INTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 1, No 2, 2010 © Copyright 2010 All rights reserved Integrated Publishing Association RESEARCH ARTICLE ISSN 09764259
Estimating the Solar Photovoltaic generation potential and possible plant capacity in Patiala Souvik Ganguli 1 , Jasvir Singh 2 1 Assistant Professor, Department of Electrical & Instrumentation Engineering, Thapar University Patiala. 2 Assistant Professor, Department of Electrical Engineering, Bhai Gurdas Institute of Engineering & Technology Sangrur
[email protected]
ABSTRACT The depletion of fossil fuel resources on a worldwide basis has necessitated an urgent search for alternative energy sources to meet up the present day demands. Solar energy is clean, inexhaustible, environmentfriendly and a potential resource among the various renewable energy options. The amount of incident solar radiation significantly determines the electricity produced by photovoltaic (PV) systems. The paper reports a novel method to measure the potential of solar electricity generation in Patiala on the basis of solar radiation data obtained from the weatherstation installed within the Thapar University campus. Further, possible plant capacity is estimated for an arbitrarily chosen area. The results supported justify the method proposed. Keywords: Diurnal variation, daily energy output, monthly energy output, photovoltaic (PV) system, solar radiation data, yearly energy output. 1. Introduction It is anticipated that photovoltaic (PV) systems will experience an enormous increase in decades to come. However, a successful integration of solar energy technologies into the existing energy structure depends also on a detailed knowledge of the solar resource. Therefore solar radiation is a key factor determining electricity produced by photovoltaic (PV) systems which is usually obtained using Geographical Information System (GIS). A case study of solar radiation database was prepared in Europe as was reported in [1]. Using Photovoltaic Geographic Information System (PVGIS) another study was made in the 25 European Union member states and 5 candidate countries. The calculation of electricity generation potential by contemporary PV technology is a basic step in analyzing scenarios for the future energy supply and for a rational implementation of legal and financial frameworks to support the developing industrial production of PV. Three aspects were explored the expected average annual electricity generation of a ‘standard’ 1 kWp gridconnected PV system, the theoretical potential of PV electricity generation and determination of required installed capacity for each country to supply 1% of the national electricity consumption from PV. The analysis shows that PV can already provide a significant contribution to a mixed renewable energy portfolio in the present and future European Union [2]. In [3], a GIS based analysis of the theoretical PV potential to be installed on noise barriers along Italian national roads has been carried out. [4] presents a methodology for the 253
INTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 1, No 2, 2010 © Copyright 2010 All rights reserved Integrated Publishing Association RESEARCH ARTICLE ISSN 09764259
assessment of photovoltaic potential in urban areas using opensource solar radiation tools and a 3D city model implemented in a geographic information system (GIS). The solar radiation tools are represented by the r.sun solar radiation model and PVGIS estimation utility. The applicability of the methodology has been demonstrated on a selected urban area of a small city in Eastern Slovakia. In [5] the potential of different PV systems in countries with high solar irradiation was explored and their performances were compared through the assessment of thirteen different types of PV systems that had been installed side by side in Nicosia, Cyprus and Stuttgart, Germany. Finally useful insight into the performance of the PV systems as a function of the meteorological conditions and location was highlighted. A GIS database of solar radiation was presented in [6] and photovoltaic (PV) potential estimations of 10 European Union Candidate Countries were created to support that data. The database was integrated with a web application to provide access also to the public at large. An application was developed to browse and query GIS maps and to do a simple calculation for any location within that region. The objective of the work carried in [7] was to examine the performance as well as the economic feasibility of gridconnected PV systems in the Kuwaiti climate. A program was written to evaluate the performance as well as the economic feasibility of such systems in Kuwait. The input to the program was the weather data for Kuwait, time dependent building loads, as well as the utility rates for Kuwait. Weather data generator subroutine included in TRANSYS (Transient Simulation) program was used to generate hourly weather conditions from the monthly average values of daily radiation on horizontal surface, and ambient temperature available for Kuwait. The fiveparameter PV model was then used to determine the performance of the solar modules used in that study. ENERGY10, a design tool is used to model and simulate the performance of PV systems that is integrated with the building. [9] discusses the recorded Global Solar Radiation, received in the Kathmandu valley by three different, Simonocrystalline, Sipolycrystalline and Siamorphous calibrated solar cells and proposes the bestsuited solar PV module technology for roof top solar PV systems inside the Kathmandu valley. Estimation of solar generation potential and plant capacity in several districts of West Bengal is given in [10]. The PV module efficiency considered for this work was 12%. Thus we find that most of the previous literatures involve the use of GIS systems to obtain the solar photovoltaic potential estimation. The method described in this paper suggests a unique method to measure the PV potential in Patiala and estimate the possible plant capacity based on the available area, chosen as 100 m 2 for this work. 2. Methodology To find out the solar photovoltaic generation potential, the solar radiation over 8 months (September 2009April 2010) is measured in Patiala using the weatherstation data (only solar radiation is shown). Then the diurnal variations, average monthly output, yearly output have been found out and related graphs are plotted for showing the variation in different
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INTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 1, No 2, 2010 © Copyright 2010 All rights reserved Integrated Publishing Association RESEARCH ARTICLE ISSN 09764259
seasons and time. Also observing the peak value in different days, the monthly average peak is calculated and variation of the monthly peak is plotted for a year and the average annual peak is also calculated. For calculating the output the efficiency of the PV module is taken as 14.3% [11]. Chosen area for the estimated plant capacity is considered as 100 m 2 . 3. Results and Discussions The solar radiation data is given for the month of April 2010 at 9 A.M. as a sample in Table1. Table 1: Solar Radiation Data & Calculation of Average Output April 2010 (Time: 9 AM)
Date
PV Module Efficiency
Solar Output Total Output Average 2 Radiation (Watt/m ) (Watt/m 2 ) Output (Watt/m 2 ) (Watt/m 2 ) 01.04.2010 481.25 68.81875 02.04.2010 498.125 71.231875 03.04.2010 505 72.215 04.04.2010 480.625 68.729375 05.04.2010 499.375 71.410625 06.04.2010 528.75 75.61125 07.04.2010 575.625 82.314375 08.04.2010 413.75 59.16625 09.04.2010 509.375 72.840625 10.04.2010 511.875 73.198125 11.04.2010 499.375 71.410625 12.04.2010 444.375 63.545625 13.04.2010 551.25 78.82875 14.04.2010 555 79.365 15.04.2010 14.3% 569.375 81.420625 2034.443125 67.81477083 16.04.2010 514.375 73.555625 17.04.2010 501.25 71.67875 18.04.2010 343.75 49.15625 19.04.2010 239.375 34.230625 20.04.2010 479.375 68.550625 21.04.2010 432.5 61.8475 22.04.2010 450.625 64.439375 23.04.2010 483.75 69.17625 24.04.2010 316.875 45.313125 25.04.2010 565.625 80.884375 26.04.2010 531.25 75.96875 27.04.2010 451.25 64.52875 28.04.2010 479.375 68.550625 29.04.2010 511.875 73.198125 30.04.2010 302.5 43.2575 Similar calculations are also made at intervals of one hour till 4 P.M. in the evening. Diurnal variation for that month (Table2) is then calculated.
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INTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 1, No 2, 2010 © Copyright 2010 All rights reserved Integrated Publishing Association RESEARCH ARTICLE ISSN 09764259
Table 2: Calculations for Diurnal Variations (April 2010) Time
Average Output (Watt/m 2 )
Average Daily Energy Monthly Energy Output Output Output (Watt/m 2 hr) (Watt/m 2 hr) (Watt/m 2 hr) 9 AM 67.81477083 67.81477083 10 AM 92.60739583 92.60739583 11 AM 111.1258958 111.1258958 12 NOON 117.2510625 117.2510625 729.4847083 21884.54125 1 PM 115.0345625 115.0345625 2 PM 102.9123333 102.9123333 3 PM 76.70758333 76.70758333 4 PM 46.03110417 46.03110417 Finally all the diurnal variations for eight months are plotted as shown in Figure 1 given below.
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INTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 1, No 2, 2010 © Copyright 2010 All rights reserved Integrated Publishing Association RESEARCH ARTICLE ISSN 09764259
Figure 1: Graphs for Diurnal Variations (Sept 2009Apr 2010) Thereafter, daily energy output and monthly energy output are calculated as shown in Table3. After that, the average yearly output is calculated as shown. Table 3: Total Energy Output Months
Daily Energy Output (Watt/m 2 hr)
September October November December January February March April
664.3905787 544.2209259 371.274475 284.6676219 329.40765 569.1495759 700.3972782 729.4847083
Monthly Energy Average Output Monthly Energy (Watt/m 2 hr) Output (Watt/m 2 hr) 19931.71736 16326.62778 11138.23425 8824.696279 9882.2295 15936.18813 21011.91835 21884.54125
15617.01911
Average Yearly Energy Output (Watt/m 2 hr)
187404.2293
Corresponding graphs for daily and monthly energy outputs (Figure 2) are plotted as shown.
Figure 2: Graphs for Daily and Monthly Energy Ouptuts (Sept 2009Apr 2010)
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INTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 1, No 2, 2010 © Copyright 2010 All rights reserved Integrated Publishing Association RESEARCH ARTICLE ISSN 09764259
The peak variations for the different months (Table4) are shown and the possible plant rating is determined using 100 m 2 available area. Table 4: Peak Variation & Possible Plant Rating Months
Peak Output (Watt/m 2 )
September October November December January February March April
102.9467593 98.17016204 71.46425 56.93546 60.228025 96.72928571 115.8357661 117.2510625
Average Peak Output (Watt/m 2 )
94.66075866
Average Peak Possible Plant Output for 100 Capacity (KW) m 2 Area(Watt)
9466.075866
9
Monthly peak variations are also plotted as shown in Figure 3.
Figure 3: Graph for Monthly Peak Variations (Sept 2009Apr 2010) From the results obtained it is found that the month of December produced the lowest solar radiation. Monthly and yearly outputs were calculated on the basis of 100 m 2 area. Considering the monthly peaks, the average peak output is calculated from where as estimate of the possible plant rating is made. The methodology adopted seems satisfactory for determining the possible plant capacity for an arbitrarily chosen area. From the calculations we found a solar photovoltaic power plant of capacity 9 KW can be achieved in Patiala over an available area of 100 m 2 . 4. Conclusion Available area for the calculation shown has been considered to be 100 m 2 . With greater available area higher capacity plant can be set up. Moreover, the possible plant capacity has been estimated from the peak output results available from the solar radiation readings of each month. No optimized approach has been carried out which can be taken up as a future scope of work. Thus the average output and subsequent calculation there from may not reflect the true scenario of solar photovoltaic generation potential of Patiala district of Punjab. Maximum Power Point
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INTERNATIONAL JOURNAL OF APPLIED ENGINEERING RESEARCH, DINDIGUL Volume 1, No 2, 2010 © Copyright 2010 All rights reserved Integrated Publishing Association RESEARCH ARTICLE ISSN 09764259
Tracking (MPPT) has not been employed for the calculation which could have produced better results. Had calculations been available for the month of May and June which offers the highest solar radiation, the result would have been far more accurate and yielded higher capacity plant. Relative comparison with the other districts of Punjab can be taken up for future studies. Designing, cost analysis and efficiency calculations of this solar photovoltaic power plant now need to be done once the capacity is estimated which can be carried out in future publications. Environmental impact of this photovoltaic plant can be taken up as one the important issue in near future.
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10. Ganguli S, Sinha S, 2009, “A Study and Estimation of Grid Quality Solar Photovoltaic Power Generation Potential in some districts of West Bengal”, National Conference on Trends in Instrumentation & Control Engineering, Thapar University, Patiala, pp. 522528. 11. BP Solar Datasheet (BP 7180) for 180 Watt Photovoltaic ModuleSaturn Technology.
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