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Analysis from the new solar radiation Atlas for Saudi Arabia
Available online at www.sciencedirect.com
ScienceDirect Solar Energy 130 (2016) 116–127 www.elsevier.com/locate/solener
Analysis from the new solar radiation Atlas for Saudi Arabia Sulaiman AlYahya, Mohammad A. Irfan ⇑ Department of Mechanical Engineering, Qassim University, Buraydah, Saudi Arabia Received 19 June 2015; received in revised form 27 November 2015; accepted 26 January 2016
Communicated by: Associate Editor Jan Kleissl
Abstract This paper presents one of the first discussions on the new Solar Atlas of Saudi Arabia, which was launched in February 2014. It assesses selected solar resource and surface meteorological measurements available from the new Renewable Resource Atlas for Saudi Arabia developed by the King Abdullah City for Atomic and Renewable Energy (KACARE) as part of the Renewable Resource Monitoring and Mapping (RRMM) Program. The Solar Atlas provides live data recorded from 41 stations across the country. Accurate solar resource data is critical in reducing technical and financial risks of deploying utility-scale solar energy conversion systems. This paper presents solar maps of the country over time, showing that the direct normal irradiance (DNI) in various regions of the country ranges from approximately 9000 W h/m2/day in the summer months to 5000 W h/m2/day in the winter months. Global horizontal irradiance (GHI) in various regions can be as high as 8.3 kW h/m2/day. One year of ground based solar radiation measurements from new stations at KACARE Headquarters Riyadh and Qassim University are compared with satellite-based model estimates of long-term DNI and GHI solar resources on a monthly mean daily total basis. Comparing the locally measured DNI data at KACARE Headquarters Riyadh with GeoModel, the average difference is about 6.9% of the reported values, the average measurement uncertainty being 8.4%. For GHI measured at KACARE Headquarters Riyadh, a fairly good agreement can be seen between the RRMM values and GeoModel with the average difference being about 5.6%, while the average measurement uncertainty being 6.7%. It might be noted that the GeoModel has data uncertainties for monthly mean daily total radiation in the range of ±8% to ±15% for DNI, and ±4% to ±8% for GHI. Finally, the paper reviews data from the recording station at Qassim University (QU), which is equipped with a variety of instruments for measuring solar radiation and surface metrological conditions. The measured values of DNI at QU varied from a maximum of 8367 W h/m2/day in July to a minimum of 4702 W h/m2/day in January. In terms of percentages, the average difference between the measured monthly mean daily total radiation and the GeoModel estimates is about 5.4% of the reported values, the average measurement uncertainty being 5.1%. In most cases, the observed differences between measured and modeled data were within the combined estimated uncertainty. The results from Solar Atlas are critical in guiding policies, reducing the risks for deploying solar facilities and providing judicious information for construction of solar facilities. Ó 2016 Elsevier Ltd. All rights reserved.
Keywords: Solar energy resources; Renewable resource Atlas for Saudi Arabia; Direct normal irradiance; Global horizontal irradiance; Solar radiation measurements; Spatial variability of solar resources
1. Introduction
⇑ Corresponding author.
E-mail addresses: [email protected] (S. AlYahya), [email protected]. sa (M.A. Irfan). http://dx.doi.org/10.1016/j.solener.2016.01.053 0038-092X/Ó 2016 Elsevier Ltd. All rights reserved.
The development of any solar energy facility requires a range of solar resource data, the most critical being the temporal and spatial distribution of solar irradiation along with estimates of data uncertainties. One of the
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earliest Solar Atlases is attributed to the United States National Renewable Energy Laboratory (NREL), with data dated back to 1961 (Marion and Wilcox, 1994). For designers and engineers of solar energy-related systems, the Solar Radiation Data Manual for Flat-Plate and Concentrating Collectors gives the solar resource available for various types of collectors for the United States and its territories. The data in the manual were modeled using hourly values of direct beam and diffuse horizontal solar radiation from the National Solar Radiation Data Base (NSRDB). The NSRDB contains; modeled (93%) and measured (7%) global horizontal, diffuse horizontal, and direct beam solar radiation for 1961– 1990. The European Solar Radiation Atlas (ESRA) was subsequently made available on CD-ROM with data from 1981 (Beyer et al., 1997 and Page et al., 2001). Some of the more recent published Solar Atlases contain data from Brazil (Tiba et al., 2004), Australia (Blanksby et al., 2013), Djibouti (Pillot et al. 2013a, 2013b), and Myanmar (Janjai et al., 2013). For the Arab region, the Arab League Educational, Cultural, and Scientific Organization (ALECSO) provided the first atlas of solar radiation in 1998 (Alnaser et al., 2004). The atlas contained data from nearly 280 stations from 19 Arab states over 10 years. It included tables of monthly means of direct normal and global solar radiation. The records showed that the extreme recorded mean of global solar emissions was 6.7 kW h/m2/day in Tamenraset, Algeria, while the minimum recorded average was 4.1 kW h/m2/day in Mosul, Iraq. For the Gulf region, the UAE Solar Atlas made by Masdar presents extensive data on solar radiations (UAE Solar Atlas, 2013). The earliest solar energy projects in Saudi Arabia that included data collection were initiated by King Abdulaziz City of Science and Technology (KACST) (Huraib et al., 1996). In recent years, concentrating solar thermal power companies have shown interest in solar investment in Saudi Arabia in particular. To realize these goals, initial solar resource data was required by investors to be estimated primarily through satellite observations of cloud amounts rather than on-the-ground solar radiation measurement stations. King Abdullah City for Atomic and Renewable Energy (KACARE) took responsibility for this data collection and developed the Renewable Resource Atlas as part of the Renewable Resource Monitoring and Mapping (RRMM) Program. A recent study (Zell et al., 2015) presents the data collected by 30 monitoring sites of RRMM, over a period of one year. The study presents the data from each station. The GHI and DNI data are reported and their standard deviation in mentioned. In comparison, this paper compares the station data at select stations with the GeoModel. Uncertainties of measurement are mentioned as well as uncertainties of GeoModel are mentioned. Further detailed discussion of the Atlas follows.
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1.1. Definitions and acronyms 1.1.1. Direct normal irradiance Solar radiation is scattered and absorbed by the atmosphere. Solar radiation can take the form of diffused, direct, or reflected radiation. Direct normal irradiance (DNI) is also known as direct solar radiation at normal incidence and describes the descending solar radiation emitted at a solid angle from the disk of the sun. The instrument used to measure DNI is known as a pyrheliometer with measurement for monthly mean daily total radiation being W h/m2/day. Pyrheliometers have a 5–5.7° field of view (full angle). DNI data are useful for concentrating technologies (CPV and CSP). 1.1.2. Global horizontal irradiance Global horizontal irradiance (GHI) is the total amount of shortwave radiation that is available from the sky hemisphere (2p steradians) on a horizontal surface (Marion and Wilcox, 1994). GHI is the sum of DNI x cosine (solar zenith angle) and diffuse horizontal irradiance. An unshaded pyranometer is used to measure the horizontal rays, and the units are in W h/m2/day. Ground-reflected radiation is not considered to be part of the GHI. It is to be included in the Global Tilted Irradiance (GTI). GHI and GTI data are useful for flat plate collectors. 1.1.3. Measurement uncertainties While reporting solar irradiance data, the readers must be aware of the associated estimate of measurement uncertainties. The expanded uncertainty U95 which is a representative figure for combined uncertainty is normally reported. To date, uncertainty in measuring solar irradiance, with a properly calibrated and installed state-of-the-art radiometer is greater than 4% for pyranometers and greater than 2.7% for pyrheliometers under clear-sky conditions (Reda 2011 and Habte et al., 2014). 1.1.4. King Abdulaziz City of Science and Technology (KACST) King Abdulaziz City of Science and Technology is an independent scientific body reporting to the Prime Minister. It functions both as the National Science Agency and as National Research Laboratories. It is responsible for leading and funding scientific growth in the country (King Abdulaziz City of Science and Technology, 2015). 1.1.5. King Abdullah City for Atomic and Renewable Energy (KACARE) King Abdullah City for Atomic and renewable Energy (KACARE) was established in 2010 with the aim of building a sustainable future for Saudi Arabia by developing a substantial alternative energy capacity fully supported by world-class local industries (The Establishing Order, 2010).
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1.2. Background Saudi Arabia lies in the solar belt between the latitudes of 16° and 33°N and longitudes of 34° and 56°E (Saudi Arabia, 2014). The country’s oil reservoirs are currently used to generate energy (Al Ghabban, 2013); however, domestic oil consumption, specifically for household usage, is surging and contributing to environmental degradation. Saudi Arabia is currently the seventh largest oil consumer in the world (EIA, 2013), and it is predicted that oil consumption for electricity will eventually transform the country from an oil exporter to an oil importer. Therefore, the Arabs have begun exploring renewable resources for a cleaner source of energy. To establish appropriate market strategies for the promotion of solar energy, resources, technologies, and credible information are necessary (Huraib et al., 1996). The Renewable Resource Atlas of the Kingdom of Saudi Arabia is a government-sponsored online data portal that consists of the most up-to-date and reliable information on renewable energy resources in Saudi Arabia (http s://rratlas.kacare.gov.sa/RRMMPublicPortal/?q=en/Hom e). The first edition of the Saudi Arabian Solar Radiation Atlas, covering the period of 1971 through 1980, was created by the Saudi Arabian National Center for Science and Technology (SANCST), which later became KACST. The atlas also indicated suitable conditions for photovoltaic (PV) systems, which can become contaminated by the desert sand and malfunction, therefore helping determine the best location for installation (Renewable Resource Atlas of Saudi Arabia). In 1987, a five-year agreement was signed between KACST and the United States Department of Energy (DOE) in the field of renewable energy research and development. A 12-station solar radiation-monitoring network was established with the help of National Renewable Energy Laboratory (NREL), resulting in the creation of a new Solar Radiation Atlas for Saudi Arabia in 1999 (Myers et al., 2002). Sunshine duration was measured at 17 stations, and GHI was measured at 12 stations (Myers et al., 2002). The two above-mentioned initial solar radiation atlases provided the foundation for solar research and exploration in the country. KACARE was established in 2010 with the goal of building a sustainable energy future for Saudi Arabia by developing an extensive alternative energy capacity fully supported by superlative native industries (The Establishing Order, 2010). The Renewable Resource Monitoring and Mapping (RRMM) program was created by KACARE in 2013 (Renewable Resource Atlas) in collaboration with NREL and with partnerships with the International Renewable Energy Agency (IRENA), Saudi Arabian universities, and government institutions. The paper presents a comparison of satellite-based model estimates by GeoModel Solar of monthly mean daily total irradiation (W h/m2) averaged over the period 1994–2012 for direct normal irradiance (DNI) and global
horizontal irradiance (GHI), as presented in the Renewable Resource Atlas, with ground-based measurements at KACARE Riyadh and Qassim University during 2014. The spatial variability of the DNI resource for the Kingdom is presented in a series of monthly maps based on the GeoModel estimates. The satellite-based model estimates of solar radiation, presented in the Renewable Resource Atlas, are based on METEOSAT observations of clouds and outputs from the European Center for Mid-range Weather Forecasting (ECMWF) Monitoring Atmospheric Composition and Climate (MACC) model including monthly averaged aerosol optical depth (AOD). The GeoModel has a spatial resolution of 1 km 1 km and the data uncertainties reported are in the range of ±8% to ±15% for DNI, and ±4% to ±8% for GHI (Sˇu´ri and Cebecauer, 2015). The ground-based measurements are from a Rotating Shadowband Radiometer, model RSR2. The U95 uncertainties for the ground-based measurements at various stations are in the range of 7–9% for DNI, and 6–7% for GHI (Solar Resource Data Documentation, Renewable Resource Atlas). The objectives of this paper are to compare groundbased measurements at two locations and present maps of the spatial variability of solar radiation using a satellite-based model. The Analysis includes DNI geographical maps as well as comparison between ground based measurements and GeoModel historical data. Finally, a brief description of the apparatus installed is provided along with some readings for the Qassim University. This data and analysis will be beneficial for policymaking as well as for planning the best locations for Solar PV and CSP. 2. Methods The analysis performed and presented in this paper has been carried out using KACARE’s Renewable Resource Monitoring and Mapping Program (RRMM). The program monitors and maps the solar, wind, geothermal and waste-to-energy resources of Saudi Arabia with recordings from approximately 41 stations throughout the Kingdom. The stations are classified into Tier 1 – Research Stations, Tier 2 – Midrange stations and Tier 3 – Simple Stations. Further details are explained in Table 1. Depending on the station tier, the instrumentation includes a variety of radiometers for measuring solar radiation, anemometers and vanes for wind measurements, and a number of instruments for collecting relevant surface meteorological conditions. Additionally, the atlas contains maps of Saudi Arabian infrastructure, electrical grids, population concentrations, slopes, elevations, and ecologically protected areas. The details of instruments installed in these stations are given in Tables 2 and 3 (Solar Monitoring Network Summary Report, 2015). The global community, including NREL, helped KACARE officials determine station locations throughout
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the diverse geographical and climatological conditions of the Kingdom. Areas with the maximum level of solar resources, large resource gradients due to coastal/inland,
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urban/rural, and mountain/valley differences, complex terrain, and proximity to the electrical power distribution and load centers were chosen for station construction. Details
(a)
(b)
(c)
(d)
(e)
(f)
Fig. 1. DNI maps extracted from GeoModel (a) January, (b) February, (c) March, (d) April, (e) May, (f) June, (g) July, (h) August, (i) September, (j) October, (k) November and (l) December (m) Legend.
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(g)
(h)
(i)
(j)
(k)
(l) Fig 1. (continued)
of the 41 monitoring stations, with the geographic coordinates and start of data collection are summarized in Table 4 (Solar Monitoring Network Summary Report, 2015). Some of the key stations include the KACARE headquarters in Riyadh, Hafar Al-Batin in the Eastern Province, King Abdulaziz University on the West Coast,
Tabuk University in the North, Qassim University (QU) in the central region, and Jazan University in the South. Other than the Data Display feature on the home page, the high-resolution data are sold based by subscription. The atlas is a non-static evolving tool that helps investors determine the best locations, plan
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3.1. GeoModel maps
(m) Legend Fig 1. (continued)
operations, and prepare for outages. The monthly mean daily total data summaries are available for free for the general public, academics and researchers can register and do further analysis, businesses have to pay a small fee however. The following section compares one year of ground based solar radiation measurements from new stations at KACARE Headquarters Riyadh and Qassim University with satellite-based model estimates of long-term DNI and GHI solar resources on a monthly mean daily total basis. 3. Results and discussion This section presents an analysis of some results extracted from the Renewable Resource Atlas (The Renewable Resource Atlas). In the first section satellite based maps are presented from GeoModel, while the second section compares the ground based measurements from two stations with the satellite based GeoModel.
Fig. 1 shows the GeoModel estimates for monthly mean daily total DNI in Saudi Arabia, for various months, including a color legend. The boxes in the color legend show the range of DNI in W h/m2/day. The stars in the map relate to some of the major cities in Saudi Arabia. Note the spatial resolution of GeoModel is 1 km 1 km. Also the GeoModel maps are based on the average values for the period 1994–2012. GeoModel Solar estimates of the monthly mean daily total GHI and DNI peak in summer months, with spatial differences throughout the Kingdom evident from the monthly maps. Higher solar radiation values are observed in southern region with an average maximum of 7004 W h/m2. In January, the coastal areas exhibited differences in monthly mean daily total DNI, with the DNI values ranging from 6500 to 7000 W h/m2/day in Tabuk on the West Coast, but only 3500–4000 W h/m2/day in Dammam on the East Coast. In March, the Tabuk region still showed the highest DNI, continuing to range between 6.5 and 7.0 kW h/m2/day. The southern belt showed DNI values in the range of 6500–7000 W h/m2/day. The months of May through August are considered the summer period in Saudi Arabia. For the month of May, the northwest regions showed the greatest DNI, ranging between 6500 and 7000 W h/m2/day. The month of July, the peak summer month, produced a considerable increase in DNI values for the whole country. DNI for the northern region of Tabuk ranged from 9000 to 9500 W h/m2/day; for the central region of Riyadh, 6000–6500 W h/m2/day; and for Dammam on the East Coast, only 5500– 6000 W h/m2/day. For the month of September, while the northern region exhibited DNI values between 8000 and 8500 W h/m2/day, the central regions of Riyadh and Buraidah, as well as the eastern region, showed values of only 6000–6500 W h/m2/day. In November, higher DNI values moved South, with values of 5000–6500 W h/m2/day in Jazan and 7000–7500 W h/m2/day for most of the central and
Table 1 Table of Tier concept. Source: Solar Monitoring Network Summary Report as of: February, 2015. Station Tier type
Number of stations commissioned
Tier 1-Research Stations: most complete and complex among monitoring stations Configuration A-Research and Development Laboratory: full complement of radiometric instruments with independent and redundant solar radiation component data, plus basic meteorological instruments Configuration B-Solar Broadband and Spectral Monitoring Station: all broadband solar radiometers, selected solar spectral radiometers and photometers, pyrgeometers, and basic meteorological instruments Configuration C-Broadband Baseline Monitoring Station: fundamental broadband solar irradiances (DNI, GHI, DHI, and GTI), plus basic meteorological instruments Tier 2-Mid-Range Stations: a rotating shadowband radiometer (RSR), this station configuration produces fundamental solar resource and surface meteorological data Tier 3-Simple Stations: cluster of 8 instruments taking only global horizontal and plane-of-array irradiance (GHI and GTI) measurements plus temperature readings, surrounding a single rotating shadowband radiometer (RSR).
18 2
Total
41
4 12 23 0
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For the GeoModel the average values for the period 1994–2012 are reported. For the most part RRMM measurements are greater than the GeoModel average, except in November where the RRMM reports a value 420 W h/m2 lower than that of GeoModel. On the other hand, the maximum difference of RRMM reported value with GeoModel can be seen in February where RRMM shows a value 1320 W h/m2 greater than GeoModel. On the average, the difference is 442 W h/m2. It might be noted that the range of error in RRMM measurements for this
southern regions. Arar in the Northeast and Jeddah in the Southwest had lower DNI values in the range of 4500–5000 W h/m2/day. 3.2. Comparison of ground based RRMM measurements with satellite based GeoModel Fig. 2 compares the measured values of DNI by RRMM for the year 2014 with the GeoModel (The Renewable Resource Atlas, 2015) at KACARE Headquarters Riyadh.
Table 2 Table of instruments for Tier 1 stations. Source: Solar Monitoring Network Summary Report as of February, 2015. No.
Instrument type
Measurement
No.
Instrument type
Measurement
1
Air temperature probe
Air temperature
18
Sky imager
2
Anemometer
Wind speed @ 10 m
19
3
Anemometer
Wind speed @ 3 m
20
4 5 6
Barometer GPS receiver Inverted pyranometer
Barometric pressure Precipitable water vapor Upwelling solar (shortwave) radiation
21 22 23
Soiling measurement system Soiling measurement System Solar aureole monitor Spectroradiometers Spectroradiometers
7 8
Inverted pyrgeometer PAR – photometer
24 25
Sun photometer Tilted pyranometer
9
Pyranometer
26
Tipping bucket
Precipitation
10
Pyranometer
27
UV A/B photometer
Ultraviolet radiation (Region A)
11
Pyrgeometer
28
UV A/B photometer
Ultraviolet radiation (Region B)
12
Pyrheliometer
29
Visibility sensor
Horizontal visibility
13 14
Relative humidity probe Rotating shadowband radiometer Rotating shadowband radiometer Rotating shadowband radiometer Sky camera
Upwelling infrared (longwave) radiation Photosynthetically Active Radiation (PAR) Diffuse Horizontal Irradiance (DHI) primary Global Horizontal Irradiance (GHI) primary Downwelling Infrared (Longwave) Irradiance (DIR) Direct Normal Irradiance (DNI) primary Relative humidity Diffuse Horizontal Irradiance (DHI) secondary Direct Normal Irradiance (DNI) computed Global Horizontal Irradiance (GHI) secondary Cloud fraction
Sky properties (including cloud amounts) PV module maximum power output (soiling) PV module short circuit current (soiling) Sun shape (circumsolar radiation) Direct normal spectral irradiance Global horizontal spectral irradiance Aerosol optical depth (AOD) Global Tilted Irradiance (GTI)
30 31
Wind vane Wind Vane
Wind direction @ 10 m Wind direction @ 3 m
32
Wind Vane
33
Wind Vane
Wind direction at peak speed @ 10 m Wind direction at peak speed @ 3m
15 16 17
Table 3 Table of Tiers 2 and 3 instruments. Source: Solar Monitoring Network Summary Report as of February, 2015. Tier 2 instruments
Tier 3 instruments
No.
Instrument type
Measurement
No.
Instrument type
Measurement
1 2 3
Air temperature probe Anemometer Barometer
Air temperature Wind speed @ 3 m Barometric pressure
1 2 3
Air temperature probe Barometer Pyranometer
4 5
Relative humidity probe Rotating shadowband radiometer Rotating shadowband radiometer Rotating shadowband radiometer Wind vane
Relative humidity Diffuse Horizontal Irradiance (DHI) Direct Normal Irradiance (DNI) computed Global Horizontal Irradiance (GHI) Wind direction @ 3 m
4 5
Relative humidity probe Rotating shadowband radiometer Rotating shadowband radiometer Rotating shadowband radiometer Tilted pyranometer
Air temperature Barometric pressure Global Horizontal Irradiance (GHI) T3 Relative humidity Diffuse Horizontal Irradiance (DHI) Direct Normal Irradiance (DNI) computed Global Horizontal Irradiance (GHI) T3 Global Tilted Irradiance (GTI) T3
6 7 8
6 7 8
Tier 3 micro-network comprises a cluster of approximately 8 of these instruments, around a single RSR, covering an area of 1–50 sq. km.
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Table 4 Table of Geographic coordinates of all monitoring sites and start of data collection. Source: Solar Monitoring Network Summary Report as of February, 2015. S. No.
List of monitoring sites
Tier
Latitude
Longitude
Installed date
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Al Uyaynah Research Station King Abdullah University of Science and Technology University of Dammam King Abdulaziz University Main Campus Qassim University Jazan University King Faisal University Taif University Wadi Addawasir College of Technology Tabuk University Al Wajh Technical Institute Royal Commission of Yanbu Arar Technical Institute Al Jouf Technical Institute Rania Technical Institute Al Baha University Saline Water Conversion Corporation (Al Khafji) Najran University K.A.CARE City Site Tier 2 Station K.A.CARE Building Olaya St King Fahd University of Petroleum & Minerals King Abdulaziz University East Hada Alsham Campus Al Aflaaj Technical Institute Afif Technical Institute Al Dawadmi College of Technology Shaqra University Majmaah University Salman bin Abdulaziz University Timaa Technical Institute Saline Water Conversion Corporation (Hagl) Duba Technical Institute Al Hanakiyah Technical Institute Saline Water Conversion Corporation (Umluj) Saline Water Desalination Research Institute Sharurah Technical Institute Al Qunfudhah Technical Institute King Abdulaziz University (Osfan Campus) Hafar Al Batin Technical College Tuhamat Qahtan Technical Institute Saline Water Conversion Corporation (Farasan) King Saud University
1A 1A 1B 1B 1B 1B 1C 1C 1C 1C 1C 1C 1C 1C 1C 1C 1C 1C 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
24.90689 22.3065 26.39519 21.49604 26.34668 16.96035 25.34616 21.43278 20.43008 28.38287 26.2561 24.14434 30.918154 29.79383 21.21499 20.17933 28.50671 17.63228 24.52958 24.70814 26.30355 21.80117 22.27948 23.92118 24.5569 25.17279 25.85891 24.14717 27.61727 29.28997 27.34103 24.85577 25.00411 26.9042 17.47586 19.15197 21.89252 28.33202 17.7749 16.692097 24.72359
46.39721 39.10701 50.18898 39.24492 43.76645 42.54586 49.5956 40.49173 44.89433 36.48396 36.443 37.94569 41.079801 40.04886 42.84852 41.63561 48.45504 44.53735 46.43635 46.67896 50.14412 39.72854 46.73319 42.94815 44.47411 45.14198 45.41889 47.26999 38.5252 34.93002 35.72295 40.536 37.27382 49.76274 47.08618 41.08111 39.2539 45.95708 43.17555 42.098767 46.61639
January 13, 2013 June 01, 2013 May 26, 2013 May 28, 2013 June 02, 2013 November 01, 2014 May 28, 2013 June 04, 2013 July 31, 2013 September 24, 2013 September 25, 2013 December 01, 2014 December 01, 2014 December 01, 2014 December 01, 2014 December 01, 2014 December 01, 2014 December 12, 2013 January 08, 2013 January 15, 2013 May 22, 2013 June 04, 2013 June 10, 2013 July 15, 2013 July 16, 2013 July 16, 2013 July 18, 2013 July 22, 2013 July 23, 2013 July 25, 2013 July 25, 2013 August 27, 2013 August 29, 2013 September 01, 2013 September 03, 2013 September 04, 2013 September 15, 2013 October 06, 2013 November 18, 2013 September 01, 2014 November 01, 2014
Fig. 2. DNI comparison of RRMM local measurements with GeoModel at KACARE Headquarters Riyadh.
period is 426–514 W h/m2. In terms of percentages, the average difference between the measured monthly mean daily total radiation and the GeoModel estimates is about 6.9% of the reported values, the average measurement
Fig. 3. GHI comparison of RRMM local measurements with GeoModel at KACARE Headquarters Riyadh.
uncertainty being 8.4%. In most months, the observed differences between measured and modeled data were within the combined estimated uncertainty as shown in Table 7.
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A statistical analysis was carried out to determine the significance of the differences between the 12 month means of the GeoModel (m1) and RRMM measured (m2) data. The Null Hypothesis (Ho) is satisfied when m1 = m2, and the alternate hypothesis (Ha) is satisfies when m1 – m2. The statistical analysis of the data revealed a p-value of 0.191, indicating that at significance level a = 0.20 there is a statistically significant difference between the means of the two sample datasets, whereas at lower values of a we fail to reject the null hypothesis. Fig. 3 compares the measured values of GHI by RRMM for the year 2014 with the GeoModel (Renewable Resource Atlas, 2015) at KACARE Headquarters Riyadh. For the GeoModel the average values for the period 1994–2012 are reported. A maximum difference of 693 W h/m2 is seen in April and a minimum difference of 37 W h/m2 is observed in September. The reported value of uncertainty in measurements for RRMM for this data being in the range of 270–480 W h/m2. A fairly good agreement can be seen between the RRMM values and GeoModel with the average difference between the measured monthly mean daily total radiation and the GeoModel estimates being about 5.6% of the reported values, the average measurement uncertainty being 6.7%. In all months, the observed differences between measured and modeled data were within the combined estimated uncertainty as shown in Table 7. The statistical analysis of the data revealed a p-value of 0.520, indicating that at significance level a = 0.53 there is a statistically significant difference between the means of the two sample datasets, whereas at lower values of a we fail to reject the null hypothesis. It is worthwhile to mention that there can be significant inter-annual variabilities in the measured data. Though the current RRMM atlas is relatively new to measure the interannual variabilities, elsewhere the inter-annual variabilities reported (Va´zquez et al., 2011) can range between ±8% for DNI and ±5% for GHI. Whereas another report gives the inter-annual coefficient of variance for DNI to range between 1% and 10% (Wilcox and Gueymard, 2007).
Fig. 4. Rotating shadowband radiometer at Qassim University.
Fig. 5. Sun tracker at Qassim University.
3.3. Qassim University solar resource monitoring station Due to the authors’ affiliation with QU, the university’s resource monitoring station is discussed in detail here. Qassim Province is located in the center of Saudi Arabia with an area of 58,046 km2 and a population of 1.2 million (AlQassim Province, 2014). Its capital, Buraidah, is located at latitude 25.8063°N and longitude 42.8732°E. As a part of KACARE’s regional partnership program with universities, KACARE has installed solar monitoring equipment at Qassim University, including a rotating shadowband radiometer (Fig. 4), spectroradiometer, and sun tracker (Fig. 5). The sun tracker has thermopile-based radiometers for independent measurements of GHI (measured by unshaded pyranometer), DNI (measured by pyrheliometer), and DHI (measured shaded pyranometer). Table 5 presents the Qassim University station summary (Station Summary Report, 2014). Table 6 lists the various equipment installed at the station. Fig. 6 shows the average DNI measurement at QU for the year 2014. As seen in the figure, the DNI varied between a maximum of 7900 W h/m2/day and a minimum of 4700 W h/m2/day. A maximum difference of 1625 W h/m2 is seen in the month of February and a minimum difference of 21 W h/m2 is observed in month of January. In terms of percentages the average difference between the measured monthly mean daily total radiation and the GeoModel estimates is about 5.4% of the reported values, the average measurement uncertainty being 5.1%. In most months, the observed differences between measured and modeled data were within the combined estimated uncertainty, as shown in Table 7. However, the maximum difference can vary between 35% (RRMM data lower than GeoModel data) and +24% (RRMM data higher than GeoModel data). The statistical analysis of the data revealed a p-value of 0.30, indicating that at significance level a = 0.31 there is a statistically significant difference between the means of the two sample datasets, whereas at lower values of a we fail to reject the null hypothesis.
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The differences observed in Figs. 2, 3 and 6 can occur due to many possible sources (Sˇu´ri and Cebecauer, 2015) including:
(v) Difficulties of modeling surface irradiances in complex terrain, etc. (vi) Issues in ground measurements.
(i) Inconsistent periods of record: The comparison is made between satellite based GeoModel (1994– 2002) and ground based measurements by RRMM for the year 2014. (ii) Inter-annual variability: For the ground based measurements there will be inter-annual variability which has not been taken into account in the current analysis. (iii) Limitations of GeoModel input atmospheric data (Aerosol Optical Depth, Water Vapor, Ozone, etc.) (iv) Accuracy of Environmental Variables (Altitude, Terrain shading, Temperature)
While comparing the ground based measurements with the satellite based estimates, it is pertinent to mention the combined uncertainty. The combined uncertainty (Ucombined) is defined by (Suri and Cebecauer, 2014): qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi U combined ¼ U 2model þ U 2measurements
Table 5 Qassim University station summary information. Station name City Latitude (N) Longitude (E) Station type and measurements
Data transmittal Station power source Operating since Station elevation Calibration schedule Maintenance schedule
Qassim University Solar Monitoring Station Qassim 26.34668° 43.76645° Research (Tier 1B) station containing all broadband solar radiometers, selected solar spectral radiometers and photometers, pyrgeometers, and basic meteorological instruments, plus instruments measuring dust deposition and horizontal visibility Internet connection Electric grid June 2, 2013 698 m As per manufacturer specifications Daily
The uncertainty of GeoModel has been mentioned earlier (Suri and Cebecauer, 2015). The uncertainty of measurements includes the uncertainty of instruments and the uncertainty due to interannual variability of data. In this research the interannual variability of data has not been included due to the nascent data being for only two years. Table 7 lists the summary of combined estimated uncertainties. The first column lists the reported uncertainty of RRMM measured data, for the month where maximum
Fig. 6. Average DNI measured at Qassim University station and comparison with historical GeoModel data.
Table 6 Qassim University solar resource monitoring station equipment and measurements (1-min resolution). Equipment
Measurement
Nominal uncertainty
Anemometer Barometer Rotating shadowband radiometer Rotating shadowband radiometer Rotating shadowband radiometer Rotating shadowband radiometer Temp./relative humidity (RH) probe Wind vane
Wind speed @ 3 m (Tier 2 or Tier 1 roof sites) Barometric pressure Diffuse horizontal irradiance (DHI) DNI computed GHI primary GHI secondary Air temperature and RH Wind direction at 3 m (Tier 2 or Tier 1 roof)
±1.1% ±1.5 mb ±4% ±4% ±4% ±4% ±0.6 °C for temperature, ±3% to ±7% for RH ±4°
Table 7 Summary of combined uncertainty.
KACARE headquarters DNI KACARE headquarters GHI Qassim University DNI a
RRMMa uncertainty (± %)
GeoModel uncertainty (± %)
Combined uncertainty (± %)
Maximum observed difference (%)
8.13 6.5 5.5
15 8 15
17.06 10.31 15.98
21 10.8 35
RRMM Uncertainty reported for the particular month where maximum difference is observed.
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difference is observed. The second column lists the GeoModel uncertainty as mentioned earlier in this paper. The third column is the combined estimated uncertainty. The last column lists the maximum observed difference between the values of RRMM measurements and GeoModel estimates, for a particular month. It can be seen that the maximum difference is greater than the combined estimated uncertainty. The above work presents and initial overview of the RRMM solar map with comparisons between ground based measurement for the year 2014 and historical GeoModel. For future work, continued solar radiation measurements will provide a basis for estimating the inter-annual variability of solar resources at Qassim University, and Spectral irradiance measurements will strengthen the ability to provide accurate inputs of aerosol optical depth and other parameters needed by the satellite-based models. 4. Conclusions The current paper is one of the first academic reports of the recently launched Solar Atlas of Saudi Arabia. The atlas, established in February 2014, presents publically accessible data of temperature, DNI, and GHI from 41 recording and monitoring stations spread across the country. The solar maps from GeoModel show DNI averaging 9000 W h/m2/day in the summer months in the northern areas, 6000–6500 W h/m2/day in the central belt, and 6000–6500 W h/m2/day in the eastern region. In the winter months, DNI averaging 8000 W h/m2/day was observed in the northern and southern areas, and DNI of 7000– 7500 W h/m2/day was measured in the central belt. The northeastern Arar region and southwestern Jeddah region showed a DNI in the range of 4500–5000 W h/m2/day. The data comparison presented in this paper are based on long-term estimates by GeoModel (average of data from 1994 to 2012), and the measurements from 2014 at KACARE Headquarters Riyadh and QU using the RSR2. The DNI measured at KACARE Headquarters Riyadh showed an average difference of 442 W h/m2 with the GeoModel, while the range of error in RRMM measurements for this period is 426–514 W h/m2. In terms of percentages, the average difference is about 6.9% of the reported values, which is well within the combined uncertainty. A maximum difference in of 693 W h/m2 in GHI is seen in April and a minimum difference of 37 W h/m2 is observed in September, while uncertainty in measurements for RRMM for this data being in the range of 270– 480 W h/m2. A fairly good agreement can be seen between the RRMM values and GeoModel, for GHI, with the average difference being about 5.6%, which is well within the combined uncertainty. It might be noted that the GeoModel has data uncertainties in the range of ±8% to ±15% for DNI, and ±4% to ±8% for GHI. The measured values of DNI at QU varied from a maximum of 8367 W h/m2/day in July to a minimum of
4702 W h/m2/day in January. The comparison with GeoModel showed a maximum difference of 1625 W h/m2 in the month of February and a minimum difference of 21 W h/m2 in month of January. The uncertainty in measurements for this data being in the range of 200–557 W h/m2. In terms of percentages, the average difference is about 5.4% of the reported values, the average measurement uncertainty being 5.1%. Looking at the combined estimated uncertainty of RRMM measurements and GeoModel, the maximum observed difference between the two values lies mostly within the combined estimated uncertainty. With improved model inputs of aerosol optical depth, total precipitable water vapor, ozone, etc., based on RRMM measurements, perhaps the model performance can be improved and the estimated uncertainties reduced. It is hoped the results of this study will provide the evidence needed to establish a degree of confidence in the solar resource information available from the Solar Atlas and become instrumental in establishing a solar PV or concentrated solar power (CSP) facility in any geographical area of the country. The results from Solar Atlas are critical in guiding policies and to ascertain feasibility of installing solar plants for the country. As future work, further studies can be carried out for comparisons of data from Qassim University and K.A. CARE HQ in downtown Riyadh; RSR2 vs Kipp & Zonen instruments at QU; Photometric and/or spectral measurements at QU station. Acknowledgments The authors acknowledge Solar Atlas of Saudi Arabia, prepared by KACARE (http://rratlas.kacare.gov.sa). The access provided was greatly helpful in the preparation of solar maps and downloading the data for this paper. The access is also helpful for teaching and research. References Al Ghabban, A., 2013. Presentation, KACARE, Saudi Arabia’s renewable energy strategy and solar energy deployment roadmap. IRENA Lecture Program, 26 March 2013. Al-Qassim Province, 2014. https://en.wikipedia.org (accessed 28.04.14). Alnaser, W.E., Eliagoubi, B., Al-Kalak, A., Trabelsi, H., Al-Maalej, M., El-Sayed, H.M., Alloush, M., 2004. First solar radiation atlas for the Arab world. Renew. Energy 29, 1085–1107. Beyer, H.G., Czeplak, G., Terzenbach, U., Wald, L., 1997. Assessment of the method used to construct clearness index maps for the New European Solar Radiation Atlas (ESRA). Sol. Energy 61 (6), 389–397. Blanksby, C., Bennett, D., Langford, S., 2013. Improvement to an existing satellite data set in support of an Australia Solar Atlas. Sol. Energy 98, 111–124. EIA, Energy Information Administration, USA, 2013.. Habte, A., Sengupta, M., Reda, I. Andreas, A., Konings J., 2014. Calibration and measurement uncertainty estimation of radiometric data. Presented at Solar 2014 San Francisco, California July 6–10, 2014. Conference Paper NREL/CP-5D00-62214 November 2014.
S. AlYahya, M.A. Irfan / Solar Energy 130 (2016) 116–127 Huraib, F.S., Hasnain, S.M., Alawaji, S.E., 1996. Lessons learned from solar energy projects in Saudi Arabia, WREC. WREC-IV World Renewable Energy Congress No. 4, Denver, Colorado, ETATS-UNIS (15/06/1996), 1996, vol. 9, No. 1–4, pp. 1144–1147. Janjai, S., Masiri, I., Laksanaboonsong, J., 2013. Satellite-derived solar resource maps for Myanmar. Renew. Energy 53, 132–140. King Abdulaziz City of Science and Technology, 2015. (accessed 16.06.15). Marion, W., Wilcox, S., 1994. Solar Radiation Data Manual for FlatPlate and Concentrating Collectors. . Myers, D.R., Wilcox, S.M., Marion, W.F. Al-Abbadi, N.M., bin Mahfoodh, M., Al-Otaibi, Z., 2002. Final Report for Annex IIAssessment of Solar Radiation Resources in Saudi Arabia 1998–2000. NREL/TP-560-31546. . Page, J., Albuisson, M., Wald, L., 2001. The European solar radiation Atlas: a valuable digital tool. Sol. Energy 71 (1), 81–83. Pillot, B., Muselli, M., Poggi, P., Haurant, P., Hared, I., 2013a. The first disaggregated solar atlas of Djibouti: a decision-making tool for solar systems integration in the energy scheme. Renew. Energy 57, 57–69. Pillot, B., Muselli, M., Poggi, P., Haurant, P., Hared, I., 2013b. Solar energy potential atlas for planning energy system off-grid electrification in the Republic of Djibouti. Energy Convers. Manage. 69, 131– 147. Reda, I., 2011. Method to calculate uncertainties in measuring shortwave solar irradiance using thermopile and semiconductor solar radiometers. Technical Report, NREL/TP-3B10-52194, July 2011. Renewable Resource Atlas, 2015. King Abdullah City for Atomic and Renewable Energy (K.A.CARE), Saudi Arabia. . Saudi Arabia, 2014. Wikipedia (accessed 19.04.14). Solar Monitoring Network Summary Report, 2015. (accessed 12.07.15).
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Station Summary Report, 2014. Qassim University Solar Resource Monitoring Station, prepared by KACARE – RRMM program, February 2014.. Solar Monitoring Network Summary Report as of February, 2015. (accessed 12.07.15). Solar Resource Data Documentation, 2016. (accessed on January, 2016. Sˇu´ri, M., Cebecauer, T., 2014. Satellite based solar resource data: model validation statistics versus user’s uncertainty. ASES SOLAR 2014 Conference, San Francisco, 7–9 July 2014. Sˇu´ri, M., Cebecauer, T., 2015. Uncertainty of satellite-based solar resource data. Presented at ISES Webinar on Solar Resource Data Applications for Utility Planning and Operations, 23 February 2015. Tiba, C., Fraidenraich, N., Gallegos, H.G., Lyra, F.J.M., 2004. Brazilian solar resource Atlas CD-ROM. Renew. Energy 29, 991–1001. The Establishing Order, King Abdullah City for Atomic and Renewable Energy, 2010. . The UAE Solar Atlas, 2013. By the Research Center for Renewable Energy Mapping and Assessment (ReCREMA, Masdar). . Va´zquez, D.P., Wilbert, S., Gueymard, C.A., Alados-Arboledas, L., Santos-Alamillos, F. J. Granados-Mun˜oz, M.J., 2011. Interannual Variability of Long Time Series of DNI and GHI at PSA, Spain (2001– 2010). Technical Report. . Wilcox, S., Gueymard, C.A., 2007. Spatial and temporal variability of the solar resource in the United States (1998–2005). NREL Technical Report. . Zell, E., Gasim, S., Wilcox, S., Katamoura, S., Stoffel, T., Shibli, H., Engel-Cox, J., Al Subie, M., 2015. Assessment of solar radiation resources in Saudi Arabia. Sol. Energy 119, 422–438.
ScienceDirect Solar Energy 130 (2016) 116–127 www.elsevier.com/locate/solener
Analysis from the new solar radiation Atlas for Saudi Arabia Sulaiman AlYahya, Mohammad A. Irfan ⇑ Department of Mechanical Engineering, Qassim University, Buraydah, Saudi Arabia Received 19 June 2015; received in revised form 27 November 2015; accepted 26 January 2016
Communicated by: Associate Editor Jan Kleissl
Abstract This paper presents one of the first discussions on the new Solar Atlas of Saudi Arabia, which was launched in February 2014. It assesses selected solar resource and surface meteorological measurements available from the new Renewable Resource Atlas for Saudi Arabia developed by the King Abdullah City for Atomic and Renewable Energy (KACARE) as part of the Renewable Resource Monitoring and Mapping (RRMM) Program. The Solar Atlas provides live data recorded from 41 stations across the country. Accurate solar resource data is critical in reducing technical and financial risks of deploying utility-scale solar energy conversion systems. This paper presents solar maps of the country over time, showing that the direct normal irradiance (DNI) in various regions of the country ranges from approximately 9000 W h/m2/day in the summer months to 5000 W h/m2/day in the winter months. Global horizontal irradiance (GHI) in various regions can be as high as 8.3 kW h/m2/day. One year of ground based solar radiation measurements from new stations at KACARE Headquarters Riyadh and Qassim University are compared with satellite-based model estimates of long-term DNI and GHI solar resources on a monthly mean daily total basis. Comparing the locally measured DNI data at KACARE Headquarters Riyadh with GeoModel, the average difference is about 6.9% of the reported values, the average measurement uncertainty being 8.4%. For GHI measured at KACARE Headquarters Riyadh, a fairly good agreement can be seen between the RRMM values and GeoModel with the average difference being about 5.6%, while the average measurement uncertainty being 6.7%. It might be noted that the GeoModel has data uncertainties for monthly mean daily total radiation in the range of ±8% to ±15% for DNI, and ±4% to ±8% for GHI. Finally, the paper reviews data from the recording station at Qassim University (QU), which is equipped with a variety of instruments for measuring solar radiation and surface metrological conditions. The measured values of DNI at QU varied from a maximum of 8367 W h/m2/day in July to a minimum of 4702 W h/m2/day in January. In terms of percentages, the average difference between the measured monthly mean daily total radiation and the GeoModel estimates is about 5.4% of the reported values, the average measurement uncertainty being 5.1%. In most cases, the observed differences between measured and modeled data were within the combined estimated uncertainty. The results from Solar Atlas are critical in guiding policies, reducing the risks for deploying solar facilities and providing judicious information for construction of solar facilities. Ó 2016 Elsevier Ltd. All rights reserved.
Keywords: Solar energy resources; Renewable resource Atlas for Saudi Arabia; Direct normal irradiance; Global horizontal irradiance; Solar radiation measurements; Spatial variability of solar resources
1. Introduction
⇑ Corresponding author.
E-mail addresses: [email protected] (S. AlYahya), [email protected]. sa (M.A. Irfan). http://dx.doi.org/10.1016/j.solener.2016.01.053 0038-092X/Ó 2016 Elsevier Ltd. All rights reserved.
The development of any solar energy facility requires a range of solar resource data, the most critical being the temporal and spatial distribution of solar irradiation along with estimates of data uncertainties. One of the
S. AlYahya, M.A. Irfan / Solar Energy 130 (2016) 116–127
earliest Solar Atlases is attributed to the United States National Renewable Energy Laboratory (NREL), with data dated back to 1961 (Marion and Wilcox, 1994). For designers and engineers of solar energy-related systems, the Solar Radiation Data Manual for Flat-Plate and Concentrating Collectors gives the solar resource available for various types of collectors for the United States and its territories. The data in the manual were modeled using hourly values of direct beam and diffuse horizontal solar radiation from the National Solar Radiation Data Base (NSRDB). The NSRDB contains; modeled (93%) and measured (7%) global horizontal, diffuse horizontal, and direct beam solar radiation for 1961– 1990. The European Solar Radiation Atlas (ESRA) was subsequently made available on CD-ROM with data from 1981 (Beyer et al., 1997 and Page et al., 2001). Some of the more recent published Solar Atlases contain data from Brazil (Tiba et al., 2004), Australia (Blanksby et al., 2013), Djibouti (Pillot et al. 2013a, 2013b), and Myanmar (Janjai et al., 2013). For the Arab region, the Arab League Educational, Cultural, and Scientific Organization (ALECSO) provided the first atlas of solar radiation in 1998 (Alnaser et al., 2004). The atlas contained data from nearly 280 stations from 19 Arab states over 10 years. It included tables of monthly means of direct normal and global solar radiation. The records showed that the extreme recorded mean of global solar emissions was 6.7 kW h/m2/day in Tamenraset, Algeria, while the minimum recorded average was 4.1 kW h/m2/day in Mosul, Iraq. For the Gulf region, the UAE Solar Atlas made by Masdar presents extensive data on solar radiations (UAE Solar Atlas, 2013). The earliest solar energy projects in Saudi Arabia that included data collection were initiated by King Abdulaziz City of Science and Technology (KACST) (Huraib et al., 1996). In recent years, concentrating solar thermal power companies have shown interest in solar investment in Saudi Arabia in particular. To realize these goals, initial solar resource data was required by investors to be estimated primarily through satellite observations of cloud amounts rather than on-the-ground solar radiation measurement stations. King Abdullah City for Atomic and Renewable Energy (KACARE) took responsibility for this data collection and developed the Renewable Resource Atlas as part of the Renewable Resource Monitoring and Mapping (RRMM) Program. A recent study (Zell et al., 2015) presents the data collected by 30 monitoring sites of RRMM, over a period of one year. The study presents the data from each station. The GHI and DNI data are reported and their standard deviation in mentioned. In comparison, this paper compares the station data at select stations with the GeoModel. Uncertainties of measurement are mentioned as well as uncertainties of GeoModel are mentioned. Further detailed discussion of the Atlas follows.
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1.1. Definitions and acronyms 1.1.1. Direct normal irradiance Solar radiation is scattered and absorbed by the atmosphere. Solar radiation can take the form of diffused, direct, or reflected radiation. Direct normal irradiance (DNI) is also known as direct solar radiation at normal incidence and describes the descending solar radiation emitted at a solid angle from the disk of the sun. The instrument used to measure DNI is known as a pyrheliometer with measurement for monthly mean daily total radiation being W h/m2/day. Pyrheliometers have a 5–5.7° field of view (full angle). DNI data are useful for concentrating technologies (CPV and CSP). 1.1.2. Global horizontal irradiance Global horizontal irradiance (GHI) is the total amount of shortwave radiation that is available from the sky hemisphere (2p steradians) on a horizontal surface (Marion and Wilcox, 1994). GHI is the sum of DNI x cosine (solar zenith angle) and diffuse horizontal irradiance. An unshaded pyranometer is used to measure the horizontal rays, and the units are in W h/m2/day. Ground-reflected radiation is not considered to be part of the GHI. It is to be included in the Global Tilted Irradiance (GTI). GHI and GTI data are useful for flat plate collectors. 1.1.3. Measurement uncertainties While reporting solar irradiance data, the readers must be aware of the associated estimate of measurement uncertainties. The expanded uncertainty U95 which is a representative figure for combined uncertainty is normally reported. To date, uncertainty in measuring solar irradiance, with a properly calibrated and installed state-of-the-art radiometer is greater than 4% for pyranometers and greater than 2.7% for pyrheliometers under clear-sky conditions (Reda 2011 and Habte et al., 2014). 1.1.4. King Abdulaziz City of Science and Technology (KACST) King Abdulaziz City of Science and Technology is an independent scientific body reporting to the Prime Minister. It functions both as the National Science Agency and as National Research Laboratories. It is responsible for leading and funding scientific growth in the country (King Abdulaziz City of Science and Technology, 2015). 1.1.5. King Abdullah City for Atomic and Renewable Energy (KACARE) King Abdullah City for Atomic and renewable Energy (KACARE) was established in 2010 with the aim of building a sustainable future for Saudi Arabia by developing a substantial alternative energy capacity fully supported by world-class local industries (The Establishing Order, 2010).
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1.2. Background Saudi Arabia lies in the solar belt between the latitudes of 16° and 33°N and longitudes of 34° and 56°E (Saudi Arabia, 2014). The country’s oil reservoirs are currently used to generate energy (Al Ghabban, 2013); however, domestic oil consumption, specifically for household usage, is surging and contributing to environmental degradation. Saudi Arabia is currently the seventh largest oil consumer in the world (EIA, 2013), and it is predicted that oil consumption for electricity will eventually transform the country from an oil exporter to an oil importer. Therefore, the Arabs have begun exploring renewable resources for a cleaner source of energy. To establish appropriate market strategies for the promotion of solar energy, resources, technologies, and credible information are necessary (Huraib et al., 1996). The Renewable Resource Atlas of the Kingdom of Saudi Arabia is a government-sponsored online data portal that consists of the most up-to-date and reliable information on renewable energy resources in Saudi Arabia (http s://rratlas.kacare.gov.sa/RRMMPublicPortal/?q=en/Hom e). The first edition of the Saudi Arabian Solar Radiation Atlas, covering the period of 1971 through 1980, was created by the Saudi Arabian National Center for Science and Technology (SANCST), which later became KACST. The atlas also indicated suitable conditions for photovoltaic (PV) systems, which can become contaminated by the desert sand and malfunction, therefore helping determine the best location for installation (Renewable Resource Atlas of Saudi Arabia). In 1987, a five-year agreement was signed between KACST and the United States Department of Energy (DOE) in the field of renewable energy research and development. A 12-station solar radiation-monitoring network was established with the help of National Renewable Energy Laboratory (NREL), resulting in the creation of a new Solar Radiation Atlas for Saudi Arabia in 1999 (Myers et al., 2002). Sunshine duration was measured at 17 stations, and GHI was measured at 12 stations (Myers et al., 2002). The two above-mentioned initial solar radiation atlases provided the foundation for solar research and exploration in the country. KACARE was established in 2010 with the goal of building a sustainable energy future for Saudi Arabia by developing an extensive alternative energy capacity fully supported by superlative native industries (The Establishing Order, 2010). The Renewable Resource Monitoring and Mapping (RRMM) program was created by KACARE in 2013 (Renewable Resource Atlas) in collaboration with NREL and with partnerships with the International Renewable Energy Agency (IRENA), Saudi Arabian universities, and government institutions. The paper presents a comparison of satellite-based model estimates by GeoModel Solar of monthly mean daily total irradiation (W h/m2) averaged over the period 1994–2012 for direct normal irradiance (DNI) and global
horizontal irradiance (GHI), as presented in the Renewable Resource Atlas, with ground-based measurements at KACARE Riyadh and Qassim University during 2014. The spatial variability of the DNI resource for the Kingdom is presented in a series of monthly maps based on the GeoModel estimates. The satellite-based model estimates of solar radiation, presented in the Renewable Resource Atlas, are based on METEOSAT observations of clouds and outputs from the European Center for Mid-range Weather Forecasting (ECMWF) Monitoring Atmospheric Composition and Climate (MACC) model including monthly averaged aerosol optical depth (AOD). The GeoModel has a spatial resolution of 1 km 1 km and the data uncertainties reported are in the range of ±8% to ±15% for DNI, and ±4% to ±8% for GHI (Sˇu´ri and Cebecauer, 2015). The ground-based measurements are from a Rotating Shadowband Radiometer, model RSR2. The U95 uncertainties for the ground-based measurements at various stations are in the range of 7–9% for DNI, and 6–7% for GHI (Solar Resource Data Documentation, Renewable Resource Atlas). The objectives of this paper are to compare groundbased measurements at two locations and present maps of the spatial variability of solar radiation using a satellite-based model. The Analysis includes DNI geographical maps as well as comparison between ground based measurements and GeoModel historical data. Finally, a brief description of the apparatus installed is provided along with some readings for the Qassim University. This data and analysis will be beneficial for policymaking as well as for planning the best locations for Solar PV and CSP. 2. Methods The analysis performed and presented in this paper has been carried out using KACARE’s Renewable Resource Monitoring and Mapping Program (RRMM). The program monitors and maps the solar, wind, geothermal and waste-to-energy resources of Saudi Arabia with recordings from approximately 41 stations throughout the Kingdom. The stations are classified into Tier 1 – Research Stations, Tier 2 – Midrange stations and Tier 3 – Simple Stations. Further details are explained in Table 1. Depending on the station tier, the instrumentation includes a variety of radiometers for measuring solar radiation, anemometers and vanes for wind measurements, and a number of instruments for collecting relevant surface meteorological conditions. Additionally, the atlas contains maps of Saudi Arabian infrastructure, electrical grids, population concentrations, slopes, elevations, and ecologically protected areas. The details of instruments installed in these stations are given in Tables 2 and 3 (Solar Monitoring Network Summary Report, 2015). The global community, including NREL, helped KACARE officials determine station locations throughout
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the diverse geographical and climatological conditions of the Kingdom. Areas with the maximum level of solar resources, large resource gradients due to coastal/inland,
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urban/rural, and mountain/valley differences, complex terrain, and proximity to the electrical power distribution and load centers were chosen for station construction. Details
(a)
(b)
(c)
(d)
(e)
(f)
Fig. 1. DNI maps extracted from GeoModel (a) January, (b) February, (c) March, (d) April, (e) May, (f) June, (g) July, (h) August, (i) September, (j) October, (k) November and (l) December (m) Legend.
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(g)
(h)
(i)
(j)
(k)
(l) Fig 1. (continued)
of the 41 monitoring stations, with the geographic coordinates and start of data collection are summarized in Table 4 (Solar Monitoring Network Summary Report, 2015). Some of the key stations include the KACARE headquarters in Riyadh, Hafar Al-Batin in the Eastern Province, King Abdulaziz University on the West Coast,
Tabuk University in the North, Qassim University (QU) in the central region, and Jazan University in the South. Other than the Data Display feature on the home page, the high-resolution data are sold based by subscription. The atlas is a non-static evolving tool that helps investors determine the best locations, plan
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3.1. GeoModel maps
(m) Legend Fig 1. (continued)
operations, and prepare for outages. The monthly mean daily total data summaries are available for free for the general public, academics and researchers can register and do further analysis, businesses have to pay a small fee however. The following section compares one year of ground based solar radiation measurements from new stations at KACARE Headquarters Riyadh and Qassim University with satellite-based model estimates of long-term DNI and GHI solar resources on a monthly mean daily total basis. 3. Results and discussion This section presents an analysis of some results extracted from the Renewable Resource Atlas (The Renewable Resource Atlas). In the first section satellite based maps are presented from GeoModel, while the second section compares the ground based measurements from two stations with the satellite based GeoModel.
Fig. 1 shows the GeoModel estimates for monthly mean daily total DNI in Saudi Arabia, for various months, including a color legend. The boxes in the color legend show the range of DNI in W h/m2/day. The stars in the map relate to some of the major cities in Saudi Arabia. Note the spatial resolution of GeoModel is 1 km 1 km. Also the GeoModel maps are based on the average values for the period 1994–2012. GeoModel Solar estimates of the monthly mean daily total GHI and DNI peak in summer months, with spatial differences throughout the Kingdom evident from the monthly maps. Higher solar radiation values are observed in southern region with an average maximum of 7004 W h/m2. In January, the coastal areas exhibited differences in monthly mean daily total DNI, with the DNI values ranging from 6500 to 7000 W h/m2/day in Tabuk on the West Coast, but only 3500–4000 W h/m2/day in Dammam on the East Coast. In March, the Tabuk region still showed the highest DNI, continuing to range between 6.5 and 7.0 kW h/m2/day. The southern belt showed DNI values in the range of 6500–7000 W h/m2/day. The months of May through August are considered the summer period in Saudi Arabia. For the month of May, the northwest regions showed the greatest DNI, ranging between 6500 and 7000 W h/m2/day. The month of July, the peak summer month, produced a considerable increase in DNI values for the whole country. DNI for the northern region of Tabuk ranged from 9000 to 9500 W h/m2/day; for the central region of Riyadh, 6000–6500 W h/m2/day; and for Dammam on the East Coast, only 5500– 6000 W h/m2/day. For the month of September, while the northern region exhibited DNI values between 8000 and 8500 W h/m2/day, the central regions of Riyadh and Buraidah, as well as the eastern region, showed values of only 6000–6500 W h/m2/day. In November, higher DNI values moved South, with values of 5000–6500 W h/m2/day in Jazan and 7000–7500 W h/m2/day for most of the central and
Table 1 Table of Tier concept. Source: Solar Monitoring Network Summary Report as of: February, 2015. Station Tier type
Number of stations commissioned
Tier 1-Research Stations: most complete and complex among monitoring stations Configuration A-Research and Development Laboratory: full complement of radiometric instruments with independent and redundant solar radiation component data, plus basic meteorological instruments Configuration B-Solar Broadband and Spectral Monitoring Station: all broadband solar radiometers, selected solar spectral radiometers and photometers, pyrgeometers, and basic meteorological instruments Configuration C-Broadband Baseline Monitoring Station: fundamental broadband solar irradiances (DNI, GHI, DHI, and GTI), plus basic meteorological instruments Tier 2-Mid-Range Stations: a rotating shadowband radiometer (RSR), this station configuration produces fundamental solar resource and surface meteorological data Tier 3-Simple Stations: cluster of 8 instruments taking only global horizontal and plane-of-array irradiance (GHI and GTI) measurements plus temperature readings, surrounding a single rotating shadowband radiometer (RSR).
18 2
Total
41
4 12 23 0
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For the GeoModel the average values for the period 1994–2012 are reported. For the most part RRMM measurements are greater than the GeoModel average, except in November where the RRMM reports a value 420 W h/m2 lower than that of GeoModel. On the other hand, the maximum difference of RRMM reported value with GeoModel can be seen in February where RRMM shows a value 1320 W h/m2 greater than GeoModel. On the average, the difference is 442 W h/m2. It might be noted that the range of error in RRMM measurements for this
southern regions. Arar in the Northeast and Jeddah in the Southwest had lower DNI values in the range of 4500–5000 W h/m2/day. 3.2. Comparison of ground based RRMM measurements with satellite based GeoModel Fig. 2 compares the measured values of DNI by RRMM for the year 2014 with the GeoModel (The Renewable Resource Atlas, 2015) at KACARE Headquarters Riyadh.
Table 2 Table of instruments for Tier 1 stations. Source: Solar Monitoring Network Summary Report as of February, 2015. No.
Instrument type
Measurement
No.
Instrument type
Measurement
1
Air temperature probe
Air temperature
18
Sky imager
2
Anemometer
Wind speed @ 10 m
19
3
Anemometer
Wind speed @ 3 m
20
4 5 6
Barometer GPS receiver Inverted pyranometer
Barometric pressure Precipitable water vapor Upwelling solar (shortwave) radiation
21 22 23
Soiling measurement system Soiling measurement System Solar aureole monitor Spectroradiometers Spectroradiometers
7 8
Inverted pyrgeometer PAR – photometer
24 25
Sun photometer Tilted pyranometer
9
Pyranometer
26
Tipping bucket
Precipitation
10
Pyranometer
27
UV A/B photometer
Ultraviolet radiation (Region A)
11
Pyrgeometer
28
UV A/B photometer
Ultraviolet radiation (Region B)
12
Pyrheliometer
29
Visibility sensor
Horizontal visibility
13 14
Relative humidity probe Rotating shadowband radiometer Rotating shadowband radiometer Rotating shadowband radiometer Sky camera
Upwelling infrared (longwave) radiation Photosynthetically Active Radiation (PAR) Diffuse Horizontal Irradiance (DHI) primary Global Horizontal Irradiance (GHI) primary Downwelling Infrared (Longwave) Irradiance (DIR) Direct Normal Irradiance (DNI) primary Relative humidity Diffuse Horizontal Irradiance (DHI) secondary Direct Normal Irradiance (DNI) computed Global Horizontal Irradiance (GHI) secondary Cloud fraction
Sky properties (including cloud amounts) PV module maximum power output (soiling) PV module short circuit current (soiling) Sun shape (circumsolar radiation) Direct normal spectral irradiance Global horizontal spectral irradiance Aerosol optical depth (AOD) Global Tilted Irradiance (GTI)
30 31
Wind vane Wind Vane
Wind direction @ 10 m Wind direction @ 3 m
32
Wind Vane
33
Wind Vane
Wind direction at peak speed @ 10 m Wind direction at peak speed @ 3m
15 16 17
Table 3 Table of Tiers 2 and 3 instruments. Source: Solar Monitoring Network Summary Report as of February, 2015. Tier 2 instruments
Tier 3 instruments
No.
Instrument type
Measurement
No.
Instrument type
Measurement
1 2 3
Air temperature probe Anemometer Barometer
Air temperature Wind speed @ 3 m Barometric pressure
1 2 3
Air temperature probe Barometer Pyranometer
4 5
Relative humidity probe Rotating shadowband radiometer Rotating shadowband radiometer Rotating shadowband radiometer Wind vane
Relative humidity Diffuse Horizontal Irradiance (DHI) Direct Normal Irradiance (DNI) computed Global Horizontal Irradiance (GHI) Wind direction @ 3 m
4 5
Relative humidity probe Rotating shadowband radiometer Rotating shadowband radiometer Rotating shadowband radiometer Tilted pyranometer
Air temperature Barometric pressure Global Horizontal Irradiance (GHI) T3 Relative humidity Diffuse Horizontal Irradiance (DHI) Direct Normal Irradiance (DNI) computed Global Horizontal Irradiance (GHI) T3 Global Tilted Irradiance (GTI) T3
6 7 8
6 7 8
Tier 3 micro-network comprises a cluster of approximately 8 of these instruments, around a single RSR, covering an area of 1–50 sq. km.
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Table 4 Table of Geographic coordinates of all monitoring sites and start of data collection. Source: Solar Monitoring Network Summary Report as of February, 2015. S. No.
List of monitoring sites
Tier
Latitude
Longitude
Installed date
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
Al Uyaynah Research Station King Abdullah University of Science and Technology University of Dammam King Abdulaziz University Main Campus Qassim University Jazan University King Faisal University Taif University Wadi Addawasir College of Technology Tabuk University Al Wajh Technical Institute Royal Commission of Yanbu Arar Technical Institute Al Jouf Technical Institute Rania Technical Institute Al Baha University Saline Water Conversion Corporation (Al Khafji) Najran University K.A.CARE City Site Tier 2 Station K.A.CARE Building Olaya St King Fahd University of Petroleum & Minerals King Abdulaziz University East Hada Alsham Campus Al Aflaaj Technical Institute Afif Technical Institute Al Dawadmi College of Technology Shaqra University Majmaah University Salman bin Abdulaziz University Timaa Technical Institute Saline Water Conversion Corporation (Hagl) Duba Technical Institute Al Hanakiyah Technical Institute Saline Water Conversion Corporation (Umluj) Saline Water Desalination Research Institute Sharurah Technical Institute Al Qunfudhah Technical Institute King Abdulaziz University (Osfan Campus) Hafar Al Batin Technical College Tuhamat Qahtan Technical Institute Saline Water Conversion Corporation (Farasan) King Saud University
1A 1A 1B 1B 1B 1B 1C 1C 1C 1C 1C 1C 1C 1C 1C 1C 1C 1C 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
24.90689 22.3065 26.39519 21.49604 26.34668 16.96035 25.34616 21.43278 20.43008 28.38287 26.2561 24.14434 30.918154 29.79383 21.21499 20.17933 28.50671 17.63228 24.52958 24.70814 26.30355 21.80117 22.27948 23.92118 24.5569 25.17279 25.85891 24.14717 27.61727 29.28997 27.34103 24.85577 25.00411 26.9042 17.47586 19.15197 21.89252 28.33202 17.7749 16.692097 24.72359
46.39721 39.10701 50.18898 39.24492 43.76645 42.54586 49.5956 40.49173 44.89433 36.48396 36.443 37.94569 41.079801 40.04886 42.84852 41.63561 48.45504 44.53735 46.43635 46.67896 50.14412 39.72854 46.73319 42.94815 44.47411 45.14198 45.41889 47.26999 38.5252 34.93002 35.72295 40.536 37.27382 49.76274 47.08618 41.08111 39.2539 45.95708 43.17555 42.098767 46.61639
January 13, 2013 June 01, 2013 May 26, 2013 May 28, 2013 June 02, 2013 November 01, 2014 May 28, 2013 June 04, 2013 July 31, 2013 September 24, 2013 September 25, 2013 December 01, 2014 December 01, 2014 December 01, 2014 December 01, 2014 December 01, 2014 December 01, 2014 December 12, 2013 January 08, 2013 January 15, 2013 May 22, 2013 June 04, 2013 June 10, 2013 July 15, 2013 July 16, 2013 July 16, 2013 July 18, 2013 July 22, 2013 July 23, 2013 July 25, 2013 July 25, 2013 August 27, 2013 August 29, 2013 September 01, 2013 September 03, 2013 September 04, 2013 September 15, 2013 October 06, 2013 November 18, 2013 September 01, 2014 November 01, 2014
Fig. 2. DNI comparison of RRMM local measurements with GeoModel at KACARE Headquarters Riyadh.
period is 426–514 W h/m2. In terms of percentages, the average difference between the measured monthly mean daily total radiation and the GeoModel estimates is about 6.9% of the reported values, the average measurement
Fig. 3. GHI comparison of RRMM local measurements with GeoModel at KACARE Headquarters Riyadh.
uncertainty being 8.4%. In most months, the observed differences between measured and modeled data were within the combined estimated uncertainty as shown in Table 7.
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A statistical analysis was carried out to determine the significance of the differences between the 12 month means of the GeoModel (m1) and RRMM measured (m2) data. The Null Hypothesis (Ho) is satisfied when m1 = m2, and the alternate hypothesis (Ha) is satisfies when m1 – m2. The statistical analysis of the data revealed a p-value of 0.191, indicating that at significance level a = 0.20 there is a statistically significant difference between the means of the two sample datasets, whereas at lower values of a we fail to reject the null hypothesis. Fig. 3 compares the measured values of GHI by RRMM for the year 2014 with the GeoModel (Renewable Resource Atlas, 2015) at KACARE Headquarters Riyadh. For the GeoModel the average values for the period 1994–2012 are reported. A maximum difference of 693 W h/m2 is seen in April and a minimum difference of 37 W h/m2 is observed in September. The reported value of uncertainty in measurements for RRMM for this data being in the range of 270–480 W h/m2. A fairly good agreement can be seen between the RRMM values and GeoModel with the average difference between the measured monthly mean daily total radiation and the GeoModel estimates being about 5.6% of the reported values, the average measurement uncertainty being 6.7%. In all months, the observed differences between measured and modeled data were within the combined estimated uncertainty as shown in Table 7. The statistical analysis of the data revealed a p-value of 0.520, indicating that at significance level a = 0.53 there is a statistically significant difference between the means of the two sample datasets, whereas at lower values of a we fail to reject the null hypothesis. It is worthwhile to mention that there can be significant inter-annual variabilities in the measured data. Though the current RRMM atlas is relatively new to measure the interannual variabilities, elsewhere the inter-annual variabilities reported (Va´zquez et al., 2011) can range between ±8% for DNI and ±5% for GHI. Whereas another report gives the inter-annual coefficient of variance for DNI to range between 1% and 10% (Wilcox and Gueymard, 2007).
Fig. 4. Rotating shadowband radiometer at Qassim University.
Fig. 5. Sun tracker at Qassim University.
3.3. Qassim University solar resource monitoring station Due to the authors’ affiliation with QU, the university’s resource monitoring station is discussed in detail here. Qassim Province is located in the center of Saudi Arabia with an area of 58,046 km2 and a population of 1.2 million (AlQassim Province, 2014). Its capital, Buraidah, is located at latitude 25.8063°N and longitude 42.8732°E. As a part of KACARE’s regional partnership program with universities, KACARE has installed solar monitoring equipment at Qassim University, including a rotating shadowband radiometer (Fig. 4), spectroradiometer, and sun tracker (Fig. 5). The sun tracker has thermopile-based radiometers for independent measurements of GHI (measured by unshaded pyranometer), DNI (measured by pyrheliometer), and DHI (measured shaded pyranometer). Table 5 presents the Qassim University station summary (Station Summary Report, 2014). Table 6 lists the various equipment installed at the station. Fig. 6 shows the average DNI measurement at QU for the year 2014. As seen in the figure, the DNI varied between a maximum of 7900 W h/m2/day and a minimum of 4700 W h/m2/day. A maximum difference of 1625 W h/m2 is seen in the month of February and a minimum difference of 21 W h/m2 is observed in month of January. In terms of percentages the average difference between the measured monthly mean daily total radiation and the GeoModel estimates is about 5.4% of the reported values, the average measurement uncertainty being 5.1%. In most months, the observed differences between measured and modeled data were within the combined estimated uncertainty, as shown in Table 7. However, the maximum difference can vary between 35% (RRMM data lower than GeoModel data) and +24% (RRMM data higher than GeoModel data). The statistical analysis of the data revealed a p-value of 0.30, indicating that at significance level a = 0.31 there is a statistically significant difference between the means of the two sample datasets, whereas at lower values of a we fail to reject the null hypothesis.
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The differences observed in Figs. 2, 3 and 6 can occur due to many possible sources (Sˇu´ri and Cebecauer, 2015) including:
(v) Difficulties of modeling surface irradiances in complex terrain, etc. (vi) Issues in ground measurements.
(i) Inconsistent periods of record: The comparison is made between satellite based GeoModel (1994– 2002) and ground based measurements by RRMM for the year 2014. (ii) Inter-annual variability: For the ground based measurements there will be inter-annual variability which has not been taken into account in the current analysis. (iii) Limitations of GeoModel input atmospheric data (Aerosol Optical Depth, Water Vapor, Ozone, etc.) (iv) Accuracy of Environmental Variables (Altitude, Terrain shading, Temperature)
While comparing the ground based measurements with the satellite based estimates, it is pertinent to mention the combined uncertainty. The combined uncertainty (Ucombined) is defined by (Suri and Cebecauer, 2014): qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi U combined ¼ U 2model þ U 2measurements
Table 5 Qassim University station summary information. Station name City Latitude (N) Longitude (E) Station type and measurements
Data transmittal Station power source Operating since Station elevation Calibration schedule Maintenance schedule
Qassim University Solar Monitoring Station Qassim 26.34668° 43.76645° Research (Tier 1B) station containing all broadband solar radiometers, selected solar spectral radiometers and photometers, pyrgeometers, and basic meteorological instruments, plus instruments measuring dust deposition and horizontal visibility Internet connection Electric grid June 2, 2013 698 m As per manufacturer specifications Daily
The uncertainty of GeoModel has been mentioned earlier (Suri and Cebecauer, 2015). The uncertainty of measurements includes the uncertainty of instruments and the uncertainty due to interannual variability of data. In this research the interannual variability of data has not been included due to the nascent data being for only two years. Table 7 lists the summary of combined estimated uncertainties. The first column lists the reported uncertainty of RRMM measured data, for the month where maximum
Fig. 6. Average DNI measured at Qassim University station and comparison with historical GeoModel data.
Table 6 Qassim University solar resource monitoring station equipment and measurements (1-min resolution). Equipment
Measurement
Nominal uncertainty
Anemometer Barometer Rotating shadowband radiometer Rotating shadowband radiometer Rotating shadowband radiometer Rotating shadowband radiometer Temp./relative humidity (RH) probe Wind vane
Wind speed @ 3 m (Tier 2 or Tier 1 roof sites) Barometric pressure Diffuse horizontal irradiance (DHI) DNI computed GHI primary GHI secondary Air temperature and RH Wind direction at 3 m (Tier 2 or Tier 1 roof)
±1.1% ±1.5 mb ±4% ±4% ±4% ±4% ±0.6 °C for temperature, ±3% to ±7% for RH ±4°
Table 7 Summary of combined uncertainty.
KACARE headquarters DNI KACARE headquarters GHI Qassim University DNI a
RRMMa uncertainty (± %)
GeoModel uncertainty (± %)
Combined uncertainty (± %)
Maximum observed difference (%)
8.13 6.5 5.5
15 8 15
17.06 10.31 15.98
21 10.8 35
RRMM Uncertainty reported for the particular month where maximum difference is observed.
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difference is observed. The second column lists the GeoModel uncertainty as mentioned earlier in this paper. The third column is the combined estimated uncertainty. The last column lists the maximum observed difference between the values of RRMM measurements and GeoModel estimates, for a particular month. It can be seen that the maximum difference is greater than the combined estimated uncertainty. The above work presents and initial overview of the RRMM solar map with comparisons between ground based measurement for the year 2014 and historical GeoModel. For future work, continued solar radiation measurements will provide a basis for estimating the inter-annual variability of solar resources at Qassim University, and Spectral irradiance measurements will strengthen the ability to provide accurate inputs of aerosol optical depth and other parameters needed by the satellite-based models. 4. Conclusions The current paper is one of the first academic reports of the recently launched Solar Atlas of Saudi Arabia. The atlas, established in February 2014, presents publically accessible data of temperature, DNI, and GHI from 41 recording and monitoring stations spread across the country. The solar maps from GeoModel show DNI averaging 9000 W h/m2/day in the summer months in the northern areas, 6000–6500 W h/m2/day in the central belt, and 6000–6500 W h/m2/day in the eastern region. In the winter months, DNI averaging 8000 W h/m2/day was observed in the northern and southern areas, and DNI of 7000– 7500 W h/m2/day was measured in the central belt. The northeastern Arar region and southwestern Jeddah region showed a DNI in the range of 4500–5000 W h/m2/day. The data comparison presented in this paper are based on long-term estimates by GeoModel (average of data from 1994 to 2012), and the measurements from 2014 at KACARE Headquarters Riyadh and QU using the RSR2. The DNI measured at KACARE Headquarters Riyadh showed an average difference of 442 W h/m2 with the GeoModel, while the range of error in RRMM measurements for this period is 426–514 W h/m2. In terms of percentages, the average difference is about 6.9% of the reported values, which is well within the combined uncertainty. A maximum difference in of 693 W h/m2 in GHI is seen in April and a minimum difference of 37 W h/m2 is observed in September, while uncertainty in measurements for RRMM for this data being in the range of 270– 480 W h/m2. A fairly good agreement can be seen between the RRMM values and GeoModel, for GHI, with the average difference being about 5.6%, which is well within the combined uncertainty. It might be noted that the GeoModel has data uncertainties in the range of ±8% to ±15% for DNI, and ±4% to ±8% for GHI. The measured values of DNI at QU varied from a maximum of 8367 W h/m2/day in July to a minimum of
4702 W h/m2/day in January. The comparison with GeoModel showed a maximum difference of 1625 W h/m2 in the month of February and a minimum difference of 21 W h/m2 in month of January. The uncertainty in measurements for this data being in the range of 200–557 W h/m2. In terms of percentages, the average difference is about 5.4% of the reported values, the average measurement uncertainty being 5.1%. Looking at the combined estimated uncertainty of RRMM measurements and GeoModel, the maximum observed difference between the two values lies mostly within the combined estimated uncertainty. With improved model inputs of aerosol optical depth, total precipitable water vapor, ozone, etc., based on RRMM measurements, perhaps the model performance can be improved and the estimated uncertainties reduced. It is hoped the results of this study will provide the evidence needed to establish a degree of confidence in the solar resource information available from the Solar Atlas and become instrumental in establishing a solar PV or concentrated solar power (CSP) facility in any geographical area of the country. The results from Solar Atlas are critical in guiding policies and to ascertain feasibility of installing solar plants for the country. As future work, further studies can be carried out for comparisons of data from Qassim University and K.A. CARE HQ in downtown Riyadh; RSR2 vs Kipp & Zonen instruments at QU; Photometric and/or spectral measurements at QU station. Acknowledgments The authors acknowledge Solar Atlas of Saudi Arabia, prepared by KACARE (http://rratlas.kacare.gov.sa). The access provided was greatly helpful in the preparation of solar maps and downloading the data for this paper. The access is also helpful for teaching and research. References Al Ghabban, A., 2013. Presentation, KACARE, Saudi Arabia’s renewable energy strategy and solar energy deployment roadmap. IRENA Lecture Program, 26 March 2013. Al-Qassim Province, 2014. https://en.wikipedia.org (accessed 28.04.14). Alnaser, W.E., Eliagoubi, B., Al-Kalak, A., Trabelsi, H., Al-Maalej, M., El-Sayed, H.M., Alloush, M., 2004. First solar radiation atlas for the Arab world. Renew. Energy 29, 1085–1107. Beyer, H.G., Czeplak, G., Terzenbach, U., Wald, L., 1997. Assessment of the method used to construct clearness index maps for the New European Solar Radiation Atlas (ESRA). Sol. Energy 61 (6), 389–397. Blanksby, C., Bennett, D., Langford, S., 2013. Improvement to an existing satellite data set in support of an Australia Solar Atlas. Sol. Energy 98, 111–124. EIA, Energy Information Administration, USA, 2013.
S. AlYahya, M.A. Irfan / Solar Energy 130 (2016) 116–127 Huraib, F.S., Hasnain, S.M., Alawaji, S.E., 1996. Lessons learned from solar energy projects in Saudi Arabia, WREC. WREC-IV World Renewable Energy Congress No. 4, Denver, Colorado, ETATS-UNIS (15/06/1996), 1996, vol. 9, No. 1–4, pp. 1144–1147. Janjai, S., Masiri, I., Laksanaboonsong, J., 2013. Satellite-derived solar resource maps for Myanmar. Renew. Energy 53, 132–140. King Abdulaziz City of Science and Technology, 2015.
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Station Summary Report, 2014. Qassim University Solar Resource Monitoring Station, prepared by KACARE – RRMM program, February 2014.