Study and Evaluation of Solar Energy Variation in Nigeria

Transcription

1 Study and Evaluation of Solar Energy Variation in Nigeria Engr. C. O. Osueke (Ph.D, Post Ph.D) 1, Engr. (Dr) P. Uzendu 2, Engr. I. D. Ogbonna 3 1 Department of Mechanical Engineering, Landmark University, Omu-Aran, Kwara State, Nigeria 2,3 Department of Mechanical Engineerign, Enugu State University of Science and Technoogy, Enugu State, Nigeria Abstract---- A successful and efficient solar power system cannot be achieved without a proper study of the geographical location of the site. This work engages a study of the solar irradiance variation in four locations in Nigeria (Enugu, Lagos, Abuja and Maiduguri) as provided by the meteorological information acquired by National Aeronautics and Space Administration research for 22 years period using the SPSS statistical tool and a bar chart. The models developed from the statistical analysis showed good levels of linear conformity with correlation coefficients of except for Abuja which had a fair value of The models also showed good levels of significance with that of Enugu yielding a high significance of Also from the bar chart, it is clearly seen that Maiduguri seemed to experience the greatest solar irradiance range of kWh/m2/day except for the month of January, November, and December. This shows that solar irradiance is more prevalent in Maiduguri when compared Enugu, Lagos, and Abuja and should be a better site to install solar power stations. Keywords--- solar irradiance, geographical location, variation, meteorological information, power station I. INTRODUCTION The use of alternative sources is gaining popularity in different parts of the world especially with lots of interest being focused on harnessing solar energy. To utilize and harness this energy, a proper knowledge of its behavior has to be known. Solar energy is available at any part of the globe, but the amount made available differs with respect to geographical locations, times and season. The solar energy available at any particular geographical location is a measure of the solar irradiance falling on that location. Solar irradiance is the solar radiation intensity falling on a surface and is measured in N/m2 or KW/m2. Some authors and researchers refer to it as insolation value. An average radiation intensity falling on an imaginary surface, perpendicular to the sun s ray and at the edge of the earth s atmosphere is called the solar constant (Isc). The word solar constant is a little misleading since, because of the earth s elliptical orbit the intensity of the solar radiation falling on the earth changes by about 7% between January 1st, when the earth is nearest the sun and July 3rd, when the earth is furthest from the sun. A yearly average value is thus taken and the solar constant equals 1367W/m2/s. Even this value is inaccurate since the output of the sun changes by about 0.25% due to sunspot cycles [2]. Paul (2009) [3] reported that about 13% of this radiation reaching the earth may be absorbed by the atmosphere and 13% scattered. This implies that the direct radiation available at the earth s surface close to the tropics in the middle of a cloudless day is about 75% of the level of the radiation at the surface of the atmosphere which is about 1,000 Joules per m2 per second. This assertion is true if the earth is assumed to be a stationary body. From basic knowledge of physics, it is known that we can receive up to 1,367W/m2/s for a part of each day because the earth rotates. The solar radiation reaching the earth surface varies for different geographical locations at a given instant of time. This is the reason why meteorological information of any geographical location has to be gotten before modeling a solar system for that location. Nwabueze et al., [1] specified that geographical factors like insolation value was needed for a proper design of a solar power system. Nigeria is a country located in the western region of Africa at latitude between 4 N and 13 N and longitude between 3 E and 15 W. The country is made up of over 36 states. Although one can attach an average value for every geographic parameter when considering her as a reference location but the fact remains that there are still variations in the amount of energy reaching different locations within her. A little grasp of curiosity as to how a particular geographical location still encounters variation in solar energy distribution across it might lead to one discovering that the various states that make up Nigeria posses different meteorological data which accounts for the variation in solar energy radiation. It is generally known that the northern part of Nigeria is always hotter than other regions, places close to the plateau are always colder while at the eastern, western & southern region have slightly moderate weather condition. All these are as a result of variation in solar irradiance brought about by variation in their position on the earth surface. Researchers in the past have done some work in evaluating solar radiation at various parts of the country. Chiemeka [4] tried to estimate the solar radiation at Uturu Abia state Nigeria, latitude 5.33 N and 6.33 N. 501

2 He obtained temperature data from 5th 31st October 2007 using the maximum and minimum thermometer placed in Stevenson screen at 1.5m after which he used the Hargreaves equation to obtain the estimate. He reported that the mean global solar radiation obtained for the period is kwh per day. He attributed this poor value to the fact that Uturu is bounded on the west and south by a hilly escarpment. Fadare [5] developed a model for prediction of solar energy in Nigeria using an artificial neural network model. Meteorological data of 195 cities in Nigeria for a period of 10 years ( ) from the National Aeronautics and space administration (NASA) geo-satellite data base were used for training and testing the network. He used meteorological data such as latitude, longitude, altitude, monthly sunshine duration, mean temperature, and relative humidity were used as inputs to the network while the solar radiation intensity was used as the output of the network. He reported that the monthly mean solar radiation potential in northern and southern regions ranged from to kwh/m2 respectively. Okundamiya et al [6] proposed an empirical model for estimating global solar radiation on horizontal surfaces for Abuja, Benin, Kastina, Lagos, Nsukka and Yola cities in Nigeria. From his report, these cities experienced a decrease in the horizontal global solar radiation from March through August (during rainy season) with benin city having the lowest monthly mean daily horizontal global solar radiation of 3.46 kwh/m2/day in July. They also reported that the variation of daily horizontal global solar radiation with month of the year in Kastina differs from other cities because Kastina is located at longitude 7.6 E, and latitude 13.0 N. Falayi et al., (2008) [7] developed a number of multilinear regression equation based on Angstrom equation to predict the relationship between global solar radiation with one or more combinations of some weather parameters for Iseyin Nigeria for five years. He reported that the equation with the highest value of correlation coefficienct (r), least value of root mean square error (RMSE), mean bias error (MBE), and mean percentage error (MPE) was adopted for the estimation of different geographical locations in Nigeria. The quest for meteorological models and data to model solar power systems is the motivating factor behind this work. To achieve this, meteorological data from four different states were studied, evaluated to establish solar insolation potential, models and comparison. II. APPROACH To perform this study, it was necessary to collect meteorological data for Enugu, Lagos, Abuja and Maiduguri. These states were selected due to their even geographical displacement within the country. The monthly averaged insolation on a horizontal surface, average daily temperature range ( C), monthly averaged relative humidity (%), and monthly average cloud amount at 12GMT (%) for 22 years were gotten for these states from the NASA atmospheric science data center. Figure I: Location of Enugu, Lagos, Abuja & Maiduguri on the Nigerian map. III. STATISTICAL ANALYSIS Using the SPSS statistical analysis tool, the meteorological data of the four states were analyzed and correlated to establish linear mathematical models to account for insolation dependency on other meteorological variable. Since the insolation value tells how much irradiation in that location. The monthly averaged insolation incident on a horizontal surface is made the dependent variable while the monthly averaged cloud amount at 12GMT, average daily temperature range, and monthly averaged relative humidity are made the independent variable. Halouami and Ngguyen (1993) [8]; Falayi et al., 2008 [7]; Al-Salihi et al., (2010) [9] acknowledged the use of statistical indicators such as: Root mean square error (RMSE) and mean bias error (MBE) for adjustment of solar radiation data. Stone (1993) [10] also proposed the t-statistics (TS) indicator for solar radiation analysis. 502

3 The SPSS statistical tool was used because it provides boundary set indicators such as coefficient of regression, value of significance, standard error of the estimate, etc to define the ideality, significance and relevance of a particular model. It also allows models to be compared. TABLE I MONTHLY AVERAGED INSOLATION INCIDENT ON A HORIZONTAL SURFACE (KWH/M2/DAY) FOR 22 YEARS Enugu (lat: 6.44, long: 7.51) Lagos (lat: 6.5, long: 3.35) Abuja (lat: 9.06, long 7.49) TABLE II MONTHLY AVERAGED CLOUD AMOUNT AT 12GMT (%) FOR 22 YEARS Enugu (lat: 6.44, long: 7.51) \ Lagos (lat: 6.5, long: 3.35) Abuja (lat: 9.06, long 7.49) TABLE III AVERAGE DAILY TEMPERATURE RANGE ( ) Enugu (lat: 6.44, long: 7.51) Lagos (lat: 6.5, long: 3.35) Abuja (lat: 9.06, long 7.49) TABLE IV MONTHLY AVERAGED RELATIVE HUMIDITY (%) Enugu (lat: 6.44, long: 7.51) Lagos (lat: 6.5, long: 3.35) Abuja (lat: 9.06, long 7.49) TABLE V RESULT OF STATISTICAL ANALYSIS R2 Sum of squares F Change Std error of the estimate Enugu (lat: 6.44, long:7.51) Lagos (lat: 6.5, long: 3.35) Abuja (lat: 9.06, long 7.49) Maiduguri (lat:11.85, long:

4 Insolation value (kwh/m 2 /day) International Journal of Emerging Technology and Advanced Engineering Table V shows the result of the statistical regression analysis carried out on the meteorological data of the four states. The analysis carried out also reveals the statistical relationship among the sets of data acquired which forms the model. Let the: Monthly average insolation incident on a horizontal surface for 22 years be I22A Monthly average cloud amount at 12GMT (%) for 22 years be C22A Average daily temperature range ( ) for 22 years be T22A Monthly averaged relative humidity for 22 years be H22A A. MODEL FOR ENUGU B. MODEL FOR LAGOS C. MODEL FOR ABUJA D. MODEL FOR MAIDUGURI Figures 1, 2, 3, and 4 shows the relationship between the insolation values of the four states Enugu Lagos Abuja Maiduguri 0 Month of the year Figure II: Insolation value at different months of the year for different geographical locations IV. DISCUSSION From table. Above, the model for Enugu has the highest correlation coefficient (R2) of 0.8 followed by that of Maiduguri: 0.731, Lagos: 0.695, and Abuja: which implies that the model for Enugu displays a better conformity to the linear regression fitting. To ascertain if models proposal is statistically significant, the value of significance ( is considered. As seen from table.., the model for Enugu state is highly recommended for predicting solar insolations in the state. To further tell the accuracy of the proposed models, the standard error of the estimate is also considered. 504 It can be seen that the model for Maiduguri & Enugu had a low profile of this error: and , in as much as the four models a pretty low value of it. This goes further to ratify the highly preciseness of the Enugu model although all other models are also acceptable. From figure 1, the monthly average insolation incident on a horizontal surface was highest in Maiduguri with a value of about 6.75kWh/m2/day except in the month of January, November, and December where it was surpassed by that Abuja. Lagos seemed to experience the lowest solar irradiation at 4kWh/m2/day except in the month of august when it appeared to be slightly above that of Enugu.

5 On close observation, it was discovered that Lagos had almost a constant insolation in the month of June, July, August and September. It was also observed that the four states experienced their lowest insolation in the month of August. All these observations are accounted for by the difference in the location of these states on the earth surface. This is majorly attributed to the latitude and longitude of that location. V. CONCLUSION Having established solar power as an alternative source of power for humankind, there is still need for a proper and well modeled system to optimally harness this energy, if not it would be as good as falling back to the old source of energy which is crude oil. It has been well reported by researchers and scientists on solar power systems that the geographical location of a site plays a major role in the design of a solar system and hence calls for thorough study of a location before designing a solar power system for that location. This work has helped to expose various facts about Nigeria geographical location using Enugu, Lagos, Abuja, and Maiduguri. Having collected meteorological data for these four locations from NASA research center, the models that were formed by statistical regression analysis using the SPSS regression tool showed linear conformity with correlation coefficients of except for Abuja which had a fair value of The insolation variable for locations also was made as a function of other meteorological data such that with the monthly averaged insolation incident on the location can be accounted for by the monthly averaged cloud amount, average daily temperature and monthly averaged relative humidity. The bar chart exposes pictorially the insolation variation within the four states and it was seen that Maiduguri had higher values of solar irradiance going by its highest value of insolation at every month except in the month of January, November and December. This implies that if a choice was to be made on installing a solar power station anywhere within these four locations, Maiduguri would be the best choice because of its highest availability of solar power of about 6.5kWh/m2/day. REFERENCES [1] Nwabueze, I.O, Chinweike, E, Aliogor, O, Design and construction of a solar electicity generator. Unpublished undergraduate thesis, Enugu State University of Science and Technology Enugu, Nigeria. [2] Solar energy reaching the earth surface. Retrieved April, 2010 from [3] Paul Burgress, Variation in light intensity at different latitudes and seasons, effects of clod cover, and the amount of direct and diffused light. Proceeding of continuous cover forestry group (CCFG) scientific meeting, Westonbirt Arboretum, Gloucestershire. [4] Chiemeka, I.U Estimation of solar radiation at Uturu Nigeria. International journal of physical sciences 3(5), [5] Fadare, D.A Modelling of solar energy potential in Nigeria using an artificial neural network model. Journal of applied energy, science direct. 86(9), [6] Okundamiya, M.S, Nzeako, A.N, Empirical model for estimating global solar radiation on horizontal surfaces for selected cities in the six geopolitical zones in Nigeria. Journal of control science and engineering. DOI: 1155/2011/ [7] Falayi, E.O, Adepitan, J.O, Rabiu, A.B, Empirical models for the correlation of global solar radiation with meteorological data for Iseyi, Nigeria. International journal of physical sciences, 3(9), [8] Halouani, N, Ngguyen, C, Calculation of monthly average global solar radiation on horizontal surface using daily hours of bright sunshine. Solar energy, 50: [9] Al-salihi, A.M, Kadum M.M, Mohammed A.J, Estimation of global solar radiation on horizontal surface using meteorological measurement for different cities in Iraq. Asian J. Sci. Pes, 3(4): [10] Stone R.J, Improving statistical procedure for evaluation of solar-radiation estimation model. Solar energy, 51: