• Energy Research

This paper analyzes five years hourly wind data from twenty-nine weather stations to identify the potential location for wind energy applications in Oman. Different criteria including theoretical wind power output, vertical profile, turbulence and peak demand fitness were considered to identify the potential locations. Air density and roughness length, which play an important role in the calculation of the wind power density potential, were derived for each station site. Due to the seasonal power demand, a seasonal approach was also introduced to identify the wind potential on different seasons. Finally, a scoring approach was introduced in order to classify the potential sites based on the different factors mentioned above. It is concluded that Qayroon Hyriti, Thumrait, Masirah and Rah Alhad have high wind power potential and that Qayroon Hyriti is the most suitable site for wind power generation.

This paper proposes an approach for accurate wind speed forecasting. While previous works have proposed approaches that have either underperformed in accuracy or were too computationally intensive, the work described in this paper was implemented using a computationally efficient model. This model provides wind speed nowcasting using a combination of perturbed observation ensemble networks and artificial neural networks. The model was validated and evaluated via simulation using data that were measured from wind masts. The simulation results show that the proposed model improved the normalized root mean square error by 20.9% compared to other contending approaches. In terms of prediction interval coverage probability, our proposed model shows a 17.8% improvement, all while using a smaller number of neural networks. Furthermore, the proposed model has an execution time that is one order of magnitude faster than other contenders.

To plan operations and avoid any grid disturbances, power utilities require accurate power generation estimates for renewable generation. The generation estimates for wind power stations require an accurate prediction of wind speed and direction. This paper proposes a new prediction model for nowcasting the wind speed and direction, which can be used to predict the output of a wind power plant. The proposed model uses perturbed observations to train the ensemble networks. The trained model is then used to predict the wind speed and direction. The paper performs a comparative assessment of three artificial neural network models. It also studies the performance of introducing perturbed observations to the model using six different interpolation techniques. For each technique, the computational efficiency is measured and assessed. Furthermore, the paper presents an exhaustive investigation of the performance of neural network types and several techniques in training, data splitting, and interpolation. To check the efficacy of the proposed model, the power output from a real wind farm is predicted and compared with the actual recorded measurements. The results of the comprehensive analysis show that the proposed model outperforms contending models in terms of accuracy and execution time. Therefore, this model can be used by operators to reliably generate a dispatch plan.

Accurate wind forecast at a wind farm is an essential process in wind energy industry and marketing. Numerical Weather Prediction (NWP) models can be used but they provide wind forecast as a single value for a given time horizon. Therefore, forecasting wind speed as a deterministic value doesn't represent the uncertainty of the wind speed forecast. Ensemble NWP forecast can be used to calculate the probability of occurrence of different wind speeds classes. The main disadvantage of this approach is the extensive computational resources required to run multiple copies of the NWP model. This paper, explores the possibility of using pseudo ensemble method for generating probabilistic wind forecast for wind farm applications. The proposed method utilizes the spatial and temporal neighborhoods of the forecast point to generate forecast dataset and then calculate the required probabilities. A case study using the proposed method is tested and validated using wind data from NWP model and measurements from three ground weather stations in Oman.

This paper investigates the wind energy potential over 42 sites in Iran. Ten years of wind data (1996–2005) were used from weather stations located at these sites. Different criteria were considered, including vertical wind profile, wind power density (WPD), wind frequency distribution, wind sustainability, seasonal variation, turbulence intensity and peak demand matching. Air density and roughness length, which play important role in the calculation of the WPD potential, were derived for each station site. Simple scoring was used to rank the mostly windy sites. It is concluded that Sistan and Bluchistan governorate (Zabol) has the highest potential. Zahedan, Jazireh Kish and Ardebil also have high wind power potential.

Abstract Duqm is an emerging port city for both industrial and truism applications. Energizing such city requires carful utilization of all available resources. Due to its geographical location, Duqm can harness the wind energy through developing grid connected wind farm. Therefore, a proper wind energy assessment using high temporal and spatial data is required before any financial investment. This paper investigates the assessment of a large-scale wind energy potential in the emerging city of Duqm (Oman). The initial wind assessment is conducted by the mean of dynamical downscaling Ensemble Numerical Weather Prediction Models. Multi-criteria decision support system was used to analysis the land suitability for wind energy applications. 25 MW wind farm is also simulated using WAsP to estimate the annual energy production and the wake losses.

Abstract Few studies have been conducted to evaluate the wind energy potential in Oman. All of the published studies were based on ground measurements from weather stations, which provide wind data at 10 m above the ground. The spatial and temporal coverage of the actual meteorological network in Oman presents some inadequacy with the need of the wind power industry in term of data sets. Currently, these assessments are done on a site-by-site basis, using existing station data and limited on-site monitoring data. A primary task under this research is to supplement existing wind-power potential assessments and develop an enhanced (spatially explicit) inventory of the wind resource across Oman. With the current available computational power, it is possible to use of high resolution numerical weather prediction (NWP) models to provide wind data usable for wind energy resource assessment. This paper evaluates the performance of NWP model data for wind energy application for Oman with a comparison to satellite data. Two models namely HRM and COSMO were used in this study. Suitable model resolution was first selected by evaluating the ability of the models to simulate weak local wind circulation (i.e. sea breeze), then one year of data was generated and compared to satellite data and ground measurements. The NWP data showed better accuracy over satellite data with comparison to the ground measurements. Wind power output was also calculated for selected sites with high potential.

Wind energy resource assessment applications require accurate wind measurements. Most of the published studies used data from existing weather station network operated by meteorological departments. Due to relatively high cost of weather stations the resolution of the weather station network is coarse for wind energy applications. Typically, meteorological departments install weather stations at specific locations such as airports, ports and areas with high density population. Typically, these locations are avoided during wind farms siting. According to WMO regulations, weather stations provide measurements for different weather elements at specific altitudes such as 2 m for air temperature and 10 m for wind measurements. For wind energy resource assessment applications, minimum of one year of wind measurements is required to build wind climatology for a certain site. Therefore data collected from a certain site cannot be used before one year of operation. Due to these limitations, wind energy resource assessment application needs to use data from different sources. Recently, wind assessment studies were conducted using data generated by Numerical Weather Prediction models. This paper reviews the use of the Numerical Weather Prediction data for wind energy resource assessment. It gives a general overview of NWP models and how they overcome the limitations in the classical wind measurements.