MODELLING TOPOGRAPHIC VARIATION OF SOLAR RADIATION USING GIS

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1 MODELLING TOPOGRAPHIC VARIATION OF SOLAR RADIATION USING GIS Muhammad Ahsan Mahboob Institute of Geographical Information Systems National University of Sciences and Technology, NUST Campus, H-12, Islamabad, Pakistan Iqra Atif Institute of Geographical Information Systems National University of Sciences and Technology, NUST Campus, H-12, Islamabad, Pakistan Javed Iqbal Institute of Geographical Information Systems National University of Sciences and Technology, NUST Campus, H-12, Islamabad, Pakistan Abstract Renewable energy is clean sources and has a much lower environmental impact than other energy sources and solar energy is one such renewable energy. The Estimation of solar energy potential of a region requires detailed solar radiation climatology, and it is necessary to collect extensive radiation data of high accuracy covering all climatic zones of the region. The study area National University of Sciences and Technology is the one of the top ranked universities of Pakistan and has some limitations on the number of synoptic stations. Hence, the aim of this study is to determine the theoretical solar radiation potential in NUST by using Area Solar Radiation (ASR) model based on topography and geographical information systems. The ASR model was successfully applied with a core of practical methodology. The results show that NUST has very good solar potential with value of KWH/m 2 and KWH/m 2 as maximum and minimum respectively. The Area Solar Radiation model can also be used to calculate the insolation across an entire landscape. The ASR model is very efficient and time saving which offers means of entering, accessing, and interpreting the information for the purpose of sound decision making. Keywords Solar energy; Solar map; GIS-based approach; Potential assessment I. Introduction As per energy demand is increasing all over the world, renewable energy resources such as solar and wind has turned out to be of great importance [1]. At present, there are several renewable energy uses in advanced and emerging countries and renewable energy resources are likely to be converting into a major element of the total energy sources in the future [2]. Renewable energy methods are ecologically friendly related to conventional energy methods. They do not create any physical contamination especially greenhouse gases [3]. They inputs for renewable energy are plentiful in nature [4-5]. Solar energy is one of the best renewable energy sources, with the minimum adverse effects on the environment [6]. Incoming solar radiation (insolation), with a continual input of 170 billion megawatts to the earth, is the primary driver for our planet's physical and biological processes [7-10]. Almost all human activities (agriculture, forestry, building design, and land management) ultimately depend upon insolation. At a global scale, the latitudinal gradients of insolation, caused by the geometry of Earth's rotation and revolution about the sun, are well known. At the landscape scale, topography is the major factor which modifies the distribution of insolation. Variability in elevation, surface orientation (slope and aspect), and shadows cast by topographic features create strong local gradients of insolation. This leads to high spatial and temporal heterogeneity in local energy and water balance, which determines micro-environmental factors such as air and soil temperature regimes, evapotranspiration, snow melt patterns, soil moisture, and light available for photosynthesis. These factors in turn affect the spatial modeling of natural processes and human endeavor. Assessment of the solar energy potential of a metropolitan through the solar mapping is an analytical method that allows determination of the value of local capacity for energy production and uses these results to design and implement energy strategies and urban planning, in line with the aims and objectives of sustainable development. Modeling urban environments can be a difficult task because of the limited reliability of 3D city models, and constitutes one of the active research topics in Geography. Several techniques have been developed for accurate information of roof top area estimation some commonly used techniques in this context are image matching algorithms, image segmentation or integration of different data sources [12-14]. Moreover, the solar maps should be updated regularly and therefore used for monitoring effects of policy and planning. The planning for energy investments on solar systems requires information on 1) the electric power demand, and 2) on the generation capabilities. As far as the generation capabilities of the solar systems, it is essential to highlight that to implement solar technologies; detailed solar suitability information on every building in a community should be available for urban

2 planners [11]. Recognizing buildings that are suitable for solar panel installation requires 1) modeling the urban environment, 2) the available area at the rooftops for panels installation and, 3) the solar irradiation. Accurate insolation maps at landscape scales are desired for many applications. Although there are thousands of solar radiation monitoring locations throughout the world (many associated with weather stations), for most geographical areas accurate insolation data are not available. Simple interpolation and extrapolation of point-specific measurements to areas are generally not meaningful because most locations are affected by strong local variation. Accurate maps of insolation would require a dense collection station network, which is not feasible because of high cost. Spatial solar radiation models provide a cost-efficient means for understanding the spatial and temporal variation of insolation over landscape scales [9-10]. Such models are best made available within a geographic information system (GIS) platform, whereby insolation maps can be conveniently generated and related to other digital map layers. In this research study we proposed a geographical information system based Area Solar Radiation (ASR) model which has been applied to estimate the solar insolation in the study area. II. Data And Methodology A. Study area National University of Sciences and Technology (NUST) was established in March 1991 for the promotion of higher scientific education in the country with geographic location of 33º N and 72º E. On the North-East side of study area there is highlands and on South and South-East side academic blocks as well as residential area is located as shown in Fig. 1. surface orientation and visible sky. The local effect of topography is accounted for by empirical relations [15-17], by visual estimation [18-19], or, more accurately, by the aid of upward-looking hemispherical (fisheye) photographs [20-22]. Point-specific models can be highly accurate for a given location, but it is not feasible to build a specific model for each location over a landscape. In contrast, area-based models compute insolation for a geographical area, calculating surface orientation and shadow effects from a digital elevation model (DEM) [23-25]. These models provide important tools for understanding landscape processes. The SolarFlux model [23-24, 27], developed for use within the ARC/INFO GIS platform (Environmental Systems Research Institute [Esri], Redlands, CA), simulates the influence of shadow patterns on direct insolation using the ARC/INFO Hillshade function at discrete intervals through time. Solarflux was implemented in the Arc Macro Language (AML), which strongly limits its computation speed and its accessibility. Kumar et al. in 1997 [28] developed a similar model using ARC/INFO and the GENAMAP GIS software (GENASIS, Australia). Whereas point-specific models can be highly accurate for a specific location, area-based models can calculate insolation for every location over a landscape. A new generation of spatial models is needed that combines these respective advantages, providing rapid and accurate maps of insolation over landscape scales. C. Area Solar Radiation (ASR) Model The Area Solar Radiation (ASR) model is Esri ArcGIS model which calculate insolation across a landscape or for specific locations, based on methods from the hemispherical viewshed algorithm developed by Rich et al. [21-22] and further developed by Fu and Rich [27-28]. The total amount of radiation calculated for a particular location or area is given as global radiation. The calculation of direct, diffuse, and global insolation are repeated for each feature location or every location on the topographic surface, producing insolation maps for an entire geographic area. D. Solar Radiation Equations Global radiation (Global tot ) is calculated as the sum of direct (Dir tot ) and diffuse (Dif tot ) radiation of all sun map and sky map sectors, respectively as shown in equation (1) Global tot = Dir tot + Dif tot (1) Fig. 1. Study area National University of Sciences and Technology. B. Spatial Solar Radiation Models Spatial insolation models can be categorized into two types: point specific and area based. Point-specific models compute insolation for a location based upon the geometry of Total direct insolation (Dir tot ) for a given location is the sum of the direct insolation (Dir θ,α ) from all sun map sectors as in equation (2). Dir tot = Σ Dir θ,α (2) The direct insolation from the sun map sector (Dir θ,α ) with a centroid at zenith angle (θ) and azimuth angle (α) is calculated using the following equation (3) Dir θ,α = S Const β m(θ) SunDur θ,α SunGap θ,α cos(angin θ,α ) (3) Where:

3 S Const is the solar flux outside the atmosphere at the mean earth-sun distance, known as solar constant. The solar constant used in the analysis is 1367 W/m 2. This is consistent with the World Radiation Center (WRC) solar constant. β is the transmissivity of the atmosphere (averaged over all wavelengths) for the shortest path (in the direction of the zenith). m(θ) is the relative optical path length, measured as a proportion relative to the zenith path length (see equation 4 below). SunDur θ,α is the time duration represented by the sky sector. For most sectors, it is equal to the day interval (for example, a month) multiplied by the hour interval (for example, a half hour). For partial sectors (near the horizon), the duration is calculated using spherical geometry. SunGap θ,α is the gap fraction for the sun map sector. AngIn θ,α is the angle of incidence between the centroid of the sky sector and the axis normal to the surface (see equation 5 below). Relative optical length, m(θ), is determined by the solar zenith angle and elevation above sea level. For zenith angles less than 80, it can be calculated using the following equation (4): m(θ) = EXP( Elev Elev 2 ) / cos(θ) (4) Where: θ is the solar zenith angle. Elev is the elevation above sea level in meters. The effect of surface orientation is taken into account by multiplying by the cosine of the angle of incidence. Angle of incidence (AngInSky θ,α ) between the intercepting surface and a given sky sector with a centroid at zenith angle and azimuth angle is calculated using the following equation (5): AngIn θ,α = acos(cos(θ) Cos(G z )+Sin(θ) Sin(G z ) Cos(α- G a )) (5) Where: G z is the surface zenith angle (zenith angles greater than 80, refraction is important). G a The surface azimuth angle. E. Digital Elevation Model Digital elevation model (DEM) is the basic input as elevation surface raster to Area Solar Radiation model. DEM is basically the representation of continuous elevation values over a topographic surface by a regular array of z-values, referenced to a common datum. DEMs are typically used to represent terrain relief. A large number of DEMs derived from contour maps or satellite images are currently available and widely used for different terrain analyses and other related tasks. The most popular DEMs are those from the Shuttle Radar Topography Mission (SRTM) with resolutions of 90 and 30 m. The SRTM-90 data, also known as three arc-second DEMs, are free for all covered territories. The SRTM-30 data are free only for North America. The ASTER GDEM, which is maintained by NASA and METI, is also very popular and provides nearly worldwide elevation models with a 30 m ground resolution. In this research we have also used the 30 m resolution DEM as shown in figure 2. Fig. 2. Digital Elevation Model of NUST. The advantage of this elevation model is that it combines the visual sensors of ASTER and the radar sensor of SRTM to produce a reliable surface. The visual sensor improves the representation of high-energy areas that often have errors in purely radar-based elevation models. F. Latitude Latitude is the angular distance of a place north or south of the earth's equator, usually expressed in degrees and minutes. Along with the earth s tilt latitude also has great significance in estimation of insolation to a particular region of earth. Due to the angle and revolution around the sun, different latitudes are exposed differently to solar energy. The equator has sunlight more often than higher latitudes. NUST lies at the latitude of 33º. The mean annual solar energy distribution map of the world with effect of different latitudes is shown in figure 3. Fig. 3. Average annual solar energy distribution map (Source: INFORSE - International Network for Sustainable Energy)

4 G. Input parameters of Area Solar Radiation (ASR) Model The input parameters of ASR model as shown in figure 4 is as under: 1) Input elevation surface raster 2) The latitude for the site area. The units are decimal degrees, with positive values for the northern hemisphere and negative for the southern. For input surface raster containing a spatial reference, the mean latitude is automatically calculated; otherwise, latitude will default to 45 degrees. 3) The resolution or sky size for the viewshed, sky map, and sun map grids. The units are cells. The default creates a raster of 200 by 200 cells. 4) Specifies the time configuration (period) used for calculating solar radiation. The Time class objects are used to specify the time configuration. The different types of time configurations available are: a. Time Within Day b. Time Multi Days c. Time Special Days and d. Time Whole Year 5) The time interval through the year (units: days) used for calculation of sky sectors for the sun map. The default value is 14 (biweekly). 6) Time interval through the day (units: hours) used for calculation of sky sectors for sun maps. The default value is ) The number of ground x,y units in one surface z unit. The z-factor adjusts the units of measure for the z units when they are different from the x,y units of the input surface. The z-values of the input surface are multiplied by the z-factor when calculating the final output surface. If the x,y units and z units are in the same units of measure, the z-factor is 1. This is the default. If the x,y units and z units are in different units of measure, the z-factor must be set to the appropriate factor, or the results will be incorrect. 8) The slope raster of the study area in degrees or percentage. 9) The aspect raster of the study area with eight classical convention system along with flat regions. 10) Type of diffuse radiation model. i.e. Uniform Sky or Standard Overcast Sky 11) The proportion of global normal radiation flux that is diffuse. Values range from 0 to 1. This value should be set according to atmospheric conditions. The default value is 0.3 for generally clear sky conditions. 12) The fraction of radiation that passes through the atmosphere (averaged over all wavelengths). Values range from 0 (no transmission) to 1 (all transmission). The default is 0.5 for a generally clear sky. Fig. 4. Area Solar Radiation (ASR) Model input parameters. III. Results and Discussions A. Slope Slope is the calculation of the maximum rate of change in value from that cell to its neighbors. Basically, the maximum change in elevation over the distance between the cell and its eight neighbors identifies the steepest downhill descent from the cell. In the study area, NUST the slope in degrees vary from 0 to 9 as minimum and maximum respectively. The figure 5 represents the spatial distribution of the slope in the study area. Fig. 5. Map of Spatial Distribution of Slope. As shown in map the major portion of the slope range from 5.3 to 9 degrees and is situated East-West direction. There is smaller portion of the same slope value situated on North of the study area.

5 B. Aspect Aspect identifies the downslope direction of the maximum rate of change in value from each cell to its neighbors. It can be thought of as the slope direction. The values of each cell in the output raster indicate the compass direction that the surface faces at that location. It is measured clockwise in degrees from 0 (due north) to 360 (again due north), coming full circle. Flat areas having no downslope direction are given a value of -1. The aspect map of the study area is shown in the figure 6. Fig. 8. Incoming Solar Radiation (Insolation) Map of NUST. Fig. 6. Map of Different Aspects of the Study Area. C. Solar Potential at NUST Area Solar Radiation (ASR) Model was applied to calculate the annual incoming solar radiation (insolation) for the study area i.e. NUST. Solar radiation for NUST is calculated by summing the above direct and diffuse insolation originating from the unobstructed sky directions. In addition to standard parameters, such as the digital elevation model with resolution of 30m, slope and aspect was also derived from it. The graph (Figure 8) shows the eight years clear sky average ( ) insolation at Pakistan Meteorological Observatory Islamabad. Fig. 7. Clear Sky Average Insolation at Islamabad ( ). The graph shows that the maximum insolation is in the month of March i.e. 10 KWhm 2 and minimum in the month of February and December with average value of 4 KWhm 2. The map of incoming solar radiation (insolation) calculated through Area Solar Radiation (ASR) model is shown in figure 8. The map shows that the maximum insolation in the study area is 15.5 KWhm 2 and the lowest value of insolation is 12.8 KWhm 2. The region with high insolation capability is mostly the region of North and North-West aspect with a slope of degrees. The area is found to be very suitable for installation of PV solar systems as this is mostly highland area. The Area Solar Radiation (ASR) Model does not currently model clouds, per se, although clouds can be taken into account when estimating transmittivity and diffuse proportion. Clouds are extremely hard to model or predict, and detailed information, such as clouds distribution, thickness, cloud type, are not available for most areas. If such data are available, the Solar Analyst could readily be customized by adding a cloud skymap that can be overlaid with the viewshed, skymap, and sunmap. IV. Conclusion This paper presented a study on solar potential mapping in NUST using GIS-based solar radiation model known as Area Solar Radiation (ASR). We implemented a fast and effective model that permits accurate calculation of topographic influences on solar radiation over local and landscape scales. The results obtained showed very high potentials of solar energy and solar electricity generation on most of the land of NUST during the whole year. The developed GIS solar radiation maps will have the capability to communicate with other models such as feasibility and cost analyses, combination of solar energy with other sources of energies (hybrid applications). The Area Solar Radiation (ASR) model serves as a powerful tool for analyzing spatial and temporal patterns of insolation at local and landscape scales. Applications span a broad range of fields, including forestry, agriculture, hydrology, micrometerology, environmental assessment, and ecological research. The model also promises to be useful in engineering and design fields, for such applications such as site assessment, building design, solar collector design, and topographic radiometric correction for remote sensing. More complete 3-D city models can be also developed which include the effects of trees, city infrastructure, socio-

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