An Analysis of Current and Future Wind Energy Gain Potential for Central Iowa

Sharifi, Farrokh; Hashemi, Nastaran

doi:10.18186/jte.67038

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…There are three types of kurtosis: zero, positive, and negative. Zero kurtosis distribution generally follows Normal distribution; positive kurtosis has heavier tails and a higher peak than Normal, whereas [52], [59], [60], the underlying location can be classified in the fifth and sixth classes, which prove the location appropriateness for the installation of large-scale wind power turbines [61], [62].…”

Section: Resultsmentioning

confidence: 99%

Five different distributions and metaheuristics to model wind speed distribution

Wadi

2021

Journal of Thermal Engineering

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This paper presents a comprehensive empirical study of five distribution functions to analyze wind energy potential: Rayleigh, Weibull, Gamma, Burr Type XII, and Generalized Extreme Value. In addition, two metaheuristics optimization methods, Grey Wolf optimization and Whale optimization algorithm, are utilized to determine the optimal parameter values of each distribution. Five error measures are investigated and compared to test the accuracy of the introduced distributions and optimization methods, such as mean absolute error, root mean square error, regression coefficient, correlation coefficient, and net fitness. The Catalca site in Istanbul, Turkey, was selected to be the case study to conduct this analysis. The obtained results confirm that all introduced distributions based on optimization methods efficiently model wind speed distribution in the selected site. Although Gamma distribution based on GWO and WOA outperformed other distributions for all datasets at all heights, it was the worst in terms of computation complexity. Rayleigh distribution occupied the latest rank, but it was the best in terms of computation complexity. MATLAB 2020b and Excel 365 were used to perform this study.

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Section: Resultsmentioning

confidence: 99%

Five different distributions and metaheuristics to model wind speed distribution

Wadi

2021

Journal of Thermal Engineering

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“…The wind power density was assessed using Weibull probability density function (pdf) given by the expression in Equation 13 which have also been applied in similar studies [20,21].…”

Section: Weibull Wind Power Densitymentioning

confidence: 99%

Potential of Wind Energy Development for Water Abstraction Systems in Developing Country Context: A Case of Teso Sub-Region of Uganda

Kasozi¹,

Kiggundu²,

Wanyama³

et al. 2018

Preprint

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Wind energy powered pumps could be an alternative to conventional fuel powered pumps for water abstraction because they rely on a free energy and they are environmentally friendly. The objective of this study was to assess the potential of wind energy to operate water abstraction systems in Teso sub-region of Uganda for livestock watering Daily mean wind speeds recorded at a height of 10 m for a period of ten years (2005–2015) were collected from Amuria and Soroti Meteorological stations in the study area. Data were analyzed using Weibull distribution to evaluate the annual wind speed frequency distributions and consequently assess their potential for water abstraction. The results indicated that warmer months (January, February and March) have higher mean wind speeds than the cold months (August, September and October). High wind speeds in the dry seasons corresponded to the periods of high water demand. The highest shape parameter (k) of 3.07 was registered in 2009 and scale parameter (c) of 3.78 in 2012. The highest wind power density of 43 W/m2 was obtained the year 2012 while the lowest wind power density of 15.47 W/m2 was obtained for Soroti district in the year 2009. The maximum power extractable in Amuria in 2012 was 324 W/m2 which is potentially enough for water abstraction. Maximum discharges of 1.86 m3/s and 1.52 m3/s were obtained for Amuria and Soroti districts respectively at mean wind speeds of 5 m/s. Therefore, Teso sub region winds have potential for water abstraction and Amuria district better sites for livestock watering using wind energy.

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An economic and technological analysis of hybrid photovoltaic/wind turbine/battery renewable energy system with the highest self-sustainability

Hassan

Jaszczur

Abdulrahman³

et al. 2022

Energy Harvesting and Systems

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This research established the technoeconomic feasibility of an on-grid hybrid renewable energy system for delivering electricity to the deanery building of the Engineering College at the University of Diyala, Iraq. The most cost-effective system design was discovered by modeling and optimization, with an average daily load of 25.0 kWh and total cost and energy cost of $5142 and $0.05/kWh, respectively. In relation to the energy generated by conventional resources, the designed system is more cost-effective and has a lower carbon footprint of about 13,052 kg/year of CO2 emissions avoided. According to the research, using a hybrid power system to electrify and decarbonize the electrical energy supply could be a reliable and economical way to do both at the same time. Innovation is in integrating the suggested hybrid system so that the use of electricity can effectively be decreased to meet the load. As a result, the system components are not oversized, which reduces system costs and reduces emissions.

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An Analysis of Current and Future Wind Energy Gain Potential for Central Iowa

Cited by 3 publications

References 7 publications

Five different distributions and metaheuristics to model wind speed distribution

Five different distributions and metaheuristics to model wind speed distribution

Potential of Wind Energy Development for Water Abstraction Systems in Developing Country Context: A Case of Teso Sub-Region of Uganda

An economic and technological analysis of hybrid photovoltaic/wind turbine/battery renewable energy system with the highest self-sustainability

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