INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 ISSN 0976-6480 (Print) ISSN 0976-6499 (Online) Volume 5, Issue 10, October (2014), pp. 157-164 IAEME: www.iaeme.com/ IJARET.asp Journal Impact Factor (2014): 7.8273 (Calculated by GISI) www.jifactor.com IJARET I A E M E ASSESSMENT OF SOLAR ENERGY DISTRIBUTION FOR INSTALLING SOLAR PANELS USING REMOTE SENSING & GIS TECHNIQUES Hameed Majeed Saber*, Deepak Lal** *Ministry of Electricity-Directorate General for the production electrical energy northern region- Salahuddin-Mullah Abdullah power plant gas- Republic of Iraq **Shiats Allahabad, INDIA ABSTRACT Many cities across the world are encouraging the use of solar energy technologies in promoting the concept of sustainable cities. The active and passive applications of solar energy could effectively transform neighborhoods, commercial districts, and urban areas into small, localized power plants. The current study was conducted in Sam Higginbottom Institute of Agriculture, Technology & Sciences, Allahabad, India to assess the potential of installing solar panels on the roof tops of its various buildings. Monthly and yearly accumulated solar radiation maps were generated for the study area using Solar Analyst module in ArcGIS 9.3. The monthly and yearly accumulated roof top solar radiation for each building under consideration was extracted from the respective solar radiation maps. The buildings were ranked according to the amount of potential solar radiation they received. Keywords: GPS. ARC GIS SOFTWARE. INTRODUCTION The overall demand for renewable energy assimilation to the power grids highlights the significance of economic and technological issues connected with growing levels of flat-panel photo voltaic, concentrated solar power, penetrations into the power grid. These concerns arise from the variable nature of the solar resource, seasonal deviations in production and load profiles, the high cost of energy storage, and the balance between grid flexibility and reliability [1, 2]. Because of this, solar plants are often backed by ancillary generators for periods of high unpredictability, which 157
increases the capital and operational costs of solar generation. Precise solar forecasts over several time horizons are required so that independent system operators or equivalent grid balancing authorities are able to successfully integrate increased levels of solar power production while maintaining reliability [3-5]. Solar forecasts on multiple time horizons become increasingly important as solar penetration grows for the purposes of grid regulation, load-following production, and power scheduling along with unit commitment. Short-term, intra-hour solar forecasts are particularly useful for power plant operations, grid balancing, real-time unit dispatching, automatic generation control and trading [6-8]. Forecasts for longer time horizons are of interest to utilities for unit commitment, scheduling and for improving balance area control performance. In this regard, a spectrum of solar forecasts is required to address the planning. In satellite-based remote sensing, sensors acquire data on the way various earth surface features emit and reflect electromagnetic energy and these data are analyzed to provide information about the resources under investigation. Clear water absorbs relatively little energy having wavelengths less than about 0.6 mm. high transmittance typifies these wavelengths with a maximum in the blue-green portion of the spectrum and provides clearly the contrast between land and water features and therefore is best suited for identification and mapping perennial streams. All forms of remotely sensed images are nonselective in nature and cannot be directly integrated into applications. An interpretative process is necessary before useful thematic information relating to environment can be extracted from these images. Thu s, the process of visual interpretation of wide variety of remotely sensed data is a complex intuitive process of combining evidential information from different sources and subjecting such information to an expert s knowledge, experience and heuristics at each levels namely detection, identification, analysis, recognition and classification of the process [9-11]. It calls for the analysis of a number of related information by a domain expert. An expert system shown in Figure 1.1 is developed with computer-based program in ArcGIS that uses knowledge, facts and different reasoning techniques to solve problems. The associated information and logical reasoning that are used by a well-trained human interpreter are encoded in the form of rules and facts. Locating potential sites for PV panels should also take into account other factors based on the scale and types of installation involved. More specifically, for rooftop solar panel installation, shadow effects due to surrounding obstacles and roof structure are important factors to consider. Regarding ground-mounted solar panel installations, environmental and economic concerns, and energy generation potential should be taken into account during the site selection process [12-15]. For example, installation sites should be located away from forest areas and environmental sensitive land. Proximity to existing transmission facilities is desirable for reducing transmission loss. In some regions with abundant dust, effects of dust accumulation on the performance of solar panels are important. In order to consider these factors and to identify potential sites for solar panel installation, a Multicriteria Analysis (MCA) is undertaken in this study. This work will examine the potential benefits of using spatial analytical techniques for identifying optimal sites for roof-mounted solar panels at the micro-scale level and ground-mounted solar panels at the macro-scale level OBJECTIVE OF THE PRESENT WORK The overall objective of this project is to evaluate the amount solar radiation received at the roof tops of various building in Sam Higginbottom Institute of Agriculture, Technology & Sciences (SHIATS) by combining GIS and remote sensing techniques. The accumulated solar radiation for each month over various buildings of SHIATS are analyzed in this study. Additionally, based on total accumulated annual solar radiation, the various building of SHIATS are ranked according to their potential for installing solar panel. 158
The specific objectives of the current study are as follows: 1- To study the spatial distribution and amount of solar radiation under clear-sky conditions for the various buildings in SHIATS. 2- To determine suitable buildings based on the amount of solar radiation received for installing the solar panels. Study Area The current analyses was based on solar radiation accumulated from the roof tops of the various building of SHIATS (Figure 3.1). The buildings considered in the present study are listed in table 3.1. Figure 3.1: Buildings Within The Study Area Data Utilized The primary data used in the present study was a Cartosat-1 Digital Elevation Model (CartoDEM). CartoDEM is a National DEM developed by the Indian Space Research Organization (ISRO). It is derived from the Cartosat-1 stereo payload launched in May. METHODOLOGY OVERVIEW The amount of incoming solar irradiance can be estimated using a variety of techniques. For example, solar radiation from dispersed observation points can be interpolated to generate a 159
International Journal of Advancedd Research in Engineering and Technology (IJARET), ISSN 0976 continuous solar map by geo statistical techniques that estimate solar radiation levels via interpolation methods. Multivariate statistical methods can also estimate solar radiation by accounting for multiple variables affecting the radiation in artificial intelligence models. For the application of geostationary satellite imagery, meteorological factors from specific bands, such as visible and infrared bands, can be used to estimate the radiation under various sky conditions. Data with land surface elevation information are also commonly used, while considering shadow effects and obstacles. Incoming solar radiation amounts, considering shadow effects, provide the main indicator of solar energy supply or inputs in the present study. The output products at both micro- and macrocould be subsequently scales of the study will be accumulated with the help of radiation maps that used for site selection for solar panel installation. In the present study the Solar Analyst module in ArcGIS 9.3 was to calculate the amount of solar radiation received at the rooftops of the SHIATS's buildings. A survey was conducted to gather the location, shape and size information of each building. A GPS was used to acquire the latitude & longitude information for each building considered in the present study. The latitude & longitudes were imported within the ArcGIS environment. Based on the latitude & longitude information, the roof tops for each building were digitized within ArcGIS Figure 3.2: Flow chart of the Methodology Followed in the Present Study SOLAR RADIATION MODEL The solar radiation analysis tools in ArcGIS calculate incoming solar radiation or insolation across a landscape or for specific locations, based on methods from the hemispherical view shed algorithm developed by Rich et al. (Rich 1990, Rich et al. 1994), as furtherr developed by Fu and Rich(2000, 2002). 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 160
geographic area. The equations used in the solar radiation model are described below (ArcGIS documentation). Global Radiation Calculation Global radiation (Globaltot) is calculated as the sum of direct (Dirtot) and diffuse (Diftot) radiation of all sun map and sky map sectors, respectively. Global tot = Dir tot + Dif tot (3.1) Direct Radiation Calculation Total direct insolation (Dirtot) for a given location is the sum of the direct insolation (Dirθ,α) from all sunmap sectors: Dir tot = ΣDir θ,α (3.2) The direct insolation from the sun map sector (Dirθ,α) with a centroid at zenith angle (θ) and azimuth angle (α) is calculated using the following equation: Where: Dirθ,α = SConst * βm(θ)* SunDurθ,α * SunGapθ,α * cos(anginθ,α) (3.3) Sconst 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 WM-2. This is consistent with the World Radiation Center (WRC) solar constant. β is transmisivity 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 3 below). SunDurθ,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; SunGapθ,α is the gap fraction for the sunmap sector; AngInθ,α is the angle of incidence between the centroid of the sky sector and the axis normal to the surface (see equation 4 below). Relative optical length (m(θ)) is determined by the solar zenith angle and elevation above sea level. For zenith angles less than 80o, it can be calculated using the following equation: RESULTS AND DISCUSSION m(θ) = EXP(-0. 000118 * Elev - 1. 638 * 10-9 * Elev2) /cos(θ) (3.4) The availability of incoming solar radiation was the primary considerations when assessing potential solar panel installation sites on SHIATS's buildings' rooftops. The solar radiation 161
calculation was implemented by the area solar radiation tool in ArcGIS software associated with elevation data as the primary input. The accuracy and quality of collected data were not investigated in this study. The data have known geometric errors due to systematic and random errors from the instrument during the collection phase. Geometric calibration could not be performed without available ground control points for reference. The elevation and geo referencing of buildings may not be very accurate, which may result in errors in the solar radiation estimations. Fortunately, information about rooftop structures could still be achieved from recorded elevation information, which allows for reliable assessment of spatial patterns of incoming solar radiation. In other words, although the absolute heights of buildings were not recorded correctly during the data collection phase, the relative comparisons of the buildings roofs were correct. Based on these heights, roof structures can be easily recognized and analyzed. All of the analyses at the micro-scale level were based on solar radiation maps deduced from data. As previously described, solar radiation availability and shading effects are two main factors for determining optimal sites in the micro-scale building site assessment, while shading effects were taken into account during the solar radiation calculation. However, incoming radiation estimates may not be accurate, since the estimates are highly dependent on weather conditions, while in this study, only clear sky conditions were considered. As a result, potential energy production was estimated based on an ideal circumstance, and actually results would be less than the estimates. The total amount of solar radiation received at the Earth's surface varies seasonally. Solar radiation flux reaches its maximum during the summer months, which in turn, will have a higher impact on the yearly optimum tilt angle. It is also important to consider that a minimum structural setback from roof edges should be applied for safety. Figure 4.5: Accumulated Annual Solar Radiation Distribution for SHIATS's Buildings 162
From this figure, it's apparent that Jacob School of Biotechnology & Bioengineering (JSBB) is receiving the highest amount of solar radiation for a given year followed by Department of Plant Pathology, Vice Chancellor's Office, Directorate of Seed & Farm, Department of Chemistry, etc. Rest of the buildings are shown in figure 4.8. Figure 4.8: Rest of the SHIATS's Buildings in Terms of Annual Solar Radiation Received SUMMARY AND CONCLUSIONS The current study was conducted in Sam Higginbottom Institute of Agriculture, Technology & Sciences, Allahabad, India to assess the potential of installing solar panels on the roof tops of its various buildings. A survey within SHIATS was conducted to obtain latitude and longitude points for each of the building considered in the current analysis. Google Earth's high resolution imagery was utilized for digitizing the roof tops of the buildings. Spatially distributed solar radiation maps were generated for the study area and the data was extracted for each of the building. The potential of installing solar panels on SHIATS's buildings was assessed by analyzing the total solar radiation that potentially gets accumulated on each building. The buildings were ranked according to amount of solar radiation they accumulated. Annually, the highest amount of solar radiation was accumulated on the roof top of Jacob School of Biotechnology and Bioengineering followed by Department of Plant Pathology and the 163
Vice Chancellor's office respectively. The least amount of solar radiation was found to be accumulated on the roof top of School of Film and Mass Communication preceded by the Department of Horticulture and Office of the Registrar respectively. REFERENCES [1] UN world urbanization prospects - the 2007 revision data, table and highlights. Department of Economics and Social Affairs; 2007. [2] Yang J, Brandon PS, Sidwell AC. Smart and sustainable city. In: Yang J, Brandon PS, Sidwell AC, editors. Smart and sustainable built environments. Oxford: Blackwell Publishing Inc.; 2005. p. 33-42. [3] Graham P. Building ecology: first principles for a sustainable built environment. 2003. Blackwell Science; 2003. Pp.34-81. [4] Oliver M, Jackson T. Energy and economic evaluation of building-integrated photovoltaics. Energy 2001; Vol. 26: pp 431-9. [5] Ordenes M, Marinoski DL, Braun P, Ruther R. The impact of building integrated photovoltaics on the energy demand of multi-family dwellings in Brazil. Energy and Buildings 2007; vol. 39: pp 629-42. [6] Chow TT, Hand JW, Strachan PA. Building-integrated photovoltaic and thermal applications in a subtropical hotel building. Applied Thermal Engineering 2003; vol. 23: pp.2035-49. [7] Omer SA, Wilson R, Riffat SB. Monitoring results of two examples of building integrated PV (BIPV) systems in the UK. Renewable Energy 2003; vol.28: pp.1387-99. [8] EIA. Annual energy review. Energy Information Administration, http://www.eia.doe.gov/aer; 2008. [9] EIA. Electric power monthly. Energy Information Administration, http://www.eia.doe.gov/cneaf/electricity/epm/epm_sum.html; 2010. [10] Cory KS, Swezey BG. Renewable portfolio standards in the states: balancing goals and implementation strategies. Department of Energy; 2007. [11] CSEI. Solar energy in southern Arizona. Tucson. Available at, http://giffords.house.gov/solar%20energy%20in%20southern%20arizona%20report_exec %0Summary.pdf; 2008. [12] DSIRE. Database of state incentives for renewables and efficiency. US Department of Energy; 2010. [13] Rose LS, Akbari H, Taha H. Characterizing the fabric of the urban environment: a case. [14] Federal Register. Federal Register 2009;74(194):52115e27. From the Federal Register Online via GPO Access DOCID: fr08oc09-120. Available at, http://wais.access.gpo.gov. [15] Izquierdo S, Rodrigues M, Norberto F. A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations. Solar Energy 2008; vol.82: pp. 929-39. [16] Mohammed Hashim Ameen and Dr. R. K. Pandey, Delineation of Irrigation Infrastructural, Potential and Land Use/ Land Cover of Muzaffarnagar by using Remote Sensing and GIS, International Journal of Civil Engineering & Technology (IJCIET), Volume 4, Issue 3, 2013, pp. 60-69, ISSN Print: 0976 6308, ISSN Online: 0976 6316. [17] Smita Pareek, J Sandeep Soni, and Dr. Ratna Dahiya, Modeling & Simulation of Grid Connected Photovoltaic System Incorporated with Insolation & Temperature Variation, International Journal of Electronics and Communication Engineering & Technology (IJECET), Volume 4, Issue 7, 2013, pp. 128-133, ISSN Print: 0976-6464, ISSN Online: 0976 6472. 164