Papers by Keyword: Capacity Factor

Abstract: In Ethiopia, and particularly in the Amhara region, the government as well as the concerned organization would not give special attention to establishing wind energy plants. Lack of scientific research inputs about potential assessment can be one of the reasons behind it. In this paper, a wind energy potential assessment for Debel, Malawa, Enwari, and Ayba Eyesus sites in the Amhara region has been investigated. Five statistical distribution methods namely Weibull 3P, Weibull 2P, Rayleigh 2P, Normal, and Lognormal are used to fit the data to the probability density function and cumulative distribution function. The proposed parameter estimation method, to precisely predict the values of the shape parameter, scale parameter, and location parameter, was the Maximum Likelihood Estimation Method (MLE). To analyze the goodness of fit of the models, Kolmogorov, Andersen Darling, and Chi-Square have been used. The test indicated that Weibull 3P is the best fitting method, except for Ayba Eyesus, which is suited to Weibull 2P. For Debel, Malawa, Enwari, and Ayba Eyesus, the maximum annual average wind power density was found to be 74.291 W/m², 19.183 W/m², 68.972 W/m², and 49.221 W/m² correspondingly. The evaluations show that VENSYS 87 turbine model has better performance in all three sites except Enwari, where Inox Wind DF 100 is favored. With their best performance turbine, the capacity factor of the sites is determined as 14%, 7%, 12%, and 14% for Debel, Malawa, Enwari, and Ayba Eyesus respectively. Furthermore, Economical analysis by initial cost, lifetime, operation, and maintenance cost, has been carried out to estimate the cost of energy. With VENSYS 87 turbine model, the three sites' present value costs are $5,479,586, while it costs $7,306,115 in Enwari with Inox Wind DF 100 turbine. The cost of electricity per kWh is estimated to be $0.00231, $0.00455, $0.00391, and $0.00312 for Debel, Malawa, Enwari, and Ayba Eyesus respectively, and it is significantly lower than the cost from Ethiopian electric utility (EEU), which is around 0.009$/kwh. Access to electricity in Ethiopia was reported at 45% in 2019. This indicates there is a shortage of energy in the country. This kind of study can help authorities and policymakers in taking into account wind power to mitigate energy poverty in the country.

Abstract: This paper presents a study of the monthly variability of wind energy potential at several heights and an investigation of the best fitting commercial wind turbine in the Cotonou coast (Benin Republic). The monthly Weibull parameters are calculated at 10 m and extrapolated at 30 and 50 m heights. The monthly Weibull wind power density and the wind speed carrying maximum energy are calculated at 10, 30 and 50 m. We showed that wind resource in the Cotonou coast is favorable for wind energy production at 30 and 50 m heights. The capacity factor of selected commercial wind turbines is calculated to investigate the best fitting wind turbine in the Cotonou coast. It turns out that Polaris 19-50 is the best fitting wind turbine in the selected turbines with a mean capacity factor of 0.49.

Abstract: This paper presents the technical and economic assessment of a 10 MW wind farm at Pakphanang district in Nakhon Si Thammarat province, southern Thailand. The microscale wind resource map within 10 km is developed based on 3 years recorded wind data at 120 m above ground level (agl) (2012-2014) along with computational fluid dynamic (CFD) wind flow modeling with resolution of 90 m. The 5 x 2.0 MW and 4 x 2.5 MW modeled wind farms are positioned along the shoreline with a position criteria of 5 times the rotor diameter between the turbines. The net annual energy production (AEP) and capacity factor (CF) with wake losses are analyzed. The economic analysis is done based on the current project cost and financial incentive (Adder). Results show that the annual mean speed at 120 m agl is 5.2 m/s. The net AEP and CF are 36.60 GWh/year and 41.78%, respectively with wake loss of 0.40%. Under project cost of 75 million THB/MW and 70% debt ratio and Thailand Board of Investment (BOI) tax exemption promotion, the benefit cost ratio is 1.04, the net present value is 65.96 million THB, the financial internal rate of return is 17.70% and the payback period is 4 years. Finally, a 10 MW wind power project could avoid greenhouse gas emission of 19,764 tons CO₂eq per year.Keywords: Wind Energy, Wind Farm, Capacity Factor, Wind Flow Modeling, Project Analysis

Abstract: The construction of large capacity Kemskaya wind power plant (WPP) at the coast of the White Sea was considered to decrease the deficiency of energy in the region. The results of wind energy resources research of the region are presented. The results of calculation of Kemskaya wind power plant parameters including net electricity production, hours of plant’s installed capacity utilization, and capacity factor are given. Three variants of Kemskaya WPP construction are considered: with capacity 30, 180 and 1000 MW.

Abstract: The operational performances of coastal wind farms, located at the east coast of Jiangsu Province of China, are reported in this paper. The result shows that although a rich wind resources predicted in this area, a strong variation of wind resources characteristics are observed between wind farms. Annual averaged wind speeds of five wind farms at hub height range from 4.8 to 6.6 m/s. In addition, the wind shear coefficient shows largely different although they appear to agree with coastal region characteristics. Ru Dong not only has the highest wind shear coefficient but also a largest range of wind shear coefficient variation amplitude. The Capacity Factors of the selected wind farms range from 16% to 27%. Ru Dong shows a lowest Capacity Factor of 16% which may due to the low average wind speed and high wind shear coefficient. On the contrary, Da Feng has a lower Capacity Factor with sensible wind resources indicating either an improved wind turbine technology or an optimized wind farm operational maintenance is needed to increase wind farm efficiency. The result obtained in this paper provides valuable information for future offshore wind farm development in China.

Abstract: This research investigates causes of the low performance of the first commercial wind farm in Thailand. The measured data suggests that this wind farm is uncompetitive. We found that this is due to poor turbine-site matching. In contrary to a traditionally held belief, the hub-height and turbine capacity are not the contributing factors. Key performance indicators are obtained for use as benchmarks in future wind farm appraisal. Then a turbine selection method is proposed to increase the capacity factor (CF) of the wind farm. CF is used as the main performance indicator, which can be compared to other wind farms. The real capacity factor (CF_R) determined using measured data is 14.90%. This CF_R is considerably lower than the estimated capacity factor (CF_E) of 21.53%. The low CF_R is due to grid instability. In addition, the CF_R is lower than the CF_E by a factor of 0.69. This information is valuable to investors and wind farm developers in a wind farm feasibility study. A graphical wind turbine-site matching is proposed. Wind turbine-site matching is achieved by using normalised power output plots and power density plots on a probability density graph of the wind site. This process consumes a short period of time. An improved turbine-site matching is achieved.

Abstract: This paper is concerned with the enhancement of the Capacity Factor for wind turbines, especially offshore. In particular the work describes analysis of data on a wind turbine drive train to enhance the reliability of components, reduce maintenance time and provide early warning of failures from the understanding of mechanical dynamics.

Abstract: Wind energy has been most prevalently utilized to generate electric power due to non pollution to the environment and the conservation of fossil fuel resources. The energy generated from wind turbine depends on the wind site characteristics and the wind turbine parameters. So, the choice of certain wind turbine for specific site is very important in terms of price of electric energy generated from wind energy system. Therefore, optimal choice of wind turbine is one of the most crucial issues in the design of wind energy system, which can utilize wind energy as efficiently as possible and achieve the best economic benefits. So this paper introduces a new and simple mathematic formulation for the wind turbine-site matching problem, based on wind speed characteristics of any site and the power curve parameters of any wind turbine. Wind speed at any site is characterized by the scale parameter (c) and the shape parameter (k) of the Weibull distribution function. The power curve parameters of any wind turbine are characterized by the cut-in, rated, and furling speeds and the rated power. The new formulation method is derived based on a generic formulation for the product of the Capacity Factor (CF) and Normalized Power (P_N). Three case studies are also presented to demonstrate the effectiveness of the proposed method to choose between a group of wind sites and a list of commercial wind turbines.