Modeling and Zoning Solar Energy Received at the Earth's Surface in Arid and Semiarid Regions of Central Iran

آب و خاک

Article 4, Volume 30, Issue 4 - Serial Number 48, September 2016, Pages 1294-1308   

Document Type: Research Article

DOI: 10.22067/jsw.v30i4.44412

Authors

azam gholamnia^* ; mohammadhosein mobin; atefe jebali; hamid alipor

Yazd University

Abstract

Introduction: Solar radiation (Rs) energy received at the Earth's surface is measured usingclimatological variables in horizontal surface and is widely used in various fields. Domination of hot and dry climates especially in the central regions of Iran results from decreasing cloudiness and precipitation and increasing sunshine hours, which shows the high potential of solar energy in Iran. There is a reasonable climatic field and solar radiation in most of regions and seasons which have provided an essential and suitable field to use and extend new and pure energy.
Materials and Methods: One of the common methods to estimate the solar energy received by the earthis usingtemperature variables in any place . An empirical model is proposed to estimate the solar energy as a function of other climatic variables (maximum temperature) recorded in 50 climatological, conventional stations; this model is helpful inextending the climatological solar-energy estimation in the study area. The mean values of both measured and estimated solar energy wereobjectively mapped to fill the observation gaps and reduce the noise associated with inhomogeneous statistics and estimation errors. This analysis and the solar irradiation estimation method wereapplied to 50 different climatologicalstations in Iran for monthly data during1980–2005. The main aim of this study wasto map and estimate the solar energy received in four provinces of Yazd, Esfahan, Kerman and Khorasan-e-Jonoubi.The data used in this analysis and its processing, as well as the formulation of an empirical model to estimate the climatological incident of solar energy as a function of other climatic variables, which is complemented with an objective mapping to obtain continuous solar-energy maps. Therefore, firstly the Rswasestimated using a valid model for 50 meteorological stations in which the amounts of solar radiation weren't recorded for arid and semi-arid areas in Iran. Then, the appropriate method was selected to interpolate by GS+ software and after that, the seasonal maps of the received solar energy over the ground surface were produced by GIS software. The best fitof the Gaussian model was determined in winter with the lowest residual error and the highest correlation 1.87 and 0.913respectively, in spring with the lowest RSS and highest R23.87 and 0.86 respectively and during summer with RSS and R2, 5.9 and 0.851 and the exponential model in autumn withthe RSS and R2, 3.61 and 0.88..
Results and Discussion: Naturally, some of the differences in the mean solar energy among the stations may be related to inter annual variability rather than to differences in the climatic, radiative regimes. If different periods for the climatological estimations are used, the resulting mean values can be representative of the regional climatic regime of solar energy. The results showed that 53% of Yazd province Received 26 Mj / m2.day, in summer.In winter, more than 80% of Yazd province received 15 Mj / m2.day radiation. Kerman compared to other provinces received high solar radiation, especially this feature wasmore pronounced in winter because in this season compared to Yazd, Kerman radiation didnot only showed greater range, but also about 40% of the province's total area received 16 Mj / m2.day radiation, whereas Yazd no radiation was received during this season. Because Kerman is located in the southeast of region and itreceived more solar radiation than other provinces.In this study, the amount of solar energy in surface of 4 provinces including Yazd, Esfahan, Kerman and South Khorasan in arid and semiarid regions of Iran was estimated by the geostatistic. Seasonal mean values of solar energy absorbed at the surface of 4 stationswascalculated. The results showed that Kerman with receiving 27.25 (Mj m-2. D-1) averagely has the most received solar energy and Esfahan with 21.54 (Mj m-2. D-1) during the summer has received the least solar energy. The limited records of solar energy used in thisanalysis madethe analysis of long-term variations impossible. This paper wasthe first stage of a more extensive study which involvedmonitoring the behavior of photocells under real environmental conditions, which allowedto obtain efficiency curves used in the mapping of actual photovoltaic potential inarid and semiarid regions of Central Iran. This analysis must be complemented by better, higher resolution estimates of the incident solar energy; a viable alternative for such a task is the use of satellite observations. However, a better photovoltaic prospection, with high quality data, is necessary.

Keywords

Modeling; Geostatistics; Photovoltaic cell, Solar radiation, The central regions of Iran

References

1- Ajayi O.O., Ohijeagbon O.D., Nwadialo C.E., and Olumide O. 2014. New model to estimate daily global solar radiation over Nigeria. Sustainable Energy Technologies and Assessments, 5: 28–36.

2- Almorox J., Benito M., and Hontoria C. 2005. Estimation of monthly Angstrom–Prescott equation coefficients from measured daily data in Toledo, Spain. Renewable Energy. 30: 931–6.

3- Bahadori A., and Nwaoha C. 2013. A review on solar energy utilisation in Australia. Renew Sustainable Energy. Rev; 18: 1-5.

4- Bohling G. 2005. Introduction to GeoStatistics and Variogram Analysis, Assistant Scientist Kansas Geological Survey Ferro, V., Giordano, G. and Lovino, M. 1991. Isoerosivity and erosion risk map for Sicily. Hydrology Science Journal, 36(6): 549–564.

5- Gholamnia A., Mobin M., and Sodaeizade H. 2014. Provide a general model for estimating daily solar energy in Yazd. First National Conference on Environmental Health, safety and environmental sustainability, Hamadan. (In Persian with English abstract(.

6- Haurant P., Muselli M., Pillot B., and Oberti P. 2012. Disaggregation of satellite derived irradiance maps: evaluation of the process and application to Corsica. Sol Energy; 86: 68-82.

7- Heydari M. 2004. Locating build solar power plants in Iran. Oil and Energy, 38-49. (In Persian with English abstract(.

8- Hirsche K., Boerner S., Kalkomey C., and Gastaldi C. 1998. Avoiding pitfalls in geostatistical reservoir characterization: A survival guide: The leading Edge, 17: 493-504.

9- Hohn M.E. 1998. Geostatistics and petroleum geology, Kluwer Academic Publisher, Netherlands.

10- Johnston K., Ver Hoef J.M., Krivoruchko K., and Lucas N. 2001. Using Geostatistical Analyst, Environmental Systems Research Institute, Inc (ESRI).

11- Khalid A., and Junaidi H. 2013. Study of economic viability of photovoltaic electric power for Quetta-Pakistan. Renewable Energy. 50: 25-38.

12- Khosravi M., Jahanbakhsh asl S., and Derakhshi J. 2012. Estimation and mapping of solar radiation received on a horizontal surface using climatic parameters in GIS: A Case Study of East Azerbaijan Province. geographical space Journal, 43: 43-63. (In Persian with English abstract(.

13- Lahvanian H. 2007. Solar-Photovoltaic-in power supply of agricultural water wells. Eleventh Conference on Electricity Distribution Network. (in Persian with English abstract(

14- Li M.F., Li F., BinLiu H., Tao Guo P., Wuc Wei. 2013. A general model for estimation of daily global solar radiation using air temperatures and site geographic. Parameters in Southwest China. Journal of Atmospheric and Solar-Terrestrial Physics. 92, 145–150.

15- Lu G.Y., and Wong D.W. 2008. An Adaptive Inverse-Distance Weighting spatial Interpolation Technique. Comp. Geosci. 34: 1044-1055.

16- Mogheri A., and Tavoosi T. 2013. Feasibility and zoning potential sites for the deployment of solar Panel relying based on Climate variables in Sistan-Baluchistan province. Energy Planning and Policy Journal, 1, Journal, 1: 99-114. (In Persian with English abstract(.

17- Musavi baigi M., and Ashraf B. 2012. Identify areas with the least amount of cloud to throw the mapping areas with highe solar radiation. Journal of Soil and Water (Agricultural Science and Technology), 25: 665-675. (in Persian with English abstract(

18- Myers KS., Klein SA., and Reindl DT. 2010. Assessment of high penetration of solar photovoltaics in Wisconsin. Energy Policy; 38: 73, 38-45.

19- Rivas D., Saleme-Vila S., Ortega-Izaguirre R., Chale-Lara F., and Caballero-Briones F. 2013. A climatological estimate of incident solar energy in Tamaulipas, northeastern Mexico. Renewable Energy. 60, 293-301.

20- Sabziparvar A., and Aliaiee A. 2012. Performance evaluation of artificial neural network in prediction of total daily solar radiation and comparison with model results Å (Case study: Tabriz synoptic). Journal of Geophysical, 5: 42-30. (In Persian with English abstract(.

21- Toklu E. 2013. Overview of potential and utilization of renewable energy sources in Turkey. Renew Energy. 50: 456-463.

22- Webster R., and Oliver M.A. 2000. Geostatistics for environmental scientists, Wiley press. 271 pp.