Statistical and economic analysis of solar radiation and climatic data for the development of solar PV system in Nigeria

Abdulkarim, H. T.; Sansom, C. L.; Patchigolla, K.; King, P. Article Statistical and economic analysis of solar radiation and climatic data for the development of solar PV system in Nigeria Energy Reports Provided in Cooperation with: Elsevier Suggested Citation: Abdulkarim, H. T.; Sansom, C. L.; Patchigolla, K.; King, P. (2020) : Statistical and economic analysis of solar radiation and climatic data for the development of solar PV system in Nigeria, Energy Reports, ISSN 2352-4847, Elsevier, Amsterdam, Vol. 6, Iss. 1, pp. 309-316, https://doi.org/10.1016/j.egyr.2019.08.061 This Version is available at: https://hdl.handle.net/10419/243749 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ Available online at www.sciencedirect.com ScienceDirect Energy Reports 6 (2020) 309–316 www.elsevier.com/locate/egyr 6th International Conference on Energy and Environment Research, ICEER 2019, 22–25 July, University of Aveiro, Portugal Statistical and economic analysis of solar radiation and climatic data for the development of solar PV system in Nigeria H.T. Abdulkarim∗, C.L. Sansom, K. Patchigolla, P. King School of Water, Energy and Environment, Cranfield University, Cranfield, MK43 0AL, United Kingdom Received 5 August 2019; accepted 25 August 2019 Abstract The growth in energy demand and global concern about the environment has resulted in the drive towards alternative energy sources and consequently this research concerning solar energy harvesting of radiation received at the earth’s surface. Analysis of solar radiation data is an important tool in the accurate designing/sizing of solar Photovoltaic (PV) systems and conducting performance analysis of the system. This paper presents the statistical and economic analysis of solar radiation and climatic data for the development of Solar PV systems in Nigeria. The data for three locations, one from each of the radiation regions in Nigeria were analysed using Minitab 17. The analysis shows that Maiduguri is more viable for solar energy conversion system than the two other locations, and for the same energy demand of 1.1MWh, the peak watt (Wp) of solar PV array required are 619419.27 Wp, 821142.52 Wp and 1219489.32 Wp for Maiduguri, Minna and Port Harcourt respectively. This leads to a difference of $460,984.72 in the total project cost between Maiduguri and Minna for the same energy demand. c 2019PublishedbyElsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 6th International Conference on Energy and Environment Research, ICEER 2019. Keywords: Analysis; Energy; Environment; Data; Nigeria; PV system 1. Introduction Energy is the major key to the economic and social growth of any country, and its importance is increasing with technological and industrial developments in the world. The most widely used resource for electrical energy generation are the fossil fuels but studies have shown that these resources have many negative impacts such as global warming and pollution on environment [1]. The development of solar energy sources has in the recent times taken the centre stage all over the world [2]. This may be attributed to the fact that solar energy is a clean energy source with no negative environment impact [3]. Solar energy can be converted to any form of energy using processes which involves a number of stages [4,5]. The availability of solar energy is an important requirements in the ∗Corresponding author. E-mail address: [email protected] (H.T. Abdulkarim). https://doi.org/10.1016/j.egyr.2019.08.061 2352-4847/ c 2019 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/). Peer-review under responsibility of the scientific committee of the 6th International Conference on Energy and Environment Research, ICEER 2019. 310 H.T. Abdulkarim, C.L. Sansom, K. Patchigolla et al. / Energy Reports 6 (2020) 309–316 Nomenclature a Angstrom constant b Angstrom constant H Monthly average daily global radiation on a horizontal surface (MJ/m2day) HoMonthly average daily extra-terrestrial radiation on a horizontal surface (MJ/m2day) Isc Solar constant (=1367 W/m2) n The number of days of the year starting from the first of January S Monthly average daily number of observed sunshine hours Smax Monthly mean value of day length at a location ωsThe mean sunrise hour angle for the given month φThe latitude of the site δThe solar declination design and development of its conversion systems [3]. Its applications such as concentrated solar power (CSP) systems, photovoltaic system sizing, and solar drying systems require the knowledge of the availability of global solar irradiance. The intensity of solar radiation per day is usually one of the variables collected by meteorological stations in tropical Africa, Nigeria especially. The magnitude and variability of solar radiation data may have a significant impact on the viability analysis of a solar energy project, its detailed design as well as its economic viability [6,7]. A common approach to quantify and evaluate the solar resource in a given region is the analysis of daily, monthly and annual values of Global Horizontal Irradiation (GHI) [6]. Nigeria was chosen because it has high availability of solar irradiance which favours the development of solar energy conversion system. Fig. 1 shows Nigeria’s solar radiation map. The areas under study are indicated on the map by Zone I for Maiduguri, Zone II for Minna and Zone III for Port Harcourt. The thick circle close to Maiduguri indicates the location of study which is in the North-Eastern Nigeria. The utilization of solar energy to economically meet the world’s increase energy demand depends on two factors: available solar energy resource and appropriate technology to harness it. Electricity generation from photovoltaic panel is directly affected by solar radiation, this also makes global solar radiation data in a particular region essential for solar energy applications [1,8]. This important data must be available, reliable and accurate for planning, projection and continuity of the system [1,8]. Fig. 1. Nigeria’s solar radiation zones [9]. H.T. Abdulkarim, C.L. Sansom, K. Patchigolla et al. / Energy Reports 6 (2020) 309–316 311 Nigeria has three radiation zones as indicated in Fig. 1. In this study, radiation and climatic data were analysed for three locations which have been carefully selected to comprise one location each from the three radiation zones. This analysis will guide the development of solar PV systems in Nigeria 2. Data and methodology 2.1. Study area The study areas are Maiduguri in the Northeast, Minna in North-central and Port Harcourt in the South-south of Nigeria. These locations were carefully chosen to represent the three radiation zones in Nigeria. Table 1 presents the geographical location of the cities selected in Nigeria. Table 1. Details of study area. Location Latitude [deg.] Longitude [deg.] Altitude [m] Average annual sunshine hours Maiduguri 11.8◦N 13.2◦E 320 2877.5 Minna 9.6◦N 6.5◦E 299 2435.4 Port Harcourt 4.8◦N 7.0E 468 1474.2 Data The data used in this study are daily average insolation, monthly ambient temperature, relative humidity, and monthly sunshine hours for 11 years (2006–2016) and were collected from the Nigeria Meteorological Agency, Abuja. 2.2. Data analysis The data were analysed using Minitab 17 and considering some statistical and economic indicators. The frequency distribution was explored with correlation of the solar data by making the solar irradiance a dependent variable and individually making temperature, relative humidity and sunshine hours independent variables. The statistical indicators are R2which is the Coefficient of Determination, R (Coefficient of Correlation), Root Mean Square Error (RMSE), Mean Bias Error (MBE) and Mean Absolute Bias Error (MABE). Economic analysis was carried out using an assumed energy demand of 1.1 MWh/day. Existing models were used in the calculation of solar radiation. There are several empirical models which have been developed to predict solar radiation. The most widely used model is the modified Angstrom-type regression equation as presented in Eq. (1). H=Ho(a+bS Smax )(1) H is the monthly average daily global solar radiation falling on a horizontal surface at a particular location, Hothe monthly average daily extra-terrestrial radiation, S the monthly average daily number of observed sunshine hours, Smax the monthly mean value of day length at a particular location and “a”, “b” the climatologically determined regression constants to be determined as follows [10]: a= −0.110 +0.235Cos∅(S Smax )(2) b=1.449 −0.553Cos∅ − 0.694 (S Smax )(3) The monthly average daily extra-terrestrial radiation, Hois given by Eq. (4) Ho=24 πIsc [1+0.033cos (360n 365 )][cos∅cosδsinωs+sin∅sinδ(2πωs 360 )] (4) 312 H.T. Abdulkarim, C.L. Sansom, K. Patchigolla et al. / Energy Reports 6 (2020) 309–316 The sunset hour angle, ωsis given as ωs=cos−1[−tanδtan∅](5) The solar declination, δcan obtained from the following expression: δ=23.45 sin (360 (n+284) 365 )(6) The accuracy of the calculated global radiation is examined using RMSE, MBE and MABE which are given by Eqs. (7) to (9) [1]. RM SE =   √ 1 n n ∑ i=1 (ci−mi)2(7) M BE =1 n n ∑ i=1 (ci−mi)(8) M AB E =1 n n ∑ i=1 (|ci−mi|)(9) where ciis the ith calculated global radiation data, miis the ith measured global radiation data and n is the number of data. The sunshine hours and the day length Smax was used to establish the percentage daily sunshine. 2.3. Economic implication of differences in average solar irradiance The economic implication of differences in average solar irradiance from one location to another was studied by assuming an energy demand of 1.1 MWh per day designing a complete solar PV system for power supply in each of the locations. The panel generation factor (PGF) and peak watt are can be obtained from Eqs. (10) and (11). PG F =Average solar irradiance ×sun shine hours Standard T est Condition Irradiance (10) Wp=1.3×Energy demand PG F (11) 3. Results and discussion The group frequency distribution (GFD) histogram of the solar irradiance for the three cities are presented in Figs. 2 to 4. The data for the global daily solar irradiance for the three locations were correlated using relative humidity and ambient temperature as independent variable. Figs. 2 to 4shows that solar irradiance of more than 200 W/m2occurs about 80.1% and 61.1% in Minna and Port Harcourt respectively. A solar irradiance of more than 210 W/m2occurs about 84.3% in Maiduguri. The daily average solar irradiance was found to be 266 W/m2, 237 W/m2, 208 W/m2for Maiduguri, Minna and Port Harcourt respectively. The highest frequency occurs at solar irradiance of 311–330 W/m2, 261–280 W/m2and 221–240 W/m2for Maiduguri, Minna and Port Harcourt respectively. Table 2 shows some statistical indicator, sunshine hours and day length. The percentage sunshine per day is highest in the month of November with 68.6% and the least occurred in the month of August with 32.9%. This result shows that there exist smore cloudy sky in the month of August than any other month in the year. Figs. 5 to 10 shows the correlation between Solar Irradiance and ambient temperature and relative humidity. The Pearson’s correlation coefficient describing the relationship between solar irradiance as the dependent variable and temperature and relative humidity as independent variables are 0.252 and −0.906; 0.880 and −0.928; 0.949 and −0.909 for Maiduguri, Minna and Port Harcourt respectively. The relationship between solar irradiance and ambient temperature in the three location shows a positive linear relationship with the result for Maiduguri coming out weak. The relationship between solar irradiance and relative humidity in the three location shows a negative linear relationship with the result for the three locations showing strong compliance with the linearity between the two parameters. H.T. Abdulkarim, C.L. Sansom, K. Patchigolla et al. / Energy Reports 6 (2020) 309–316 313 Fig. 2. Grouped frequency distribution histogram of solar irradiance for Maiduguri (2006–2016). Fig. 3. Grouped frequency distribution histogram of solar irradiance in Minna (2006–2016). Fig. 4. Grouped frequency distribution histogram of solar irradiance for Port Harcourt (2006–2016). 3.1. Economic implications To study the economic implications of the differences in the average solar irradiance of the cities, an energy demand of 1.1 MWh per day was assumed. The PGF, Wp and sizing of other components were calculated (Table 3). Eq. (12) shows the total project cost (TPC). T PC =PV +I nverter +Charge Controller +Battery +Cable +Land/permitting +Construction/Installation costs (12) 314 H.T. Abdulkarim, C.L. Sansom, K. Patchigolla et al. / Energy Reports 6 (2020) 309–316 Table 2. Some statistical indicators. Month S Smax S/Smax RMSE MBE MABE JAN 6.9 11.6 0.595 15.3 −13.3 14.1 FEB 7.6 11.8 0.643 9.9 −7.4 7.9 MAR 6.7 12.1 0.558 17.0 14.5 14.7 APR 7.6 12.3 0.616 19.4 17.1 17.9 MAY 7.5 12.6 0.596 18.4 16.2 15.8 JUN 6.6 12.7 0.525 18.7 16.1 15.9 JUL 4.7 12.6 0.375 9.4 3.2 7.4 AUG 4.1 12.4 0.329 13.6 8.3 10.2 SEP 5.5 12.2 0.456 11.4 −3.3 8.3 OCT 7.5 11.9 0.628 18.8 −10.6 13.3 NOV 8.0 11.7 0.686 21.9 −14.9 18.0 DEC 7.4 11.6 0.641 11.2 1.0 8.5 Table 3. Peak watt and project cost. Location 11-year daily average solar irradiance (W/m2) 11-year daily average sunshine hour Peak watt of solar array required Total project cost ($) Maiduguri 266 7.89 619 419.27 3,223,971.60 Minna 237 6.68 821 142.52 3,6849,56.32 Port Harcourt 218 4.89 1 219 489.32 4,595,270.99 There is about 24.6% increase in peak watt and about $460,984.72 increase in total project cost. Fig. 5. Fitted regression line of ambient temperature and solar irradiance for Maiduguri (2006–2016). Fig. 6. Fitted regression line of relative humidity and solar irradiance for Maiduguri (2006–2016). 4. Conclusion In the light of the preceding results, the following conclusions were drawn: H.T. Abdulkarim, C.L. Sansom, K. Patchigolla et al. / Energy Reports 6 (2020) 309–316 315 Fig. 7. Fitted regression line of ambient temperature and solar irradiance for Minna (2006–2016). Fig. 8. Fitted regression line of relative humidity and solar irradiance for Minna (2006–2016). Fig. 9. Fitted regression line of ambient temperature and solar irradiance for Port Harcourt (2006–2016). Fig. 10. Fitted regression line of relative humidity and solar irradiance for Port Harcourt (2006–2016). 1. Maiduguri has the highest average daily solar irradiance and Port Harcourt with lowest, therefore Maiduguri is more viable for solar energy system development, 316 H.T. Abdulkarim, C.L. Sansom, K. Patchigolla et al. / Energy Reports 6 (2020) 309–316 2. 84.3%, 80.1% and 61.1% of the period under study has a solar irradiance of above 200 W/m2for Maiduguri, Minna and Port Harcourt respectively. 3. The Project Cost of solar PV system increase with reduction in average solar irradiance and sunshine hours. 4. There is a significant positive linear relationship between solar irradiance and ambient temperature for the three locations because the correlation coefficients are significantly different from zero. 5. 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