Wind energy potential and economic evaluation of WPS using WECSs in three selected locations of Northern Ethiopia

Kodicherla, Shiva Prashanth Kumar; Gaddada, Satyanarayana; Shaik, Nagaraju

doi:10.1080/20421338.2017.1303990

Cited by 7 publications

(2 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a two-parameter distribution function [40], which is typically used in wind speed analyses. Noticeable recent applications include the wind study using LiDAR technology at a site around Lake Erie [41], the description of wind resources in Northern Ethiopia for water pumping and electricity generation [42], and determining optimal placements of wind farms in Turkey [43]. Its mathematical expression is…”

Section: The Weibull Probability Density Functionmentioning

confidence: 99%

Wind speed description and power density in northern Spain

Herrero-Novoa

Pérez

Sánchez

et al. 2017

Energy

View full text Add to dashboard Cite

Wind resources are increasingly being investigated as a clean alternative for generating energy. This paper analyses the daily wind speed recorded at 46 automatic weather stations located in Navarre, northern Spain, in 2005e2015. Key points are the surface density of stations and the range of time that ensure a faithful depiction of wind speed together with surface calculations from image analysis and correlation with height. Different statistics were used. Median wind speed at 10 m was low, about 3.3 m s À1 and its interquartile range was narrow, about 2.3 m s À1. Nearly half the surface shows a median wind speed above 3.0 m s À1. The method of moments was employed to calculate the parameters of the Weibull distribution. Around half of the surface presented a shape parameter above 2.25 and the scale parameter was above 4 m s À1 for nearly 41% of the region. Although wind resources are not suitable for wind turbine applications in most of the region, since the wind speed is low in low-lying areas, about 12% of the region is suitable for stand-alone applications and, moreover, a substantial part of the region, around 23%, presents satisfactory wind resources for the installation of wind turbines.

show abstract

Section: The Weibull Probability Density Functionmentioning

confidence: 99%

Wind speed description and power density in northern Spain

Herrero-Novoa

Pérez

Sánchez

et al. 2017

Energy

View full text Add to dashboard Cite

show abstract

“…Gaddada and Kodicherla (2016) have evaluated wind power capacity and wind energy cost estimates for electricity generation systems in eight selected locations in Tigray (Ethiopia). Kodicherla et al (2017) explored the potential of wind energy and developed an economic assessment of the water pumping system in various wind power conversion systems. In three selected Fiji Island stations, Kodicherla et al (2018) have investigated the potential of wind power-assisted wind hydrogen production using different types of turbines.…”

Section: Introductionmentioning

confidence: 99%

Wind turbine performance analysis for energy cost minimization

Charabi

Abdul‐Wahab

2020

Renewables

View full text Add to dashboard Cite

The use of wind energy worldwide has overgrown in recent years to reduce greenhouse gas emissions. Wind power is free, but the installation and maintenance of wind turbines remain very costly. The size of the installation of the wind turbine is not only determined by wind statistics at a given location, but also by turbine infrastructure and maintenance costs. The payback time of the turbine is dependent on turbine energy costs. This study estimates the wind power generation capacity of Northern and Southern Oman and discusses the selection of the most economical, efficient and reliable wind turbines in Oman. HOMER Pro Software was used in this paper to evaluate the wind energy data in the north and south of Oman and to provide well-informed guidance on the most suitable turbines for the power needs of each area. Six different standard wind turbines were measured and compared in terms of the cost of energy and performance. The simulation analysis reveals that the DW54 turbine is the best possible turbine to generate electricity in northern Oman at $0.119/kW. Due to the difference in the wind regime between the north and the south of Oman, the simulation showed that the Hummer H25.0-200 kW turbine is the best option for south Oman with power generation at $0.070/kW. The northern wind turbine plant can efficiently contribute to decarbonization of the energy sector in Oman, with a potential reduction of CO 2 emission approximately 19,000 tons/year in comparison to natural gas and 28,000 tons/year in comparison to diesel. In the Southern Power Plant, carbon emissions are reduced by 18,000 and 12,000 tons/year compared to diesel and natural gas.

show abstract