Simulation and performance analysis of 110 kWp grid-connected photovoltaic system for residential building in India: A comparative analysis of various PV technology

Shukla, Akash Kumar; Sudhakar, K.; Baredar, Prashant Article Simulation and performance analysis of 110kWp grid-connected photovoltaic system for residential building in India: A comparative analysis of various PV technology Energy Reports Provided in Cooperation with: Elsevier Suggested Citation: Shukla, Akash Kumar; Sudhakar, K.; Baredar, Prashant (2016) : Simulation and performance analysis of 110kWp grid-connected photovoltaic system for residential building in India: A comparative analysis of various PV technology, Energy Reports, ISSN 2352-4847, Elsevier, Amsterdam, Vol. 2, pp. 82-88, https://doi.org/10.1016/j.egyr.2016.04.001 This Version is available at: https://hdl.handle.net/10419/187846 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ Energy Reports 2 (2016) 82–88 Contents lists available at ScienceDirect Energy Reports journal homepage: www.elsevier.com/locate/egyr Simulation and performance analysis of 110 kWpgrid-connected photovoltaic system for residential building in India: A comparative analysis of various PV technology Akash Kumar Shukla, K. Sudhakar∗, Prashant Baredar Energy Centre, Maulana Azad National Institute of Technology Bhopal, M.P., India article info Article history: Received 9 November 2015 Received in revised form 24 March 2016 Accepted 5 April 2016 Available online 20 May 2016 Keywords: Photovoltaic Rooftop Solargis PV Planner PR Energy yield abstract System simulation is necessary to investigate the feasibility of Solar PV system at a given location. This study is done to evaluate the feasibility of grid connected rooftop solar photovoltaic system for a residential Hostel building at MANIT, Bhopal, India (Latitude: 23°16′N, Longitude: 77°36′E). The study focuses on the use of Solargis PV Planner software as a tool to analyze the performance a 110 kWpsolar photovoltaic rooftop plant and also compares the performances of different PV technologies based on simulated energy yield and performance ratio. Solargis proves to easy, fast, accurate and reliable software tool for the simulation of solar PV system. ©2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). 1. Introduction Renewable energy sources are considered as alternative energy sources due to environmental pollution, global warming and depletion of ozone layer caused by green house effect. Earth receives about 3.8×1024 J of solar energy on an average which is 6000 times greater than the world consumption (Aliman et al., 2007). Solar energy is most readily available source of energy. Solar energy is Non-polluting and maintenance free. Solar energy is becoming more and more attractive especially with the constant fluctuation in supply of grid electricity. Solar power plant is based on the conversion of sunlight into electricity, either directly using photovoltaic (PV), or indirectly using concentrated solar power. Concentrated solar power systems use lenses or mirrors and tracking systems to focus a large area of sunlight into a small beam. The voltage and current both are the function of light falling on solar PV. But too much insolation on the cell causes saturation and eventually the power output is reduced because of increase in mobility of electron and increase in temperature. The other problem is tracking of the sun according to the PV module i.e. orienting the panel in such a direction so that panel receives maximum irradiance. It is anticipated that ∗Corresponding author. E-mail address: [email protected] (K. Sudhakar). photovoltaic (PV) systems will experience an enormous increase in the decades to come. However, a successful integration of solar energy technologies into the existing energy structure depends on detailed knowledge of the solar resource availability at a particular location. The electrical and thermal simulation of a roofmounted BIPV system inclined at Ballymena, Northern Ireland and facing due south was performed by using TRNSYS (Mondol et al., 2005). The efficiency of the BIPV system as a shading device was examined at different months. The simulation program SOLCEL was developed to calculate the shading effect on the solar cells, PV module temperature, incident solar irradiance, BIPV output. The simulation of a BIPV system was performed to optimize its performance through parametric analysis (Yoo, 2011). Masa-Bote and Caamaño-Martín (2014) developed a methodology to estimate BIPV electricity production under shadow. The developed methodology was validated by means of one-year experimental data obtained from two similar PV systems. The study included several weather conditions: clear, partially overcast and fully overcast sky. A performance analysis and modeling of a BIPV system in Romania was conducted by Fara et al. (2013). Forecasting tests were run by utilizing Autoregressive Integrated Moving Average models. Kane and Verma (2013) investigated the performance enhancement of a BIPV module by using thermoelectric cooling. Thermoelectric module was attached at the back of the PV module. Mathematical modeling of individual systems was performed and then, http://dx.doi.org/10.1016/j.egyr.2016.04.001 2352-4847/©2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). A.K. Shukla et al. / Energy Reports 2 (2016) 82–88 83 Nomenclature Abbreviation PV: Photovoltaic MANIT: Maulana Azad National Institute of Technology STC: Standard Test Condition AC: Alternating Current PR: Performance Ratio Symbols c-Si: Crystalline silicon a-Si: Amorphous silicon CdTe: Cadmium telluride CIS: Copper indium selenide Gh: Horizontal Irradiance Ghm: Monthly sum of global irradiation Ghd: Daily sum of global irradiation Dhd: Daily sum of diffuse irradiation T24: Daily air temperature EPV : The annual PV electricity generated the dynamic model of the BIPV system by considering the temperature of the PV module was developed. The results of the simulations revealed that the proposed cooling method improved PV efficiency with minimal power loss. Mei et al. (2002) estimated the thermal parameters which describe the performance of ventilated photovoltaic façades integrated into buildings. The method allowed the heat transfer coefficients to be obtained from data measured on an operational ventilated photovoltaic façade. TheperformanceofPVsystemsisrealizedbycomparisonwitha corresponding reference system. The simulations require input of the horizontal solar radiation and the ambient temperature data, both on a monthly basis, which have been obtained from PVGIS (0000). Becker and Parker (2009) have stated that the words simulation and modeling used as synonyms, but they are not really the same thing; at least, not to those in the field bearing those words in its name. A simulation method was developed to study the temperature behavior of double façades (Von Grabe, 2002). The model accuracy was tested by using experimental data and the model accuracy was improved by modifying the flow resistance for several geometries. The performance simulation models of PV devices are also available in some existing software, such as PVsyst, PVWATTS, TRNSYS, PVFORM, INSEL, PHANTASM, P-Spice, PV-DesignPro, SolarPro, and PVcad (Ciulla et al.,2014;Lo Brano et al.,2012;Ma et al.,2014;Ishaque et al.,2011;Cameron et al., 2008;Al-Ibrahim,1996). Grid-connected rooftop solar PV power systems generate DC power direct from the sun’s intercepted solar energy through solar PV modules. The solar PV modules are connected through a maximum power point tracker, to a grid-inverter, converting the generated DC power into AC Power, feeding the converted AC power into the public utility grid. If the grid-connected solar PV system is a roof-top system, it is often the case that the gridconnected solar PV system supplies first the power demands of the house the system is installed, selling the excess power to the local electricity provider (utility) at a defined feed-in tariff, paid by the utilitycompanytothegrid-connectedsolarPVsystemowner.Basic grid connected rooftop PV system shown in Fig. 1. The main component for grid-connected solar PV power systems comprise of: •Solar PV modules, connected in series and parallel, depending on the solar PV array size, to generate DC power directly from the sun’s intercepted solar power. •Maximum power point tracker (MPPT), making sure the solar PV modules generated DC power at their best power output at any given time during sunshine hours. •Grid-connected DC/AC inverter, making sure the generated and converted AC power is safely fed into the utility grid whenever the grid is available. •Grid connection safety equipment like DC/AC breakers fuses etc., according to the local utility’s rules and regulations. Modeling of a yield simulation requires a large quantity of input data like solar irradiation, local weather conditions and other technical parameters of the planned PV systems (Huld et al., 2008). The level of accuracy needed for the energy yield prediction depends on the stage of project development. For example, a preliminary indication of the energy yield can be carried out using solar resource data and estimates of plant losses based on nominal values seen in existing projects (Huld et al., 2008). Photovoltaic software is widely used in the design of photovoltaic systems to calculate expected energy yield. Solargis PV Planner is one of the software that has the capability of Modeling Solar PV system (Kenny et al., 2006). The present analysis is aimed: •To describe and assess the solar resource potential at the given site. •To perform simulation of 110 kWpgrid connected rooftop solar power plant using solargis PV Planner software. •To determine annual energy yield and performance ratio of the PV system. •To determine the best PV technologies for the building based on the simulation results. •To provide a baseline information for energy and economic assessment of the PV generated electricity. 2. System description The site selected for the study is based on institution campus located 10 km north of Bhopal, Madhya Pradesh India. The institution (MANIT, Bhopal) obtains its power from Madhya Pradesh electricity board public grid which is shared with other residential and industrial consumer. The site selected for the study is a residential Hostel Building with large space available on rooftop area (roughly 1065 m2). The location and other site specific information are shown in Figs. 2,3and Table 2. The system description is given in Table 3 and total load consumption of Hostel building is listed in Table 1. 3. System modeling and performance evaluation In PV Planner, photovoltaic power production is simulated using numerical models developed or implemented by Geo Model using aggregated data based on 15-min time series of solar radiation and air temperature data as inputs. The simulation itself is quite complex process. Fig. 4 shows the main steps involved in simulation. Two parameters must be set before performing the PV simulation on the preselected site (Perez et al., 1992). •Site Parameters: Provided by Solar GIS database (solar radiation parameters, air temperature parameters) and formulas implemented in Solar GIS system (sun path geometry). •Technical Parameters: Provided by PV Planner user, otherwise default values are taken into consideration. 84 A.K. Shukla et al. / Energy Reports 2 (2016) 82–88 Fig. 1. Basic grid connected rooftop PV system. Fig. 2. Photograph of Hostel No. 1, MANIT Bhopal, Madhya Pradesh. Table 1 Load consumption in hostel building. Electrical load and capacity (W) Quantity Total capacity in Watt (kW) Hours of operation per day (h) Energy consumptions in kWh Tube light: 40 W 200 8 10 80 Fan: 40 W 180 7.2 10 72 Water pump: 746 W 3 2.24 5 11.2 Water geyser: 1500 W 6 9 5 45 Refrigerator: 1000 W 1 1 22 22 Water cooler: 700 W 6 4.2 20 84 Total energy consumption per day 314.2 kWh Table 2 Site information. Site name Hostel No.1, MANIT Bhopal, India Coordinates 23°12′45.29′′ N, 77°24′32.62′′ E Elevation a.s.l 532 m Slope inclination 1° Slope azimuth 340°north Annual global in-plane irradiation 2067 kWh/m2 Annual air temperature at 2 m 24.8 °C Table 3 System description. Installed power 110.0 kWp Type of modules c-Si/a-Si/CdTe/CSI Mounting system Fixed mounting, free standing Azimuth/inclination 180°(south)/23° Inverter Euro eff. 96.0% DC/AC losses 5.0%/2.0% Availability 95.0% These parameters are implemented in the Computation process in eight steps (Perez et al.,2002,1987). Step 1: Global irradiation on in-plane surface Initially, the 100% conversion of global in-plane irradiation at STC conditions is assumed. Global irradiation impinging on a tilted plane of PV modules is calculated from Global Horizontal Irradiance(Gh), DNI, terrain albedo, and instantaneous sun position within 15 min time interval. Step 2: Losses due to terrain shading Reduction of global in-plane irradiation is calculated assuming obstruction horizon of terrain and PV modules. Shading by terrain features is calculated by disaggregation using SRTM-3 DEM and horizon height. Shading of local features such as from nearby building, structures or vegetation is not considered: Step 3: Losses due to angular reflectivity The resulting irradiation is subjected to losses from angular reflectivity on the surface of PV modules, and the magnitude of effects depends on relative position of the sun and plane of the module. The accuracy of calculations of angular reflectivity losses depends on cleanness and specific properties of the module surface. Step 4: Losses due to performance of PV module outside of STC conditions The conversion efficiency is non-linear and depends on the distribution of pair of values of irradiance and temperature. Relative change of produced energy from this stage of conversion depends on modules technology and mounting type. Crystalline silicon is the technology with the lowest uncertainty of performance prediction among the three most commonly used technologies: crystalline silicon, copper indium gallium selenide, and cadmium telluride. Typically, loss at this step is higher for crystalline silicone modules than thin films due to higher negative thermal power coefficient of crystalline silicon and higher conversion efficiency of thin films at low light levels. A.K. Shukla et al. / Energy Reports 2 (2016) 82–88 85 Fig. 3. Site location—Hostel No.1, MANIT Bhopal, Madhya Pradesh. Source: https://earth.google.com. Step 5: DC losses A number of effects cause DC power losses: •Mismatch due to different MPP operating point of modules connected into an inverter, heat losses in interconnections and cables; these losses depend on the design and components of the PV power plant •Dirt and dust, snow, icing, soiling, bird droppings. •Inter-row shading. •Losses in the DC section are to be inserted by a user. The total magnitude of DC losses typically ranges from 5% to 9% or more, depending on the site and system configuration. Step 6: Inverter losses from conversion of DC to AC Due to inverter specification an inverter euro efficiency approximating average losses gives good information on the inverter performance. The inverter Euro efficiency typically ranges from 93.5% to 97.5%. Step 7: AC and transformer losses Losses in AC section and transformer depend on the system architecture. The inverter output is connected to the grid through the transformer. These losses usually range from 1.5% to 2.5%. Step 8: Availability Availability can be assumed that 0.5% to 2% of yearly PV power production may be lost due to various disruptive events. Solar GIS PV Planner PVGIS is Energy Capacity Assessment Tool which is used to quickly assess the feasibility of potential solar sites for Installation of roof or ground-mounted PV System. It determines the likely yield and revenue from roof-mounted plant using various PV technologies (a-Si, c-Si, CdTe, CIS) modules. Key technical assumptions (Skoczek et al.,2008,2009) 1. Modules degrade over thetime due to aging of components and stress due to weather cycles. Fig. 4. Simulation methodology. Source: http://solargis.info. 86 A.K. Shukla et al. / Energy Reports 2 (2016) 82–88 0 20 10 60 50 40 30 70 80 90 0 45 90 135 180 225 270 315 module horizon terrain horizon active area civil time sun path solar time Fig. 5. Path of the Sun over a year. 0 10 20 30 40 50 0 2 4 6 8 10 16 14 12 18 24 22 20 day length [hours] day length, horizon corrected [hours] minimum solar zenith angle [°] Fig. 6. Variation of the day length and solar zenith angle. 0 1 2 3 4 5 6 7 8 reflected diffuse direct Fig. 7. Variation of global in-plane irradiation and air temperature. 2. Degradation process of PV is considered for a period of 25 years. 3. Linear annual degradation rate is considered. 4. Low level of annual degradation rate (1%–0.5%) is taken. 5. Fixed module tilt angle of 23 is taken to maximize the irradiation received by the PV module. 6. Installed module capacity: 110 kWp. 7. Standard test conditions are assumed for performance assessment. Performance indices (Ma et al.,2014;Kumar and Sudhakar, 2015) The performance of a PV system depends on its solar radiation inputandenergyoutput under the operating conditions. According to IEC standard, two important parameters, e.g. system energy Table 4 Global horizontal irradiation and air temperature. Month GhmGhdDhdT24 January 139 4.50 1.68 17.0 February 151 5.40 1.82 20.3 March 199 6.40 2.24 26.0 April 208 6.94 2.66 31.3 May 214 6.92 3.10 34.5 June 164 5.48 3.09 31.6 July 123 3.96 2.84 26.2 August 121 3.89 2.69 24.5 September 149 4.96 2.46 24.8 October 168 5.42 2.06 23.7 November 138 4.59 1.86 20.5 December 131 4.22 1.66 17.4 Table 5 Average yearly sum of global irradiation for different types of surface. Types of surface kWh/m2Relative to inclined Horizontal 1905 92.1% Inclined (25°) 2067 100.0% 2-axis tracking 2543 123.0% yield and system performance ratio (PR), are implemented to evaluate and compare the performances of PV systems. The energy yield is defined as the energy output divided to the nameplate power of the photovoltaic generator in Standard test conditions (Ma et al.,2014,2013). Energy Yield = EPV,AC Pmax G,STC .(1) The performance ratio (PR) is defined as the energy output EAC divided by the nameplate D.C. power EDC obtained in Standard test condition. Performance Ratio (PR)= EACkWh (EDCSTC ×Irradiation).(2) 4. Results and discussion 4.1. Detailed solar assessment Solar irradiation data represent some of the most important inputs for an expert assessment of energy yield. The analysis factored in site-specific meteorological data, including temperature and humidity, across the selected sites. Fig. 5 shows that the path of the Sun over a year and variation of the day length and solar zenith angle as shown in Fig. 6. The plant has more global irradiation in the month of May (214 kWh/m2) correspondingly more daily sum of global irradiation is recorded. The plant has more global in-plane irradiation in the month of May (214 kWh/m2) correspondingly more daily sum of global in-plane irradiation was recorded (Fig. 7). Monthly global horizontal and air temperature as shown in Table 4 and Average yearly sum of global irradiation for different types of surface is given in Table 5. The modules receive 123% more solar radiation in a two axis tracking configuration over inclined configuration of 25 and as much as 92.1% solar radiation in the horizontal surface relative to the fixed titled system. Using twoaxis trackers, solar modules were assessed to deliver more energy than fixed tilt and horizontally placed panels. 4.2. Energy yield and performance ratio The performance of each PV system is depicted by its energy yield and performance ratio (PR) that are defined in (1) and (2) respectively. The energy yields and PRs of four PV A.K. Shukla et al. / Energy Reports 2 (2016) 82–88 87 0 5 10 15 20 electricilty production performance ratio 0 5 10 15 20 electricilty production performance ratio (a) Crystalline silicon (c-Si) module. (b) Amorphous silicon (a-Si) module. 0 5 10 15 20 electricilty production performance ratio 0 5 10 15 20 electricilty production performance ratio (c) Cadmium telluride (CdTe) module. (d) Copper indium selenide (CIS) module. Fig. 8. Variation of PV electricity production and performance ratio. Table 6 Annual average electricity production and average performance ratio. PV technologies Annual average electricity production (MWh) Energy output (kWh/kWp) Average performance ratio (%) Crystalline silicon (c-Si) 163.1 1483 71.6 Amorphous silicon (a-Si) 181.1 1646 79.5 Cadmium telluride (CdTe) 175.3 1593 77.0 Copper indium selenide (CIS) 166.5 1513 73.1 technologies are calculated and presented in Fig. 8. Annual average electricity production and average performance ratio are shown in Table 6. It is noticeable that system energy yields varies from 1483 kWh/kWp(c-Si module) to 1646 kWh/kWp(a-Si module) and the system PRs range from 71.6% (c-Si module) to 79.5% (a-Si module). Performance ratio above 0.8 is always desirable, as high performance directly constitutes an economic gain. Amorphous PV technology shows better performance in terms of higher annual average electricity production (181.1 MWh) and energy yield than the other three technology. This may be due to lower temperature coefficient and capture losses of these technologies, but their efficiencies may be lower under standard test conditions. 5. Conclusion This paper has evaluated the technical performance of a 110 kWpgrid connected roof top solar PV-system to supply electricity and energy for the Hostel building. Four types of PV moduleshave beensimulated todetermineperformanceratiosand Energy yield. 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