Wind energy potential and economic analysis with a comparison of different methods for determining the optimal distribution parameters

Muhammad Abid Saeed, Zahoor Ahmed and Weidong Zhang

Renewable Energy, 2020, vol. 161, issue C, 1092-1109

Abstract: Pakistan is one of the countries heavily dependent on hydrocarbon fuel for energy production which is causing a severe climate change; however, wind energy seems to be a long-term solution. Various statistical distributions have been used to draw the analysis of wind data, but the selection of an optimum method has been a challenge. This work is an assessment of wind power potential of a site located near the southern coast of Pakistan. Data collected in two years is analyzed at four heights using three variations of Weibull parameters. Weibull parameters computed through the proposed mean bias error-based artificial intelligence grey wolf optimization were compared with the computations through Rayleigh and Justus’s empirical numerical methods. Root mean square error, determination of coefficient, and mean bias error, are computed to validate and compare the computed results. The wind characteristics like most probable and Maximum energy-carrying wind are found to be in excellent compatibility with most of the wind turbines that could be used on the site. The results obtained along with a brief cost analysis, for eight selected wind turbine systems, show that the considered site is suitable for the production of a wind power project.

Keywords: Weibull distribution; Rayleigh distribution; Wind zone; Energy assessment; Optimization algorithm; Wind potential (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S096014812031140X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:161:y:2020:i:c:p:1092-1109

DOI: 10.1016/j.renene.2020.07.064

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().