Estimation (MQE), The Cramer-von Mises Minimum Distance Estimators (CvM), Anderson-Darling Minimum Distance Estimation and Kolmogorov-Smirnov Minimum Distance Estimation (K-S)) were further applied to obtain the best estimates for the best model among compared ones. We discovered in our investigation that Weibull distribution best fitted the wind data per Goodness-of-fit tests, since it has the smallest p-value for K-S (0.03179314), CvM (0.03137888) and AD (0.23725978) revealing the curve is fairly close to our data and the maximum likelihood estimators with the smallest AIC (972.7990) and BIC (980.3105) estimates for Weibull parameters, proved to be the best as compared with other methods of estimation.