Generalized Extreme Value Distribution Models for the Assessment of Seasonal Wind Energy Potential of Debuncha, Cameroon

Nkongho Ayuketang Arreyndip,Ebobenow Joseph

doi:10.1155/2016/9357812

Abstract

The method of generalized extreme value family of distributions (Weibull, Gumbel, and Frechet) is employed for the first time to assess the wind energy potential of Debuncha, South-West Cameroon, and to study the variation of energy over the seasons on this site. The 29-year (1983–2013) average daily wind speed data over Debuncha due to missing values in the years 1992 and 1994 is gotten from NASA satellite data through the RETScreen software tool provided by CANMET Canada. The data is partitioned into min-monthly, mean-monthly, and max-monthly data and fitted using maximum likelihood method to the two-parameter Weibull, Gumbel, and Frechet distributions for the purpose of determining the best fit to be used for assessing the wind energy potential on this site. The respective shape and scale parameters are estimated. By making use of the P values of the Kolmogorov-Smirnov statistic (K-S) and the standard error (s.e) analysis, the results show that the Frechet distribution best fits the min-monthly, mean-monthly, and max-monthly data compared to the Weibull and Gumbel distributions. Wind speed distributions and wind power densities of both the wet and dry seasons are compared. The results show that the wind power density of the wet season was higher than in the dry season. The wind speeds at this site seem quite low; maximum wind speeds are listed as between 3.1 and 4.2 m/s, which is below the cut-in wind speed of many modern turbines (6–10 m/s). However, we recommend the installation of low cut-in wind turbines like the Savonius or Aircon (10 KW) for stand-alone low energy need.

Highlights

Cameroon which is in the list of fast growing economies in Africa has experienced a very fast growth rate in the past two decades with the Government investing heavily on industrialization
The concept of energy shortage is a global issue as many world economies have embarked on alternative renewable energy sources to meet their energy demand
Nikolai Nawri et al applied the downscaling simulations technique performed with the Weather Research and Forecasting (WRF) model to determine the large-scale wind energy potential of Iceland with local wind speed distributions represented by Weibull statistics

Summary

Introduction

Cameroon which is in the list of fast growing economies in Africa has experienced a very fast growth rate in the past two decades with the Government investing heavily on industrialization. There is abundance of heavy water falls in the south to harness hydroelectric power, high solar intensities especially the northern regions for solar power enhancement, and wind power in its coastal cities for wind energy enhancement These sectors are underdeveloped and the country solely relies on hydroelectric power to meet its energy demand with most of these rivers seasonal. Nikolai Nawri et al applied the downscaling simulations technique performed with the Weather Research and Forecasting (WRF) model to determine the large-scale wind energy potential of Iceland with local wind speed distributions represented by Weibull statistics. They found that, in addition to seasonal and spatial variability, differences in average wind speed and power density exist for different wind directions.

Data and Method

Results and Discussion

Conclusion