Vol: 1 Issue: 1
STATISTICAL ANALYSIS OF THE WIND SPEED DISTRIBUTION FOR THE WIND POWER POTENTIAL IN CALABAR USING THE WEIBULL DISTRIBUTION FUNCTION
Ekpo Bassey Ene, Samuel Oliver Effiom
1. INTRODUCTION
The availability of stable and reliable electricity continues to be the basis of any modern economy. Wind energy has the potential to electrify Africa while also propelling economic growth and development (Alemzero et al., 2021). Energy is critical to any nation's economic development and industrialization because it stimulates productive activities in all sectors of the economy, including industry, agriculture, mining, and commerce (Amadi, 2018). However, in developing countries, particularly in Africa, the lack of adequate measurement and/or assessment studies to determine its potential for power generation has hindered its development and utilization (O O Ajayi et al., 2011). Popular sources of power generation include; large and small-scale hydroelectric, fossil fuels, and nuclear reactors. While hydroelectric power is largely recognized as environmentally friendly, fossil fuel and nuclear power generation possess harmful effects as by-products (Oluseyi O. Ajayi et al., 2014). Wind as a sustainable energy source can no doubt be an alternative for some of the major sources of electricity globally. Wind power is generally derived from wind turbines and generators operated at various conditions in specific locations. Studies conducted throughout history have proven that energy generated from fossil fuels has contributed tremendously to global greenhouse gas and CO2 emissions. Nigeria as one of the largest consumers of energy in Africa suffers from the effect of daily power generation from conventional energy sources such as fossil fuels. As a result, methods to harness other sources of reliable and sustainable energy are sourced by researchers to further promote or aim at obtaining a net-zero energy system.
Adopting wind-based resources for wind energy generation usually varies by a countryโs location, atmospheric stability, and land holding size. Despite its abundance of energy resources, Nigeria continues to experience a power crisis. Only about 40% of the 140 million people in the country have access to grid electricity (Agbetuyi et al., 2012). However, in recent years, Nigeria has engaged in several investments and commitments to developing and providing sustainable energy infrastructures including; solar, wind, and biomass.
Although harnessing wind energy is advantageous, the viability is subject to a lot of uncertainties and empirical complications, including the irregular and erratic nature of wind. In Eq (1) as shown below, the theoretical energy carried by wind (P) is proportional to the third power of wind speed. Where A is the area swept out by the rotor blades perpendicular to the prevailing wind direction, ๐ represents the air density and ๐ is the wind speed (Jesson, 2021). As a result, an accurate understanding of wind speed characteristics is critical for several aspects of wind energy development, including identifying desirable locations, predicting the economic practicability of wind farms, and designing wind turbine structures.
๐= 12๐ด๐๐3 (1)
However, accurate wind prediction is difficult due to the wind having inconsistencies over a wide range of scales, both spatially and temporally (Yan et al., 2020). The probability distribution is usually used to characterize the variation of wind speed at a certain location in the assessment of wind power development (Burton et al., 2011), which predicts the possibility that a specified wind speed will occur. As one of the most commonly used wind energy assessment methods, the two-parameter Weibull distribution has been proven to accurately obtain the skewness of the wind speed distribution, ๐(๐), as compare to other statistical functions (Burton et al., 2011), and has been studied distinctively over the years (Akpinar & Akpinar, 2005; Aukitino et al., 2017; Jesson, 2021; Mohammadi et al., 2016; Shu et al., 2015b, 2015a). Generally, the Weibull distribution functions, as given in Eq. (2), is made up of k, a dimensionless shape parameter, which reflects the width of the distribution, and a scale parameter, c, with the unit of wind speed, which estimates the abscissa scale of the wind speed distribution (Jesson, 2021)