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Monitoring the performance of the building attached photovoltaic (BAPV) system in Shanghai
Accepted Manuscript Title: Monitoring the performance of the building attached photovoltaic (BAPV) system in Shanghai Author: Xinfang Wu Yongsheng Liu Juan Xu Wei Lei Xiaodong Si Wenlong Du Chunjiang Zhao Yunbo Zhong Lin Peng Jia Lin PII: DOI: Reference:
S0378-7788(14)01042-1 http://dx.doi.org/doi:10.1016/j.enbuild.2014.11.073 ENB 5542
To appear in:
ENB
Received date: Revised date: Accepted date:
29-8-2014 26-11-2014 29-11-2014
Please cite this article as: X. Wu, Y. Liu, J. Xu, W. Lei, X. Si, W. Du, C. Zhao, Y. Zhong, L. Peng, J. Lin, Monitoring the performance of the building attached photovoltaic (BAPV) system in Shanghai, Energy and Buildings (2014), http://dx.doi.org/10.1016/j.enbuild.2014.11.073 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Highlights
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1. The first grid-connected roof-mounted BAPV system in Shanghai, China.
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2. Performance like performance ratio is analyzed based on the monitoring data.
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3. The conversion efficiency of inverter for different weather is assessed.
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4. Environmental effects are assessed based on parameters like energy consumption.
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5. Solar radiation and energy output are predicted using PVSYST.
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Monitoring the performance of the building attached photovoltaic (BAPV)
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system in Shanghai
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Xinfang Wu1, Yongsheng Liu1*, Juan Xu1, Wei Lei1, Xiaodong Si1, Wenlong Du1, Chunjiang
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Zhao1, Yunbo Zhong2, Lin Peng1, Jia Lin1
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1. Institute of Solar Energy, Shanghai University of Electric Power, Shanghai 200090,
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2. School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China
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Abstract：
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This work presents the performance monitoring of thefirst grid-connected roof-mounted
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building attached photovoltaic (BAPV) system located in Shanghai, China. The system,
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whose capacity is ~2992 Wp, began to operate in late December, 2006. The photovoltaic
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performance is monitored and analyzed from year 2007 to 2009. The average annual output
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and average daily output are 3189.13 kWh and 8.74 kWh.The predicted yearly energy output
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is 3220.21 kWh by software of PVSYST. The annual global solar radiation on horizontal,
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predicted and measured on tilt are 1315.15 kWh/m2,1320.24 kWh/m2 and 1323.09 kWh/m2,
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respectively. The daily reference yield, array yield and final yield are 3.62 h/d, 3.01 h/d and
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2.86 h/d, respectively. The average daily capture loss and system loss of the overall BAPV
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array per month are very low and ~0.61 h/d and 0.16 h/d in value, respectively, which means
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that the BAPV array matches well with the inverters. The average monthly performance ratio,
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* Corresponding author. Tel: +86-21-35303922; Fax:+86-21-68029219; E-mail address: [email protected] (Y. Liu)
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system efficiency and array efficiency are 80.66%, 10.73% and 11.34%, respectively. The
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difference between system efficiency and array efficiency is ~0.60%, which means the
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average inverter efficiency is about 94.60%.
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Keywords:BAPV, grid-connected, monitoring performance，PVSYST
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1. Introduction The current world faces some significant challenges, including climate change and
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energy demand. Climate change and global warming are mainly aroused bygreenhouse gases
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such as carbon dioxide, which is generated by the burning of the conventional energy such as
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fossil fuel [1]. As a kind of renewable energy, solar energy has many advantages while
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traditional energy rarely has, such as clean, numerous.Moreover, compared to other
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renewable energy like wind, tidal, nuclear, biomass, and others, solar energy is abundant and
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inexhaustible. Sunlight power that reaches the Earth is much more than the world’s present
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energy consumption. So, solar energy should be fully utilized [2-4].
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At present, the buildings in most cities are high-rise, and the roof area is not enough for
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stand-alone photovoltaic (PV) system installation [5]. To make the roofs of the buildings to
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be fully used, building photovoltaic power system, as a novel concept to use solar energy,
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has been proposed. Based on different installation ways, it can be divided into two models,
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the building attached photovoltaic (BAPV) and the building integrated photovoltaic (BIPV).
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For BIPV system, PV array is placed on the top of a building to replace the traditional
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materials. While for BAPV systems, PV modules are installed on the surface of building roof
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with a certain inclined angle and they are supported by some superstructure [6-9]. Both of
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them are space-saving by occupying the roofs of the building, however, the expenses of the
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installations are quite enormous [10-11].
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The total installation capacity of PV systems in year 2011 is just 3.6 GW, which only
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occupies for 0.1% of the total installation capacity of power stations in China. Chinese 4
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government proposed enlarging installation capacity of PV systems to 21 GW, which is 5
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times the installation capacity in year 2011. Furthermore, yearly solar radiation quantity in
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Shanghai, China (a latitude of 31°12´ north and a longitude of 121°24´) is about 1300
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Wh/m2. In order to use building roofs and solar power,a plan of approximately one hundred
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thousand buildings roofs with a total area of 3 million square meters can be used to generate
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power through solar energy isproposed by the government of Shanghai City. This paper
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illustrates the first grid-connected BAPV system in Shanghai. As the performance of a PV
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system can be influenced by many factors, such asshade, ambient temperature, the surface
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temperature of PV modules, solar irradiance, and others, the indicators including final yield,
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performance ratio (PR), outputyields, capture loss, system loss and some other parameters
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are chosen to evaluate the performance of the system.
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2. Description of the BAPV system
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In addition, a number of simulation softwares are developed to estimate theperformance
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of a solar power plant to assist the system designersand installers.Theselection of the
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software should be based on the judgment that howclosely the predicted and measured data
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agree. PVSYST is mostly used forstudying, sizing and performance analysis of photovoltaic
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standalone, grid connected and water pumping systems. PVSYST mainlyrequires
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meteorological data (global horizontal solar radiation,ambient temperature), electrical and
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mechanical specifications ofthe installed PV modules, as inputs. In this work PVSYT is used
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to predict thetotal monthly and yearly energy output of a PV system with an installed
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capacity of 3 kW. The measured global solar radiationon solar panel and monthly average
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ambient temperaturefrom year 2007 to 2009 are used as input data.Investigation on the
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performance of the PV system has been analyzed to provide technical reference for the PV
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system.The BAPV system is situated on the roof of a residential building which locates in
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Minhang district, Shanghai City, China. It is the first practical application of a home-based
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grid-connected roof-mounted BAPV system in China.Ithas great significance to the research
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of BAPV systems and promotes the development of Chinese renewable energy. The BAPV
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system made of polycrystalline silicon photovoltaic modules was installed on the roof in
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order to provide electricity to the building. The criterion for the PV system installation and
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experiment [12] is based on the Standard GB/T 19064-2003 Specification and Test Method
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for Residential PV System of China. The relevant procedural requirements of PV system
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components are offered by the technical standard as to guide PV system construction.
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Standard GB/T 19939-2005 Technical Requirements for Grid Connection of PV System of
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China is adopted to determine grid-connected method of PV system and technical
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requirements of PV system components [13].
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The PV system started to operate in December 2006, and an automated data monitoring
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system has been running for data acquisition, including PV ambient temperature, solar
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radiation and electricity generated data. The PV system is composed of 22 pieces of
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polycrystalline silicon photovoltaic cells with a total area of 22 m2. Though the optimal tilt
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angle in Shanghai region is 23°, considering factors of ash deposition, water conveyance,
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and other reasons, the system is designed with an inclined angle of 25°facing south. No
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buildings surrounding, the PV array is covered without any shadow, as shown in Fig. 1. On
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the standard condition, the open-circuit voltage, operating voltage, short-circuit current and
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operating current of the BAPV system are DC535 V, DC427 V, DC7.43 A and DC7.01 A 6
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respectively. The total installed capacity is 2992 Wp. The system can be considered as a
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standard grid connected photovoltaic system that includes a solar PV array, an inverter, an
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AC power distributing cabinet, and a data monitor. Based on the average solar irradiance of
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700~800 W/m2 in summer in Shanghai, even the solar irradiance is fully used, the output
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power is less than 2.5 kW. Hence the output power of the inverter of the system is designed
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to be 2.5 kW. The actual operation results show that the inverter could match with the PV
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arrays. The schematic of BAPV system is illustrated in Fig. 2.
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3. Performance analysis
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3.1 Performance criterion
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In order to analyze the performance and reliability of the system, array yields, final
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yields, reference yields, array efficiencies, system efficiencies, capture losses, system losses,
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and performance ratio (PR) are calculated based on the IEC 61724 standard [14-17].
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The array yield YAis the proportion of EDC to P0, while the final yield YF is the ratio of
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EAC to P0,where EDC is the DC energy output per day of array, P0 is the installed capacity,
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and EAC is the AC output energy that connects to the grid per day.
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YA =
E DC (1) P0
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YF =
E AC (2) P0
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The reference yield YRis the ratio of HId to GI. HId is the daily total in-plane irradiation
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by the modules. GI represents the reference in-plane irradiance.
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YR
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The array capture loss LC is the difference between YR and YA. It is caused by
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incomplete utilization of the available radiation in the array operation period.
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LC= YR – YA (4)
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to convert the DC signals to AC signals.
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LS= YA –YF
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The performance ratio (PR) is the ratio of YF to YR, which represents the relationship
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between the actual and theoretical energy output of the PV system. The loss may be caused
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by array temperature, incomplete utilization of the irradiation, and system component
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inefficiencies or failures.We can use PR to monitor the status of the PV power station over a
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long operation time and compare the performance different PV systems located at the
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different geographical areas [18–21].
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PR=
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The array efficiency Amean and system efficiency system are mean and net energy
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conversion efficiencies of the PV array, respectively, being defined as the ratio of the daily
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array DC output energy or net AC output energy to the corresponding radiation quantity per
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unit area of the PV array.
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Amean =
E DC，total (7) H id A
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system =
E AC，total (8) Hid A
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3.2 Analysis results An automated data monitoring system has been installed to measure the ambient 8
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temperature, solar radiation and output power since these parameters are important to design
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the system. The following data were recorded from year 2007 to 2009 for this study. With
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these data, we can assess the feasibility and performance of the system.
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3.2.1 The data of temperature
Temperature affects system performance strongly. The temperature coefficient describes
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the behavior of the electrical characteristics of the PV with the operating temperature and
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hence the thermal effects. The temperature indicator was installed beside the PV array to
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measure the PV ambient tempertaure. The highest and lowest temperatures are closely
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related to the over-voltage and under-voltage of the system and the capacity resisting heat
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and cold of the equipment, and can directly affect the output voltage and power of the PV
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system [22] and also affect the operation performance of the DC/AC inverter. The highest
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and lowest temperatures can also affect the charging voltage of the stand-alone system with
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battery [23]. It should be noted that the solar radiation can result in the temperature risingand
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the output voltage of the PV array unstable. So the highest and lowest temperatures are
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selected as the essential reference for the PV system design. Fig. 3 and Fig. 4 show the
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monthly lowest and highest temperatures from year 2007 to 2009. Fig. 5 describes the
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monthly average temperature in year 2007, 2008 and 2009. The lowest temperature in the
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three years is -6.26oC in January 2009, while the highest temperature is 41.44oC in July 2007.
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The average monthly temperature in July is 30oC, which is relatively high. We can use these
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data to predict the output yield and the surface temperature of PV modules.
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3.2.2 The monthly solar radiation The solar radiation is one of the factors that disturb the grid. The solar radiation 9
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instrument was installed on the same plane with the PV array to measure solar radiation. The
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solar radiation quantity is the reference for planning the monthly power generation and
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laying the power dispatching plan of the grid departments. The optimal instantaneous solar
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radiation is linked to the capacity of these electrical components, the resistance to overload
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and the design of withstand voltage of the electrical components in the PV power generation
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system. Table 1 shows the monthly solar radiation quantity from year 2007 to 2009.
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The values of the annual solar radiation quantityin 2007, 2008 and 2009 are 1345.6
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kWh/m2, 1358.9 kWh/m2 and 1264.8 kWh/m2, respectively. The corresponding annual solar
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radiation in three years is 1323.09kWh/m2. We can see that the solar radiation quantity in
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June is lowerthan that in May and July. This phenomenon is ascribed to the plum rain in June
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in Shanghai.
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The variation of monthly and yearly global horizontal, actual measured and predicted
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solar radiation in the plane of array is shown in Fig.6.The measured monthly global solar
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radiation on horizontal surfacevaries from 56.52 kWh/m2 in January to 162.31 kWh/m2 in
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May.The predicted monthly solar radiation at 25゜ tilt varies from 73.78 kWh/m2 in January
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to 142.60 kWh/m2 in July, while the actual measuredmonthly solar radiation at 25゜ tilt
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varies from56.52 kWh/m2 in January to 147.71 kWh/m2 in May. The annualglobal solar
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radiation on horizontal,predicted and measured on tilt are 1315.15 kWh/m2,1320.24
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kWh/m2and 1323.09 kWh/m2, respectively. The yearly predicted and actual measured solar
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radiation on tilt are quite close.
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3.2.3 The monthly output yield of the system The monthly amount of the generation power from year 2007 to year 2009 of the BAPV 10
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system is given in table 2. The monthly output yields of the system are 3159.0 kW.h in
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2007, 3250.5 KW.h in 2008 and 3157.8 KW.h in 2009, respectively. Total output in three
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years is 9567.4 kWh, per kilowatt output is 3189.1 kWh, and the average daily output is 8.7
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kWh.
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The variation of monthly and yearly actual measured and predicted energy output is
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shown in Figs.7. The predicted monthly energy output varies from 180.11 kWh in January to
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348.13kWh in July. The measured monthly energy outputchanges from179.13 kWh in
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January to372.16 kWh in May. The annual predicted and measured energy outputsare
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3220.21 kWh and 3189.13kWh. The results show the predicted data matches well with
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themeasured datawhen measured solar radiationdata of the site is used as input to the
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simulation software.
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From table 1 and table 2, it can be found the monthly output yield increases with a rise
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of solar radiation quantity. Both of the two parameters are higher in summer than those in
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winter. The solar radiation quantitiesin year 2007, 2008, 2009 are 95.6kWh/m2,
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88.0kWh/m2and 120.9kWh/m2in June, 149.5kWh/m2, 159.9kWh/m2 and 133.7kWh/m2in
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May, 140.3kWh/m2, 160.6kWh/m2 and 133.4kWh/m2in July. The output in year 2007, 2008,
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2009 are 227.9kWh, 215.9kWh and 291.4kWh in June, 341.3kWh, 378.4kWh and
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396.8kWh in May, 321.0kWh, 376.9kWh and 317.2kWh in July. Due to the plum rain in
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June, cloudiness in June is larger than that in May or July, and the percentage of sunshine is
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low. So the solar radiation quantity and output in June are lower than those in May and July.
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The solar radiation quantity in January, 2008 is much lower than that in January in other
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years, which is due to the snow disaster. The corresponding output power is also low as snow
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covered the PV array. Table 3 shows the number of days for different weather (including
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rainy, cloudy and sunny) and the corresponding daily output in June in the three years. From
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table 3, it can be found the number of rainy days in year 2007, 2008 and 2009 is 8, 5 and 11.
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The corresponding daily outputs are 11.0kWh, 3.4kWh and 7.0kWh. The number of cloudy
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days in year 2007, 2008 and 2009 is 10, 19 and 13. The corresponding daily outputs are
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12.0kWh, 6.3kWh and 10.6kWh. The number of sunny days in year 2007, 2008 and 2009 is
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12, 6 and 6. The corresponding daily outputs are 10.8kWh, 13.1kWh and 12.5kWh. Daily
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output in sunny are almost larger than that in rainy days and cloudy days. Daily output in
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rainy days is the smallest.
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3.2.4 Daily output yield and solar radiation quantity Fig. 8, 9, 10and 11describe the change of daily output yield with solar irradiance. Fig.
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8and Fig. 10show the parameters in sunny days, and the parameters in other two figures are
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measured in rainy days. From Fig. 8, 9, 10and 11, we can see that the solar irradiance
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reaches the maximum value at noon, and the output yield then gets to the maximum value to
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match the solar irradiance. When the later solar irradiance decreases, the corresponding
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output yield reduces as well. The highest output yield may not match the maximum solar
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irradiance. It may be due to that the high temperature of the PV modules caused by the
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maximum solar irradiance reduces the voltage of PV modules. By comparing Fig. 8, 9, 10,
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11, it can be seen that the solar irradiance changes randomly in a larger range in rainy days,
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while it keeps relatively stable in sunny days. Output yield curve is under a similar character.
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Furthermore, solar irradiance in summer is higher than that in winter, so the output yield in
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summer is also larger than that in winter. The daily output yields in these four days are
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8.03(kWh/d), 13.34 (kWh/d), 2.85 (kWh/d) and 7.24 (kWh/d), respectively. The daily output
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yields in sunny days are much higher than those in rainy days. 3.2.5 The conversion efficiency of the DC/AC inverter
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Inverter is an important equipment to convert DC signals to AC signals, and its
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performance can directly influence the size and quality of the output electrical power. Fig.
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12shows the daily change trend of efficiency of inverters on a rainy day and a sunny day,
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respectively. The red curve represents the variety trend of the conversion efficiency of the
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inverter on 24th, June, a sunny day, while the blue one indexed the measured results on 17th,
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June, a rainy day. The two curves behave a similar characterization: in the start-up phase of
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the inverter, the efficiency value is only around 0.8, which is much lower than the conversion
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efficiency value during normal operation period. The conversion efficiency increases sharply
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while the solar irradiance increases. When the conversion efficiency reaches 0.94-0.95 at
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about 8 a.m., the inverter enters the normal operation period and then remains a relatively
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stable state. At half past 5 p.m., the conversion efficiency of the inverter decreases sharply,
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then the inverter stops work. By comparing the two curves, it can be found that on the rainy
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day, because of the influence of weather, the inverter may start intermittently. Though the
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conversion efficiency value has a relatively stable interval, on basis of the daily change trend
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of the conversion efficiency of the inverter on a sunny day, it still has an apparent oscillation,
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especially in the morning and the afternoon.The output characteristicof PV cells is nonlinear,
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temperature of PV cells and solar radiation can arousean oscillation of output power of PV
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cells.The input of inverter is the output of PV array, hence irradiance fluctuations can result
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in output instability of the inverter. Oscillation of inverter will greatly impact the grid and
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influence the quality ofpower.Low quality power is not allowed to connectto public grid, we
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must ensure that inverters operate efficiently to track the maximum power when solar
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radiation changes.Reference yield, array yield, final yield, capture loss and system loss
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Fig. 13shows the experimental results of daily reference yield, array yield and final
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yield of the overall PV array per month. The daily reference yield per month varies from
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2.38 h/d in January to 4.76 h/d in May, with an average value of 3.62 h/d. The daily array
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yield per month reaches a maximum value of ~4.17 h/d in May, and gets to a minimum value
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of ~2.01 h/d in January. The monthly average daily array yield is 3.01 h/d. The monthly
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average daily final yield ranges from the largest value of 4.00 h/d in May to the smallest
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value of 1.93 h/d in January. The monthly average daily final yield is 2.86 h/d. Fig. 13shows
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that the reference yield and the final yield in summer are higher than those in winter. Taking
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these many rainy days in June into account, the reference yield and final yield are low.
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Fig. 14 describes the daily final yield, capture loss and system lossof the overall PV
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array per month. As mentioned above, capture loss is aroused by the incomplete utilization
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of the available radiation in the array operation period. The capture loss may be due to the
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shadow, uneven irradiance, the temperature of PV modules, fouling and cleanliness of the
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surface of PV modules, and other factors. System loss takes place when the inverter
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converters DC power to AC power, so it is apparently affected by the match of the inverter’s
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maximum power tracking (MPPT) function [24,25]. The capture loss varies from 0.88 h/d in
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July to 0.37 h/d in January.The average value of the capture loss is ~0.61 h/d. The capture
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loss increases with the growth of solar irradiation, as a result, arousing an increased
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temperature of PV modules. The system loss reaches the maximum of 0.20 h/d in June, and
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get to the minimum of 0.08 h/d in January, being equal to 0.16 h/d inaverage value. Fig.
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15reveals the percentage of the final yield, capture loss and system loss, which are 80.33%,
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15.74% and 3.93%, respectively. The low value in system loss means that the PV array
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matches well with the inverter.
3.2.7 The array efficiency and system efficiency and performance ratio
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Fig. 16 shows the monthly array efficiency, system efficiency and performance ratio
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(PR) of the system. PR represents the ratio of the actual output power to the theoretical
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output power and could be affected by these factors such as the shadow, the temperature of
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these PV modules, the strength of the solar irradiation, the rain and wind velocity. The
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highest and smallest value of PR is 84.81% in May and 78.12% in July. PR of 78.12% in
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July may be aroused by the high temperature of these PV modules under the strong solar
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radiation, leading to the capture loss. The average monthly PR is 80.66%.
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The monthly system efficiency varies from 10.29% in January to 11.03% in April. The
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average value is 10.73%. The monthly array efficiency varies from 10.89% in January to
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11.67% in March. The average value is 11.34%. The average monthly array efficiency is
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higher than the average monthly system efficiency with a value of about 0.60%. This means
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the average inverter efficiency is about 94.60%. The system efficiency and array efficiency
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increase with decreasing ambient temperature, so system efficiency and array efficiency in
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winter are higher than those in summer. The conclusion supports the argument that the lower
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ambient temperature can lead to the larger efficiency. Due to the influence of snow covering
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on the solar panel, the solar cell efficiency in January was low. Especially in 2008, it reached
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the touch point because of the snow disaster in the southern region of China. An average
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efficiency value can be used to predict the generated electricity power. Efficiency
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discussions in this paper are based on the actual operation conditions from year 2007 to 2009.
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Array efficiency and system efficiency will increase with the improvement of manufacturing
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skills for solar cells and inverters. Therefore, we will do further research based on the
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present manufacturing skills in the future.Based on literatures [25], the daily irradiance can
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be classified into three types: overcast, intermediate and clear.The total irradiance of a day
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less than 2400 W/（m2・d）is called overcast, the daily irradiance in a clear day is with more
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than 5000 W/（m2・d）, and the daily irradiance higher than 2400 W/（m2・d）but less than
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5000 W/（m2・d）is defined as intermediate. The total days of the monitoring in three years
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are 1096, among which 341 days are overcast (106 days in the year 2007, 111 days in the
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year 2008, and 124 days in the year 2009); 426 days are intermediate (151 days in the year
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2007, 138 days in the year 2008 and 137 days in the year 2009); the other 329 days are clear
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(108 days in the year 2007, 117 days in the year 2008 and 104 days in the year 2009).
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Table 4and 5shows the daily PR and the daily system efficiency for different weather
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from the year 2007 to 2009 with an average value, maximum value and minimum value,
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respectively. The minimum PR and system efficiency for overcast on 4th, July, 2007 are
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23.75% and 3.20%, respectively. The relative output power is ~0.73 kWh and is quite low.
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The result was due to the heavy rainstorm in that day. The PR and system efficiency for
313
intermediate with the value of 5.31% and 0.72% took place on 25th, May, 2007 which was
314
due to the system fault. The minimum PR of 1.44% and system efficiency of 0.19% for
315
overcast occurred on 2nd February, 2008, because the snow covered the PV array and
316
prevented its operation. The minimum PR of 23.05% and system efficiency of 3.10% for 16
Page 16 of 43
clear happened on 3rd February, 2008, due to the snow as well. The minimum PR of 34.83%
318
and system efficiency of 4.68% for overcast happened on 21th September, 2009, since the
319
daily solar radiation quantity was extremely low with a value of ~414.62Wh/m2. The daily
320
performance ratio can reach 94.04%, and the corresponding system efficiency can get to
321
12.63% for overcast in 2009.
cr
ip t
317
The system efficiencies for overcast, intermediate and clearare 10.71%, 10.85% and
323
10.61%, respectively. Compared with Table 3 and Table 4, the daily system efficiency for
324
clear are lower than the other two cases.It rains every month and about 70 days in a year, the
325
research results show that the rain can clean the dust on surface of solar cells. But
326
automobile traffic in Shanghai area is large, its exhaust oil particles float in the air.The oil
327
particles absorb the dust in the air and then attach to the solar glass surface, forming a layer
328
of transparent viscous lossy film, this layer of film is difficult toclean by rain. Therefore,
329
when designing a PV system in Shanghai, we can make little contribution to clean the dust
330
on cell surface, but pay more attention to the cleaning of oil particles.
332
an
M
d
te
Ac ce p
331
us
322
3.2.8 Comparison of PV systems in different countries In order to have clear picture of performance of the solar powerplant, performance
333
parameters evaluated for the Chinese solar PVplant are compared with the reported
334
performance parameters ofsolar PV plants at various locations as shown in Table 6.The
335
annual average daily final yield of the system is lower than those in Singaporeand Greece,
336
but higher than those in other countries. PR of the system is lower than that in Singapore and
337
Ireland,while higher than those in other locations.The comparative study provides an
338
insightto the performance of the poly crystalline PV technology under Chinese climatic 17
Page 17 of 43
339
conditions and indicatesthe suitability of solar power generation in China.
340
4. Conclusion
342
The performance monitoring is analyzed based on the measured data from year 2007 to
ip t
341
year 2009. The following conclusions can be made:
1. The lowest and the highest temperatures in the three years are -6.26oC and 41.44oC,
344
respectively. Temperature cannot be neglected when designing a PV system.We can use
345
these data to predict the output and the surface temperature of PV modules.
us
cr
343
2. The annual predicted and measured energy output on 25゜tilt are 3220.21 kWh and
347
3189.13 kWh. The results show the predicted data matches well with the measured data
348
when measured solar radiation data of the site is used as input to the simulation software.
349
The results can provide a guide for the design of optimized tilt angle of the similar PV
350
systems in Shanghai, China. For a similar PV system in Shanghai, China, the performances
351
of PV arrays and inverter are determined, and the effect of solar energy resources and
352
environmental temperature on PV systems has also been determined. Hence the energy
353
output is affected by tilt angle of PV arrays. Combine the performances of PV arrays and
354
inverter, loss of line, and some other factors, the data of per kilowatt output of the system
355
can provide a qualitative reference for the design of PV systems in Shanghai, China.
356
However, the accurated optimal title angle of PV systems still needs to be quantitative
357
calculated. The measured solar radiation data of the sitecan be used to predict energy output
358
when designing a PV system. The average annual output yield is 3189 KWh and average
359
daily output yield is 8.74 kWh. The output yield and solar radiation quantity in June is small
360
due to the plum rain in Shanghai. when designing a PV system in Shanghai, we can make
Ac ce p
te
d
M
an
346
18
Page 18 of 43
361
little contribution to clean the dust on cell surface, but pay more attention to the cleaning of
362
oil particles. 3. The daily reference yield, array yield, final yield, capture loss and system loss of the
364
overall PV array per month are 3.62 h/d, 3.01 h/d, 2.86 h/d, 0.61 h/d and 0.16 h/d,
365
respectively. The low system loss means the PV array matches well with the inverter. The
366
monthly average performance ratio, system efficiency and array efficiency are 80.66%,
367
10.73% and 11.34%, respectively. The difference between system efficiency and array
368
efficiency is ~0.60% and very low, which means the average efficiency of the inverter is
369
about 94.6%. The daily conversion efficiency of the inverter in a rainy day and a sunny day
370
is different. The daily conversion efficiencyof the inverter behaves large fluctuations in a
371
rainy day and keeps relatively stable in a sunny day. The daily output yield and solar
372
radiation quantity in summer and in winter with a rainy day and a sunny day are also
373
compared. Daily output yield in a sunny day is higher than that in a rainy day, and the daily
374
output yield in summer is higher than that in winter. The system efficiencies for overcast,
375
intermediate and clear are 10.71%, 10.85% and 10.61%, respectively.The parameters of the
376
daily system efficiency for clear are lower than those for overcast or intermediate.
cr
us
an
M
d
te
Ac ce p
377
ip t
363
4. The measured average annual PR of the system is 80.66%and final yield is 2.86 h/d
378
after three years of continuous operation, indicating the vast solar potential in China needs to
379
be utilized for solar power generation. The study provides an insightto the performance of
380
the poly crystalline PV technology under Chinese climatic conditions.
381 382
Acknowledgements 19
Page 19 of 43
This work is supported by Natural Science Foundation of China (Nos. 11374204,
384
51034010), “Shu Guang” project of Shanghai Municipal Education Commission and
385
Shanghai Education Development Foundation (No. 13SG52), and Projects of Science and
386
Technology Commission of Shanghai Municipality (Nos. 12JC1404400, 11160500700).
387
Nomenclature
388
YA: the array yield YA (h/d)
389
YF: the final yield (h/d)
390
YR: the reference yield (h/d)
391
P0: the installed capacity (kW)
392
EDC: the DC energy output per day of array (kWh/d)
393
EAC: the AC output energy that connects to the grid per day (kWh/d)
394
HId: the total daily in-plane irradiation by the modules (kWh/m2.d)
395
GI: the reference in-plane irradiance (kWh/m2.d)
396
LC: the array capture losses (h/d)
397
LS: the system losses (h/d)
398
PR: the performance ratio (%)
399
ηAmean: the array efficiency (%)
400
ηsystem: the system efficiency (%)
401
EDC,total: the total DC energy output per day of array (kWh/d)
402
EAC,total: the total AC output energy that connects to the grid per day (kWh/d)
Ac ce p
te
d
M
an
us
cr
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383
403
20
Page 20 of 43
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[1] H. Lund, A. Marszal, P. Heiselberg, Zero energy buildings and mismatch compensation
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Factors. Energy and Buildings 2011;43 (7) : 1646–1654.
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[2] C. Penga, Y. Huang, Z. Wu. Building-integrated photovoltaics (BIPV) in architectural
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[3] B. Parida, S. Iniyan, R. Goic. A review of solar photovoltaic technologies. Renewable
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[4] J.H. Yoon, J.H. Song, S.J. Lee. Practical application of building integrated photovoltaic
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[5] L.Y. Seng, G. Lalchand, G.M.S. Lin. Economical environmental and technical analysis
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[7] G.K. Singh. Solar power generation by PV (photovoltaic) technology: A review. Energy
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[8] C. D. Zomer, M. R. Costa, A. Nobre, R. Rüther. Performance compromises of
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building-integrated and building-applied photovoltaics (BIPV and BAPV) in Brazilian
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[9] Q. Zhu，L. Si，T. Jiang.Economical and environmental analysis of photovoltaic systems
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with different installation styles.ActaEnergiaSolarsSinica 2012;33:24-29
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[10] A.G. Hestnes, Building integration of solar energy systems. Solar Energy 1999; 67 (4-6):
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[11] D. Paul, D.N. Mandal, D. Mukherjee, S.R. BhadraChaudhuri, Optimization of
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Renewable Energy 2010;35 (10): 2182-2191.
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[12] The standard GB/T 19064-2003 Specification and Test Method for Residential PV
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[13] The standard GB/T 19939-2005 Technical Requirements for Grid Connection of PV
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System.(http://www.doc88.com/p-313702128377.html)
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[14] International Electrotechnical Commission. IEC 61724, photovoltaic system
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performance monitoring e guidelines for measurement, data exchange and analysis. 1st ed.
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Geneva, IEC: International Electrotechnical Commission; 1998.
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[15] V. Sharma, A. Kumar, O.S. Sastry, S.S. Chandel. Performance assessment of different
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solar photovoltaic technologies under similar outdoor conditions. Energy 2013;58:511-518.
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[16] V. Sharma, S.S. Chandel. Performance analysis of a 190 kWp grid interactive solar
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photovoltaic power plant in India. Energy 2013;55:476-485.
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[17] B. K. Koyunbabaa, Z. Yilmazb, K. Ulgena. An approach for energy modeling of a
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building integrated photovoltaic (BIPV) Trombe wall system. Energy and Buildings
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2013;67:680–688.
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[18] U. Jahn, W. Nasse. Operational performance of grid-connected PV systems on buildings
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in Germany.ProgPhotovolt: Res Appl 2004;12:441-8.
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[19] L. M. Ayompe, A. Duffy, S. J. McCormack, M. Conlon. Measured performance of a
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1.72 kW rooftop grid connected photovoltaic system in Ireland. Energy Convers Manage
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2011;52:816-25.
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[20] J. Mondol, Y. Yohanis, M. Smyth, B. Norton. Long term performance analysis of a grid
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Manage2006;47:2925-47.
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[21] E. Kymakis, S. Kalykakis, T. Papazoglou. Performance analysis of a grid connected
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photovoltaic park on the island of Crete. Energy Convers Manage 2009;50(3):433-8.
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[22] B. Quesada, C. Sáchez, J. Canada, R. Royo, J. Payá. Experimental results and
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simulation with TRNSYS of a 7.2 kWp grid-connected photovoltaic system. Applied Energy
456
2011; 88 :1772-1783.
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[23] C. Zhao, R. Cui. Technical research and development of solar energy buildingmaterials
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(I): Thermal investigation of integrated PV roof．ActaEnergiaSolarsSinica 2003; 24:352-356.
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[24] R. Eke, A. Senturk. Monitoring the performance of single and triple junction amorphous
460
silicon modules in two building integrated photovoltaic (BIPV) installations. Applied Energy
461
2013; 109:154-162.
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[25] S. Wittkopf, S. Valliappan, L. Liu, K. Ang, S. Cheng. Analytical performance
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monitoring of a 142.5 kWp grid-connected rooftop BIPV system in Singapore. Renewable
464
Energy 2012;47:9-20.
465
[26] S.M.Pietruszko, M.Gradzki. Performance of a grid connected small PV systemin Poland.
466
Applied Energy 2003;74:84-177.
467
[27] B. Decker, U. Jahn. Performance of 170 grid connected PV plants in NorthernGermany
468
-analysis of yields and optimization potentials. Solar Energy1997;59:33-127.
469
Caption of figures:
in
Northern
Ireland.
Energy
Convers
ip t
system
Ac ce p
te
d
M
an
us
cr
photovoltaic
23
Page 23 of 43
Fig. 1.Environment conditions of the BAPV system.
471
Fig. 2.General principle review.
472
Fig. 3.The monthly lowest temperature.
473
Fig. 4.The monthly highest temperature.
474
Fig. 5.The average monthly temperature.
475
Fig. 6. Variation of monthly and yearly global horizontal, actual measured and predicted
476
solar radiation.
477
Fig. 7.Variation of monthly and yearly actual measured and predicted energy output.
478
Fig. 8. Daily output yield with solar irradiance on 17th, June (rainy).
479
Fig. 9. Daily output yield with solar irradiance on 24th, June (sunny).
480
Fig. 10. Daily output yield with solar irradiance on 10th, December (rainy).
481
Fig. 11. Daily output yield with solar irradiance on 14th, December (sunny).
482
Fig. 12.The daily change trend of conversion efficiency of inverters.
483
Fig. 13.Daily reference yield, array yield and final yield of the overall PV array.
484
Fig. 14. Daily reference yield, capture loss and system loss of the overall PV array.
485
Fig. 15.Daily percentage of reference yield, capture loss and system loss of the overall PV
487
cr
us
an
M
d
te
Ac ce p
486
ip t
470
array.
Fig. 16.The monthly array efficiency, system efficiency and performance ratio.
24
Page 24 of 43
488
Caption of tables:
490
Table 1. Monthly amount of solar radiationquantity，/kWh˙m-2.
491
Table 2. Monthly amount of solar radiationquantity，/kWh˙m-2.
492
Table 3.Number of days and daily output for different weather from year 2007 to 2009.
493
Table 4. Daily PR for different weather conditions from the year 2007 to 2009.
494
Table 5.Daily system efficiency of different weather conditions from year 2007 to 2009.
495
Table 6.Performance study comparison of PV systems in different countries.
an
us
cr
ip t
489
Ac ce p
te
d
M
496
25
Page 25 of 43
Table 1. Monthly amount of solar radiation quantity，/kWh˙m-2. Year 2007 2008 2009
Jan. 73 55.1 93.6
Feb. 101.6 119.9 51.6
Mar. 116.5 134.5 102.4
Apr. 131.1 114 147.6
May. 149.5 159.9 133.7
Jun. 95.6 88 120.9
Jul. 140.3 160.6 133.4
Aug. 152.8 131.9 98.4
Sep. 101.8 110.1 98.0
Oct. 114.9 96 130.2
Table 2. Monthly output yield，/kWh. Jan. 174.8 127.6 235.0
Feb. 236.5 279.5 128.1
Mar. 280.5 329.7 256.3
Apr. 307.9 277.9 365.6
May. 341.3 378.4 396.8
Jun. 227.9 215.9 291.4
Dec. 71 103.6 87.2
Sep. 239.7 263.8 234.4
Oct. 271.7 232.7 310.4
Nov. 236.1 211.3 168.5
Dec. 169.5 241.3 216.9
Table 3 Number of days and daily output for different weather from year 2007 to 2009
2007 2008 2009
rainy
cloudy
days
daily output(kWh)
days
8 5 11
11.0 3.4 7.0
10 19 13
sunny
daily output(kWh)
days
daily output(kWh)
12.0 6.3 10.6
12 6 6
10.8 13.1 12.5
te
501
M
year
d
500
Aug. 351.9 315.4 237.2
an
499
Jul. 321.0 376.9 317.2
us
Year 2007 2008 2009
cr
497 498
Nov. 97.5 85.3 67.7
ip t
496
Table 4. Daily PR of different weather from year 2007 to 2009.
Ac ce p
502 Weather PR(%)
Year 2007
Overcast
Intermediate
Year 2008
Clear
Overcast
Intermediate
Year 2009 Clear
Overcast
Intermediate
Clear
Average
79.05
79.59
77.55
77.68
80.80
79.38
82.28
82.31
80.00
Minimum
23.75
5.31
70.55
1.44
23.05
74.24
34.83
69.82
74.24
Maximum
89.72
86.06
82.98
91.13
89.28
84.83
94.04
90.86
86.20
503 504 505
26
Page 26 of 43
Table 5. Daily system efficiency of different weather conditions from year 2007 to 2009. Weather System efficiency(%)
Year 2007 Overcast
Intermediate
Year 2008 Clear
Overcast
Year 2009
Intermediate
Clear
Overcast
10.62
10.63
10.41
10.45
10.85
10.66
11.05
Minimum
3.20
0.72
9.48
0.19
3.10
9.97
4.68
Maximum
12.05
11.56
11.15
10.64
11.99
11.39
Clear
11.07
10.74
10.10
9.97
12.20
11.58
cr
Average
Intermediate
ip t
506
us
12.63
507
Rated
Type of PV
Output
Final
M
Location
an
Table 6 Performance study comparison of PV systems in different countries
508
System PR(%)
Ref
technology
yield(kWh/kp)
yield(kWh/kW・d)
efficiency(%)
India
190
Polycrystalline Si
812.76
2.23
12.3
74
[16]
Ireland
1.72
Monocrystalline Si
885.1
2.4
12.6
81.5
[19]
Ac ce p
te
d
capacity(kWp)
Greece
171.36
Polycrystalline Si
1336.4
1.96-5.07
-
67.3
[21]
Singapore
142.5
Polycrystalline Si
1018.95
3.12
11.2
81
[25]
Poland
1
Amorphous-Si
830
2.3
4.0-5.0
60.8
[26]
Germany
-
Polycrystalline Si
680
1.9
-
66.5
[27]
China
3
Polycrystalline Si
1063.04
2.86
10.99
80.6
Present study
509 510 27
Page 27 of 43
Ac ce pt e
d
M
an
us
cr
ip t
Figure(s)
Page 28 of 43
an
us
cr
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Figure(s)
Ac ce pt e
d
M
Page 29 of 43
Figure(s)
30
ć
2
007 year 2
5
year 2008
20
year 2009
1
5
5
Ͳ5
2
3
4
5
6 7 month
10
8
9
10
11
us
Ͳ
1
12
cr
0
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10
Ac ce pt e
d
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an
Page 30 of 43
Figure(s)
45
year 2007
ć
40 35
year 2008
30
year 2009
25 20
ip t
15 10
1
2
3
4
5
6 7 month
8
9
10
11
12
us
0
cr
5
Ac ce pt e
d
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an
Page 31 of 43
Figure(s)
ć
35 year 2007
30
year 2008
25
year 2009
20 15
ip t
10
1
2
3
4
5
6 7 month
8
9
10
11
us
0
cr
5
12
Ac ce pt e
d
M
an
Page 32 of 43
Ac
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ed
M
an
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Figure(s)
Page 33 of 43
Figure(s)
u t pu oyg re n
hl t
ye n
mo
m o n thl
yen e r gyou t pu t
pr edi
c t ed
p t yea r l yen e r gyou t u
m ae sur ed
m o n thl
yen e r gyou t pu t
m ae sur ed
p t yea r l yen e r gyou t u 3230
h) 3220 W k( t 3210 u t pu 3200 yo g
1
2
3
4
5
6
7
8
9
10
11
Ac
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M
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12
3190 ren e 3180 l y ar 3170 ye
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400 350 300 250 200 150 100 50 0
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pr edi
Page 34 of 43
Figure(s)
900 .000 os l
ar
ir
ad i
an
2)
W/ m
ec (
tp
ou
u
t
k W (
)
2.500
800 .000
)
2.000
700 .000
2
/ m W
600 .000 1.500
( 500 .000
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i an
k(
400 .000
d
1.000 a
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300 .000
t u tp
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s
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uo
l ra o
)
200 .000
0 .500
100 .000
0 .000 0 0
54
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15
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54
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15
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06 :
07 :
08 :
09 :
09 :
0 1:
1 1:
0 0 21 : ti
54
0 3
15
0 0
54
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15
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3 1:
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15 :
15 :
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17 :
em
0 0
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Page 35 of 43
Figure(s)
1200 .000 os l
ar
ir
ad i
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2)
W/ m
ec (
tp
ou
u
t
k W (
2.500 )
1000 .000 2.000
)
2
/ m W
800 .000 1.500
( ce
k(
600 .000 i an
d
1.000 a
W
t u tp uo
400 .000
i r r
)
l ra
ip t
o s
0 .500
200 .000
0 .000
0 .000
0
0
06 :
06 :
4
0
0
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0 0
70 :
80 :
80 :
4
0
0
2
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09 :
0 1:
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4
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2
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1 1:
2 1:
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Page 36 of 43
Figure(s)
350 .000
0 .4 os l
ar
ir
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2)
W/ m
ec (
tp
ou
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t (
k W )
0 .35
300 .000 ) 2
0 .3 250 .000
/ m 0 .25 (W
) W
200 .000
ec
0 .2 i na d
t u tp
150 .000 0 .15
a i r r
k(
uo
100 .000
l ar
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0 .1
os 50 .000
0 .05
0 .000
0
4
70 :
80 :
80 :
0
0
06 :
06 :
4
0
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2
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09 :
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4
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1 1:
2 1:
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13 :
4 1:
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4
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51 :
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Page 37 of 43
Figure(s)
so l
ar
ri
da i
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2)
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ec (
tp
ou
u
t
(
k W
)
600 .000
1.6
1.4 )
500 .000 2
1.2 / m W
400 .000 )
1
( ec 300 .000
0 .8
i na d
(k
W
t u
0 .6
a
ou
r
l ar o s
tp
ip t
i r
200 .000 0 .4
100 .000
0 .2
0 .000
0
4
70 :
80 :
80 :
0
0
06 :
06 :
4
0
0
0
2
0 0
09 :
0 1:
0 1:
4
0
0
2
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1 1:
2 1:
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ti
4
0
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2
0 0
13 :
4 1:
4 1:
4
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0 0
40
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51 :
16 :
61 :
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18 :
me
4
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0
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Page 38 of 43
Figure(s)
1.2
y d a y o n 17t h , J u n e s u n y d a y o n 24t h , J u n e r a in
1 0.8 0.6
0.2
53 60 :
0 1 07 :
54 70 :
0 2
08 :
5 5 80 :
0 3
09 :
05 0 1:
04 0 1:
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f in a l y i e l d h( / d ) ac p ture l oss h( / d ) sy s tem l oss h( / d ) ( W h / m 2) m o n th l y sol a r ra d i a tio n k ( W )h m o n th l y o utputy i e l d k
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final y iela(h/d)
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S0378-7788(14)01042-1 http://dx.doi.org/doi:10.1016/j.enbuild.2014.11.073 ENB 5542
To appear in:
ENB
Received date: Revised date: Accepted date:
29-8-2014 26-11-2014 29-11-2014
Please cite this article as: X. Wu, Y. Liu, J. Xu, W. Lei, X. Si, W. Du, C. Zhao, Y. Zhong, L. Peng, J. Lin, Monitoring the performance of the building attached photovoltaic (BAPV) system in Shanghai, Energy and Buildings (2014), http://dx.doi.org/10.1016/j.enbuild.2014.11.073 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Highlights
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1. The first grid-connected roof-mounted BAPV system in Shanghai, China.
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2. Performance like performance ratio is analyzed based on the monitoring data.
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3. The conversion efficiency of inverter for different weather is assessed.
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4. Environmental effects are assessed based on parameters like energy consumption.
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5. Solar radiation and energy output are predicted using PVSYST.
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Monitoring the performance of the building attached photovoltaic (BAPV)
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system in Shanghai
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Xinfang Wu1, Yongsheng Liu1*, Juan Xu1, Wei Lei1, Xiaodong Si1, Wenlong Du1, Chunjiang
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Zhao1, Yunbo Zhong2, Lin Peng1, Jia Lin1
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1. Institute of Solar Energy, Shanghai University of Electric Power, Shanghai 200090,
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2. School of Materials Science and Engineering, Shanghai University, Shanghai 200444, China
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Abstract：
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This work presents the performance monitoring of thefirst grid-connected roof-mounted
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building attached photovoltaic (BAPV) system located in Shanghai, China. The system,
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whose capacity is ~2992 Wp, began to operate in late December, 2006. The photovoltaic
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performance is monitored and analyzed from year 2007 to 2009. The average annual output
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and average daily output are 3189.13 kWh and 8.74 kWh.The predicted yearly energy output
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is 3220.21 kWh by software of PVSYST. The annual global solar radiation on horizontal,
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predicted and measured on tilt are 1315.15 kWh/m2,1320.24 kWh/m2 and 1323.09 kWh/m2,
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respectively. The daily reference yield, array yield and final yield are 3.62 h/d, 3.01 h/d and
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2.86 h/d, respectively. The average daily capture loss and system loss of the overall BAPV
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array per month are very low and ~0.61 h/d and 0.16 h/d in value, respectively, which means
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that the BAPV array matches well with the inverters. The average monthly performance ratio,
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* Corresponding author. Tel: +86-21-35303922; Fax:+86-21-68029219; E-mail address: [email protected] (Y. Liu)
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system efficiency and array efficiency are 80.66%, 10.73% and 11.34%, respectively. The
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difference between system efficiency and array efficiency is ~0.60%, which means the
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average inverter efficiency is about 94.60%.
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Keywords:BAPV, grid-connected, monitoring performance，PVSYST
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1. Introduction The current world faces some significant challenges, including climate change and
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energy demand. Climate change and global warming are mainly aroused bygreenhouse gases
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such as carbon dioxide, which is generated by the burning of the conventional energy such as
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fossil fuel [1]. As a kind of renewable energy, solar energy has many advantages while
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traditional energy rarely has, such as clean, numerous.Moreover, compared to other
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renewable energy like wind, tidal, nuclear, biomass, and others, solar energy is abundant and
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inexhaustible. Sunlight power that reaches the Earth is much more than the world’s present
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energy consumption. So, solar energy should be fully utilized [2-4].
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At present, the buildings in most cities are high-rise, and the roof area is not enough for
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stand-alone photovoltaic (PV) system installation [5]. To make the roofs of the buildings to
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be fully used, building photovoltaic power system, as a novel concept to use solar energy,
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has been proposed. Based on different installation ways, it can be divided into two models,
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the building attached photovoltaic (BAPV) and the building integrated photovoltaic (BIPV).
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For BIPV system, PV array is placed on the top of a building to replace the traditional
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materials. While for BAPV systems, PV modules are installed on the surface of building roof
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with a certain inclined angle and they are supported by some superstructure [6-9]. Both of
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them are space-saving by occupying the roofs of the building, however, the expenses of the
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installations are quite enormous [10-11].
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The total installation capacity of PV systems in year 2011 is just 3.6 GW, which only
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occupies for 0.1% of the total installation capacity of power stations in China. Chinese 4
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government proposed enlarging installation capacity of PV systems to 21 GW, which is 5
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times the installation capacity in year 2011. Furthermore, yearly solar radiation quantity in
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Shanghai, China (a latitude of 31°12´ north and a longitude of 121°24´) is about 1300
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Wh/m2. In order to use building roofs and solar power,a plan of approximately one hundred
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thousand buildings roofs with a total area of 3 million square meters can be used to generate
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power through solar energy isproposed by the government of Shanghai City. This paper
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illustrates the first grid-connected BAPV system in Shanghai. As the performance of a PV
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system can be influenced by many factors, such asshade, ambient temperature, the surface
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temperature of PV modules, solar irradiance, and others, the indicators including final yield,
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performance ratio (PR), outputyields, capture loss, system loss and some other parameters
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are chosen to evaluate the performance of the system.
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2. Description of the BAPV system
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In addition, a number of simulation softwares are developed to estimate theperformance
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of a solar power plant to assist the system designersand installers.Theselection of the
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software should be based on the judgment that howclosely the predicted and measured data
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agree. PVSYST is mostly used forstudying, sizing and performance analysis of photovoltaic
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standalone, grid connected and water pumping systems. PVSYST mainlyrequires
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meteorological data (global horizontal solar radiation,ambient temperature), electrical and
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mechanical specifications ofthe installed PV modules, as inputs. In this work PVSYT is used
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to predict thetotal monthly and yearly energy output of a PV system with an installed
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capacity of 3 kW. The measured global solar radiationon solar panel and monthly average
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ambient temperaturefrom year 2007 to 2009 are used as input data.Investigation on the
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performance of the PV system has been analyzed to provide technical reference for the PV
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system.The BAPV system is situated on the roof of a residential building which locates in
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Minhang district, Shanghai City, China. It is the first practical application of a home-based
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grid-connected roof-mounted BAPV system in China.Ithas great significance to the research
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of BAPV systems and promotes the development of Chinese renewable energy. The BAPV
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system made of polycrystalline silicon photovoltaic modules was installed on the roof in
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order to provide electricity to the building. The criterion for the PV system installation and
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experiment [12] is based on the Standard GB/T 19064-2003 Specification and Test Method
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for Residential PV System of China. The relevant procedural requirements of PV system
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components are offered by the technical standard as to guide PV system construction.
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Standard GB/T 19939-2005 Technical Requirements for Grid Connection of PV System of
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China is adopted to determine grid-connected method of PV system and technical
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requirements of PV system components [13].
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The PV system started to operate in December 2006, and an automated data monitoring
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system has been running for data acquisition, including PV ambient temperature, solar
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radiation and electricity generated data. The PV system is composed of 22 pieces of
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polycrystalline silicon photovoltaic cells with a total area of 22 m2. Though the optimal tilt
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angle in Shanghai region is 23°, considering factors of ash deposition, water conveyance,
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and other reasons, the system is designed with an inclined angle of 25°facing south. No
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buildings surrounding, the PV array is covered without any shadow, as shown in Fig. 1. On
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the standard condition, the open-circuit voltage, operating voltage, short-circuit current and
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operating current of the BAPV system are DC535 V, DC427 V, DC7.43 A and DC7.01 A 6
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respectively. The total installed capacity is 2992 Wp. The system can be considered as a
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standard grid connected photovoltaic system that includes a solar PV array, an inverter, an
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AC power distributing cabinet, and a data monitor. Based on the average solar irradiance of
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700~800 W/m2 in summer in Shanghai, even the solar irradiance is fully used, the output
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power is less than 2.5 kW. Hence the output power of the inverter of the system is designed
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to be 2.5 kW. The actual operation results show that the inverter could match with the PV
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arrays. The schematic of BAPV system is illustrated in Fig. 2.
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3. Performance analysis
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3.1 Performance criterion
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In order to analyze the performance and reliability of the system, array yields, final
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yields, reference yields, array efficiencies, system efficiencies, capture losses, system losses,
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and performance ratio (PR) are calculated based on the IEC 61724 standard [14-17].
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The array yield YAis the proportion of EDC to P0, while the final yield YF is the ratio of
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EAC to P0,where EDC is the DC energy output per day of array, P0 is the installed capacity,
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and EAC is the AC output energy that connects to the grid per day.
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YA =
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YF =
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The reference yield YRis the ratio of HId to GI. HId is the daily total in-plane irradiation
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by the modules. GI represents the reference in-plane irradiance.
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YR
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The array capture loss LC is the difference between YR and YA. It is caused by
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incomplete utilization of the available radiation in the array operation period.
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LC= YR – YA (4)
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to convert the DC signals to AC signals.
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LS= YA –YF
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The performance ratio (PR) is the ratio of YF to YR, which represents the relationship
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between the actual and theoretical energy output of the PV system. The loss may be caused
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by array temperature, incomplete utilization of the irradiation, and system component
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inefficiencies or failures.We can use PR to monitor the status of the PV power station over a
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long operation time and compare the performance different PV systems located at the
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different geographical areas [18–21].
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PR=
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The array efficiency Amean and system efficiency system are mean and net energy
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conversion efficiencies of the PV array, respectively, being defined as the ratio of the daily
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array DC output energy or net AC output energy to the corresponding radiation quantity per
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unit area of the PV array.
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Amean =
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system =
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3.2 Analysis results An automated data monitoring system has been installed to measure the ambient 8
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temperature, solar radiation and output power since these parameters are important to design
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the system. The following data were recorded from year 2007 to 2009 for this study. With
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these data, we can assess the feasibility and performance of the system.
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3.2.1 The data of temperature
Temperature affects system performance strongly. The temperature coefficient describes
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the behavior of the electrical characteristics of the PV with the operating temperature and
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hence the thermal effects. The temperature indicator was installed beside the PV array to
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measure the PV ambient tempertaure. The highest and lowest temperatures are closely
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related to the over-voltage and under-voltage of the system and the capacity resisting heat
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and cold of the equipment, and can directly affect the output voltage and power of the PV
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system [22] and also affect the operation performance of the DC/AC inverter. The highest
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and lowest temperatures can also affect the charging voltage of the stand-alone system with
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battery [23]. It should be noted that the solar radiation can result in the temperature risingand
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the output voltage of the PV array unstable. So the highest and lowest temperatures are
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selected as the essential reference for the PV system design. Fig. 3 and Fig. 4 show the
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monthly lowest and highest temperatures from year 2007 to 2009. Fig. 5 describes the
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monthly average temperature in year 2007, 2008 and 2009. The lowest temperature in the
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three years is -6.26oC in January 2009, while the highest temperature is 41.44oC in July 2007.
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The average monthly temperature in July is 30oC, which is relatively high. We can use these
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data to predict the output yield and the surface temperature of PV modules.
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3.2.2 The monthly solar radiation The solar radiation is one of the factors that disturb the grid. The solar radiation 9
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instrument was installed on the same plane with the PV array to measure solar radiation. The
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solar radiation quantity is the reference for planning the monthly power generation and
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laying the power dispatching plan of the grid departments. The optimal instantaneous solar
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radiation is linked to the capacity of these electrical components, the resistance to overload
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and the design of withstand voltage of the electrical components in the PV power generation
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system. Table 1 shows the monthly solar radiation quantity from year 2007 to 2009.
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The values of the annual solar radiation quantityin 2007, 2008 and 2009 are 1345.6
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kWh/m2, 1358.9 kWh/m2 and 1264.8 kWh/m2, respectively. The corresponding annual solar
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radiation in three years is 1323.09kWh/m2. We can see that the solar radiation quantity in
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June is lowerthan that in May and July. This phenomenon is ascribed to the plum rain in June
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in Shanghai.
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The variation of monthly and yearly global horizontal, actual measured and predicted
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solar radiation in the plane of array is shown in Fig.6.The measured monthly global solar
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radiation on horizontal surfacevaries from 56.52 kWh/m2 in January to 162.31 kWh/m2 in
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May.The predicted monthly solar radiation at 25゜ tilt varies from 73.78 kWh/m2 in January
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to 142.60 kWh/m2 in July, while the actual measuredmonthly solar radiation at 25゜ tilt
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varies from56.52 kWh/m2 in January to 147.71 kWh/m2 in May. The annualglobal solar
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radiation on horizontal,predicted and measured on tilt are 1315.15 kWh/m2,1320.24
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kWh/m2and 1323.09 kWh/m2, respectively. The yearly predicted and actual measured solar
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radiation on tilt are quite close.
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3.2.3 The monthly output yield of the system The monthly amount of the generation power from year 2007 to year 2009 of the BAPV 10
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system is given in table 2. The monthly output yields of the system are 3159.0 kW.h in
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2007, 3250.5 KW.h in 2008 and 3157.8 KW.h in 2009, respectively. Total output in three
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years is 9567.4 kWh, per kilowatt output is 3189.1 kWh, and the average daily output is 8.7
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kWh.
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The variation of monthly and yearly actual measured and predicted energy output is
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shown in Figs.7. The predicted monthly energy output varies from 180.11 kWh in January to
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348.13kWh in July. The measured monthly energy outputchanges from179.13 kWh in
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January to372.16 kWh in May. The annual predicted and measured energy outputsare
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3220.21 kWh and 3189.13kWh. The results show the predicted data matches well with
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themeasured datawhen measured solar radiationdata of the site is used as input to the
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simulation software.
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From table 1 and table 2, it can be found the monthly output yield increases with a rise
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of solar radiation quantity. Both of the two parameters are higher in summer than those in
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winter. The solar radiation quantitiesin year 2007, 2008, 2009 are 95.6kWh/m2,
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88.0kWh/m2and 120.9kWh/m2in June, 149.5kWh/m2, 159.9kWh/m2 and 133.7kWh/m2in
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May, 140.3kWh/m2, 160.6kWh/m2 and 133.4kWh/m2in July. The output in year 2007, 2008,
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2009 are 227.9kWh, 215.9kWh and 291.4kWh in June, 341.3kWh, 378.4kWh and
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396.8kWh in May, 321.0kWh, 376.9kWh and 317.2kWh in July. Due to the plum rain in
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June, cloudiness in June is larger than that in May or July, and the percentage of sunshine is
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low. So the solar radiation quantity and output in June are lower than those in May and July.
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The solar radiation quantity in January, 2008 is much lower than that in January in other
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years, which is due to the snow disaster. The corresponding output power is also low as snow
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covered the PV array. Table 3 shows the number of days for different weather (including
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rainy, cloudy and sunny) and the corresponding daily output in June in the three years. From
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table 3, it can be found the number of rainy days in year 2007, 2008 and 2009 is 8, 5 and 11.
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The corresponding daily outputs are 11.0kWh, 3.4kWh and 7.0kWh. The number of cloudy
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days in year 2007, 2008 and 2009 is 10, 19 and 13. The corresponding daily outputs are
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12.0kWh, 6.3kWh and 10.6kWh. The number of sunny days in year 2007, 2008 and 2009 is
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12, 6 and 6. The corresponding daily outputs are 10.8kWh, 13.1kWh and 12.5kWh. Daily
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output in sunny are almost larger than that in rainy days and cloudy days. Daily output in
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rainy days is the smallest.
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3.2.4 Daily output yield and solar radiation quantity Fig. 8, 9, 10and 11describe the change of daily output yield with solar irradiance. Fig.
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8and Fig. 10show the parameters in sunny days, and the parameters in other two figures are
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measured in rainy days. From Fig. 8, 9, 10and 11, we can see that the solar irradiance
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reaches the maximum value at noon, and the output yield then gets to the maximum value to
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match the solar irradiance. When the later solar irradiance decreases, the corresponding
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output yield reduces as well. The highest output yield may not match the maximum solar
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irradiance. It may be due to that the high temperature of the PV modules caused by the
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maximum solar irradiance reduces the voltage of PV modules. By comparing Fig. 8, 9, 10,
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11, it can be seen that the solar irradiance changes randomly in a larger range in rainy days,
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while it keeps relatively stable in sunny days. Output yield curve is under a similar character.
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Furthermore, solar irradiance in summer is higher than that in winter, so the output yield in
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summer is also larger than that in winter. The daily output yields in these four days are
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8.03(kWh/d), 13.34 (kWh/d), 2.85 (kWh/d) and 7.24 (kWh/d), respectively. The daily output
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yields in sunny days are much higher than those in rainy days. 3.2.5 The conversion efficiency of the DC/AC inverter
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Inverter is an important equipment to convert DC signals to AC signals, and its
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performance can directly influence the size and quality of the output electrical power. Fig.
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12shows the daily change trend of efficiency of inverters on a rainy day and a sunny day,
235
respectively. The red curve represents the variety trend of the conversion efficiency of the
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inverter on 24th, June, a sunny day, while the blue one indexed the measured results on 17th,
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June, a rainy day. The two curves behave a similar characterization: in the start-up phase of
238
the inverter, the efficiency value is only around 0.8, which is much lower than the conversion
239
efficiency value during normal operation period. The conversion efficiency increases sharply
240
while the solar irradiance increases. When the conversion efficiency reaches 0.94-0.95 at
241
about 8 a.m., the inverter enters the normal operation period and then remains a relatively
242
stable state. At half past 5 p.m., the conversion efficiency of the inverter decreases sharply,
243
then the inverter stops work. By comparing the two curves, it can be found that on the rainy
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day, because of the influence of weather, the inverter may start intermittently. Though the
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conversion efficiency value has a relatively stable interval, on basis of the daily change trend
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of the conversion efficiency of the inverter on a sunny day, it still has an apparent oscillation,
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especially in the morning and the afternoon.The output characteristicof PV cells is nonlinear,
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temperature of PV cells and solar radiation can arousean oscillation of output power of PV
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cells.The input of inverter is the output of PV array, hence irradiance fluctuations can result
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in output instability of the inverter. Oscillation of inverter will greatly impact the grid and
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influence the quality ofpower.Low quality power is not allowed to connectto public grid, we
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must ensure that inverters operate efficiently to track the maximum power when solar
253
radiation changes.Reference yield, array yield, final yield, capture loss and system loss
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Fig. 13shows the experimental results of daily reference yield, array yield and final
255
yield of the overall PV array per month. The daily reference yield per month varies from
256
2.38 h/d in January to 4.76 h/d in May, with an average value of 3.62 h/d. The daily array
257
yield per month reaches a maximum value of ~4.17 h/d in May, and gets to a minimum value
258
of ~2.01 h/d in January. The monthly average daily array yield is 3.01 h/d. The monthly
259
average daily final yield ranges from the largest value of 4.00 h/d in May to the smallest
260
value of 1.93 h/d in January. The monthly average daily final yield is 2.86 h/d. Fig. 13shows
261
that the reference yield and the final yield in summer are higher than those in winter. Taking
262
these many rainy days in June into account, the reference yield and final yield are low.
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Fig. 14 describes the daily final yield, capture loss and system lossof the overall PV
264
array per month. As mentioned above, capture loss is aroused by the incomplete utilization
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of the available radiation in the array operation period. The capture loss may be due to the
266
shadow, uneven irradiance, the temperature of PV modules, fouling and cleanliness of the
267
surface of PV modules, and other factors. System loss takes place when the inverter
268
converters DC power to AC power, so it is apparently affected by the match of the inverter’s
269
maximum power tracking (MPPT) function [24,25]. The capture loss varies from 0.88 h/d in
270
July to 0.37 h/d in January.The average value of the capture loss is ~0.61 h/d. The capture
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loss increases with the growth of solar irradiation, as a result, arousing an increased
272
temperature of PV modules. The system loss reaches the maximum of 0.20 h/d in June, and
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get to the minimum of 0.08 h/d in January, being equal to 0.16 h/d inaverage value. Fig.
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15reveals the percentage of the final yield, capture loss and system loss, which are 80.33%,
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15.74% and 3.93%, respectively. The low value in system loss means that the PV array
276
matches well with the inverter.
3.2.7 The array efficiency and system efficiency and performance ratio
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Fig. 16 shows the monthly array efficiency, system efficiency and performance ratio
279
(PR) of the system. PR represents the ratio of the actual output power to the theoretical
280
output power and could be affected by these factors such as the shadow, the temperature of
281
these PV modules, the strength of the solar irradiation, the rain and wind velocity. The
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highest and smallest value of PR is 84.81% in May and 78.12% in July. PR of 78.12% in
283
July may be aroused by the high temperature of these PV modules under the strong solar
284
radiation, leading to the capture loss. The average monthly PR is 80.66%.
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The monthly system efficiency varies from 10.29% in January to 11.03% in April. The
286
average value is 10.73%. The monthly array efficiency varies from 10.89% in January to
287
11.67% in March. The average value is 11.34%. The average monthly array efficiency is
288
higher than the average monthly system efficiency with a value of about 0.60%. This means
289
the average inverter efficiency is about 94.60%. The system efficiency and array efficiency
290
increase with decreasing ambient temperature, so system efficiency and array efficiency in
291
winter are higher than those in summer. The conclusion supports the argument that the lower
292
ambient temperature can lead to the larger efficiency. Due to the influence of snow covering
293
on the solar panel, the solar cell efficiency in January was low. Especially in 2008, it reached
294
the touch point because of the snow disaster in the southern region of China. An average
Ac ce p
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15
Page 15 of 43
efficiency value can be used to predict the generated electricity power. Efficiency
296
discussions in this paper are based on the actual operation conditions from year 2007 to 2009.
297
Array efficiency and system efficiency will increase with the improvement of manufacturing
298
skills for solar cells and inverters. Therefore, we will do further research based on the
299
present manufacturing skills in the future.Based on literatures [25], the daily irradiance can
300
be classified into three types: overcast, intermediate and clear.The total irradiance of a day
301
less than 2400 W/（m2・d）is called overcast, the daily irradiance in a clear day is with more
302
than 5000 W/（m2・d）, and the daily irradiance higher than 2400 W/（m2・d）but less than
303
5000 W/（m2・d）is defined as intermediate. The total days of the monitoring in three years
304
are 1096, among which 341 days are overcast (106 days in the year 2007, 111 days in the
305
year 2008, and 124 days in the year 2009); 426 days are intermediate (151 days in the year
306
2007, 138 days in the year 2008 and 137 days in the year 2009); the other 329 days are clear
307
(108 days in the year 2007, 117 days in the year 2008 and 104 days in the year 2009).
cr
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308
ip t
295
Table 4and 5shows the daily PR and the daily system efficiency for different weather
309
from the year 2007 to 2009 with an average value, maximum value and minimum value,
310
respectively. The minimum PR and system efficiency for overcast on 4th, July, 2007 are
311
23.75% and 3.20%, respectively. The relative output power is ~0.73 kWh and is quite low.
312
The result was due to the heavy rainstorm in that day. The PR and system efficiency for
313
intermediate with the value of 5.31% and 0.72% took place on 25th, May, 2007 which was
314
due to the system fault. The minimum PR of 1.44% and system efficiency of 0.19% for
315
overcast occurred on 2nd February, 2008, because the snow covered the PV array and
316
prevented its operation. The minimum PR of 23.05% and system efficiency of 3.10% for 16
Page 16 of 43
clear happened on 3rd February, 2008, due to the snow as well. The minimum PR of 34.83%
318
and system efficiency of 4.68% for overcast happened on 21th September, 2009, since the
319
daily solar radiation quantity was extremely low with a value of ~414.62Wh/m2. The daily
320
performance ratio can reach 94.04%, and the corresponding system efficiency can get to
321
12.63% for overcast in 2009.
cr
ip t
317
The system efficiencies for overcast, intermediate and clearare 10.71%, 10.85% and
323
10.61%, respectively. Compared with Table 3 and Table 4, the daily system efficiency for
324
clear are lower than the other two cases.It rains every month and about 70 days in a year, the
325
research results show that the rain can clean the dust on surface of solar cells. But
326
automobile traffic in Shanghai area is large, its exhaust oil particles float in the air.The oil
327
particles absorb the dust in the air and then attach to the solar glass surface, forming a layer
328
of transparent viscous lossy film, this layer of film is difficult toclean by rain. Therefore,
329
when designing a PV system in Shanghai, we can make little contribution to clean the dust
330
on cell surface, but pay more attention to the cleaning of oil particles.
332
an
M
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Ac ce p
331
us
322
3.2.8 Comparison of PV systems in different countries In order to have clear picture of performance of the solar powerplant, performance
333
parameters evaluated for the Chinese solar PVplant are compared with the reported
334
performance parameters ofsolar PV plants at various locations as shown in Table 6.The
335
annual average daily final yield of the system is lower than those in Singaporeand Greece,
336
but higher than those in other countries. PR of the system is lower than that in Singapore and
337
Ireland,while higher than those in other locations.The comparative study provides an
338
insightto the performance of the poly crystalline PV technology under Chinese climatic 17
Page 17 of 43
339
conditions and indicatesthe suitability of solar power generation in China.
340
4. Conclusion
342
The performance monitoring is analyzed based on the measured data from year 2007 to
ip t
341
year 2009. The following conclusions can be made:
1. The lowest and the highest temperatures in the three years are -6.26oC and 41.44oC,
344
respectively. Temperature cannot be neglected when designing a PV system.We can use
345
these data to predict the output and the surface temperature of PV modules.
us
cr
343
2. The annual predicted and measured energy output on 25゜tilt are 3220.21 kWh and
347
3189.13 kWh. The results show the predicted data matches well with the measured data
348
when measured solar radiation data of the site is used as input to the simulation software.
349
The results can provide a guide for the design of optimized tilt angle of the similar PV
350
systems in Shanghai, China. For a similar PV system in Shanghai, China, the performances
351
of PV arrays and inverter are determined, and the effect of solar energy resources and
352
environmental temperature on PV systems has also been determined. Hence the energy
353
output is affected by tilt angle of PV arrays. Combine the performances of PV arrays and
354
inverter, loss of line, and some other factors, the data of per kilowatt output of the system
355
can provide a qualitative reference for the design of PV systems in Shanghai, China.
356
However, the accurated optimal title angle of PV systems still needs to be quantitative
357
calculated. The measured solar radiation data of the sitecan be used to predict energy output
358
when designing a PV system. The average annual output yield is 3189 KWh and average
359
daily output yield is 8.74 kWh. The output yield and solar radiation quantity in June is small
360
due to the plum rain in Shanghai. when designing a PV system in Shanghai, we can make
Ac ce p
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18
Page 18 of 43
361
little contribution to clean the dust on cell surface, but pay more attention to the cleaning of
362
oil particles. 3. The daily reference yield, array yield, final yield, capture loss and system loss of the
364
overall PV array per month are 3.62 h/d, 3.01 h/d, 2.86 h/d, 0.61 h/d and 0.16 h/d,
365
respectively. The low system loss means the PV array matches well with the inverter. The
366
monthly average performance ratio, system efficiency and array efficiency are 80.66%,
367
10.73% and 11.34%, respectively. The difference between system efficiency and array
368
efficiency is ~0.60% and very low, which means the average efficiency of the inverter is
369
about 94.6%. The daily conversion efficiency of the inverter in a rainy day and a sunny day
370
is different. The daily conversion efficiencyof the inverter behaves large fluctuations in a
371
rainy day and keeps relatively stable in a sunny day. The daily output yield and solar
372
radiation quantity in summer and in winter with a rainy day and a sunny day are also
373
compared. Daily output yield in a sunny day is higher than that in a rainy day, and the daily
374
output yield in summer is higher than that in winter. The system efficiencies for overcast,
375
intermediate and clear are 10.71%, 10.85% and 10.61%, respectively.The parameters of the
376
daily system efficiency for clear are lower than those for overcast or intermediate.
cr
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377
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363
4. The measured average annual PR of the system is 80.66%and final yield is 2.86 h/d
378
after three years of continuous operation, indicating the vast solar potential in China needs to
379
be utilized for solar power generation. The study provides an insightto the performance of
380
the poly crystalline PV technology under Chinese climatic conditions.
381 382
Acknowledgements 19
Page 19 of 43
This work is supported by Natural Science Foundation of China (Nos. 11374204,
384
51034010), “Shu Guang” project of Shanghai Municipal Education Commission and
385
Shanghai Education Development Foundation (No. 13SG52), and Projects of Science and
386
Technology Commission of Shanghai Municipality (Nos. 12JC1404400, 11160500700).
387
Nomenclature
388
YA: the array yield YA (h/d)
389
YF: the final yield (h/d)
390
YR: the reference yield (h/d)
391
P0: the installed capacity (kW)
392
EDC: the DC energy output per day of array (kWh/d)
393
EAC: the AC output energy that connects to the grid per day (kWh/d)
394
HId: the total daily in-plane irradiation by the modules (kWh/m2.d)
395
GI: the reference in-plane irradiance (kWh/m2.d)
396
LC: the array capture losses (h/d)
397
LS: the system losses (h/d)
398
PR: the performance ratio (%)
399
ηAmean: the array efficiency (%)
400
ηsystem: the system efficiency (%)
401
EDC,total: the total DC energy output per day of array (kWh/d)
402
EAC,total: the total AC output energy that connects to the grid per day (kWh/d)
Ac ce p
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403
20
Page 20 of 43
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[9] Q. Zhu，L. Si，T. Jiang.Economical and environmental analysis of photovoltaic systems
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[10] A.G. Hestnes, Building integration of solar energy systems. Solar Energy 1999; 67 (4-6):
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[12] The standard GB/T 19064-2003 Specification and Test Method for Residential PV
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[13] The standard GB/T 19939-2005 Technical Requirements for Grid Connection of PV
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[14] International Electrotechnical Commission. IEC 61724, photovoltaic system
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performance monitoring e guidelines for measurement, data exchange and analysis. 1st ed.
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Geneva, IEC: International Electrotechnical Commission; 1998.
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[15] V. Sharma, A. Kumar, O.S. Sastry, S.S. Chandel. Performance assessment of different
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solar photovoltaic technologies under similar outdoor conditions. Energy 2013;58:511-518.
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[16] V. Sharma, S.S. Chandel. Performance analysis of a 190 kWp grid interactive solar
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photovoltaic power plant in India. Energy 2013;55:476-485.
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[17] B. K. Koyunbabaa, Z. Yilmazb, K. Ulgena. An approach for energy modeling of a
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building integrated photovoltaic (BIPV) Trombe wall system. Energy and Buildings
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2013;67:680–688.
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[18] U. Jahn, W. Nasse. Operational performance of grid-connected PV systems on buildings
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in Germany.ProgPhotovolt: Res Appl 2004;12:441-8.
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[19] L. M. Ayompe, A. Duffy, S. J. McCormack, M. Conlon. Measured performance of a
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1.72 kW rooftop grid connected photovoltaic system in Ireland. Energy Convers Manage
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2011;52:816-25.
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[20] J. Mondol, Y. Yohanis, M. Smyth, B. Norton. Long term performance analysis of a grid
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[21] E. Kymakis, S. Kalykakis, T. Papazoglou. Performance analysis of a grid connected
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photovoltaic park on the island of Crete. Energy Convers Manage 2009;50(3):433-8.
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[22] B. Quesada, C. Sáchez, J. Canada, R. Royo, J. Payá. Experimental results and
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simulation with TRNSYS of a 7.2 kWp grid-connected photovoltaic system. Applied Energy
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2011; 88 :1772-1783.
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[23] C. Zhao, R. Cui. Technical research and development of solar energy buildingmaterials
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(I): Thermal investigation of integrated PV roof．ActaEnergiaSolarsSinica 2003; 24:352-356.
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[24] R. Eke, A. Senturk. Monitoring the performance of single and triple junction amorphous
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silicon modules in two building integrated photovoltaic (BIPV) installations. Applied Energy
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2013; 109:154-162.
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[25] S. Wittkopf, S. Valliappan, L. Liu, K. Ang, S. Cheng. Analytical performance
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monitoring of a 142.5 kWp grid-connected rooftop BIPV system in Singapore. Renewable
464
Energy 2012;47:9-20.
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[26] S.M.Pietruszko, M.Gradzki. Performance of a grid connected small PV systemin Poland.
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Applied Energy 2003;74:84-177.
467
[27] B. Decker, U. Jahn. Performance of 170 grid connected PV plants in NorthernGermany
468
-analysis of yields and optimization potentials. Solar Energy1997;59:33-127.
469
Caption of figures:
in
Northern
Ireland.
Energy
Convers
ip t
system
Ac ce p
te
d
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photovoltaic
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Fig. 1.Environment conditions of the BAPV system.
471
Fig. 2.General principle review.
472
Fig. 3.The monthly lowest temperature.
473
Fig. 4.The monthly highest temperature.
474
Fig. 5.The average monthly temperature.
475
Fig. 6. Variation of monthly and yearly global horizontal, actual measured and predicted
476
solar radiation.
477
Fig. 7.Variation of monthly and yearly actual measured and predicted energy output.
478
Fig. 8. Daily output yield with solar irradiance on 17th, June (rainy).
479
Fig. 9. Daily output yield with solar irradiance on 24th, June (sunny).
480
Fig. 10. Daily output yield with solar irradiance on 10th, December (rainy).
481
Fig. 11. Daily output yield with solar irradiance on 14th, December (sunny).
482
Fig. 12.The daily change trend of conversion efficiency of inverters.
483
Fig. 13.Daily reference yield, array yield and final yield of the overall PV array.
484
Fig. 14. Daily reference yield, capture loss and system loss of the overall PV array.
485
Fig. 15.Daily percentage of reference yield, capture loss and system loss of the overall PV
487
cr
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Ac ce p
486
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470
array.
Fig. 16.The monthly array efficiency, system efficiency and performance ratio.
24
Page 24 of 43
488
Caption of tables:
490
Table 1. Monthly amount of solar radiationquantity，/kWh˙m-2.
491
Table 2. Monthly amount of solar radiationquantity，/kWh˙m-2.
492
Table 3.Number of days and daily output for different weather from year 2007 to 2009.
493
Table 4. Daily PR for different weather conditions from the year 2007 to 2009.
494
Table 5.Daily system efficiency of different weather conditions from year 2007 to 2009.
495
Table 6.Performance study comparison of PV systems in different countries.
an
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489
Ac ce p
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496
25
Page 25 of 43
Table 1. Monthly amount of solar radiation quantity，/kWh˙m-2. Year 2007 2008 2009
Jan. 73 55.1 93.6
Feb. 101.6 119.9 51.6
Mar. 116.5 134.5 102.4
Apr. 131.1 114 147.6
May. 149.5 159.9 133.7
Jun. 95.6 88 120.9
Jul. 140.3 160.6 133.4
Aug. 152.8 131.9 98.4
Sep. 101.8 110.1 98.0
Oct. 114.9 96 130.2
Table 2. Monthly output yield，/kWh. Jan. 174.8 127.6 235.0
Feb. 236.5 279.5 128.1
Mar. 280.5 329.7 256.3
Apr. 307.9 277.9 365.6
May. 341.3 378.4 396.8
Jun. 227.9 215.9 291.4
Dec. 71 103.6 87.2
Sep. 239.7 263.8 234.4
Oct. 271.7 232.7 310.4
Nov. 236.1 211.3 168.5
Dec. 169.5 241.3 216.9
Table 3 Number of days and daily output for different weather from year 2007 to 2009
2007 2008 2009
rainy
cloudy
days
daily output(kWh)
days
8 5 11
11.0 3.4 7.0
10 19 13
sunny
daily output(kWh)
days
daily output(kWh)
12.0 6.3 10.6
12 6 6
10.8 13.1 12.5
te
501
M
year
d
500
Aug. 351.9 315.4 237.2
an
499
Jul. 321.0 376.9 317.2
us
Year 2007 2008 2009
cr
497 498
Nov. 97.5 85.3 67.7
ip t
496
Table 4. Daily PR of different weather from year 2007 to 2009.
Ac ce p
502 Weather PR(%)
Year 2007
Overcast
Intermediate
Year 2008
Clear
Overcast
Intermediate
Year 2009 Clear
Overcast
Intermediate
Clear
Average
79.05
79.59
77.55
77.68
80.80
79.38
82.28
82.31
80.00
Minimum
23.75
5.31
70.55
1.44
23.05
74.24
34.83
69.82
74.24
Maximum
89.72
86.06
82.98
91.13
89.28
84.83
94.04
90.86
86.20
503 504 505
26
Page 26 of 43
Table 5. Daily system efficiency of different weather conditions from year 2007 to 2009. Weather System efficiency(%)
Year 2007 Overcast
Intermediate
Year 2008 Clear
Overcast
Year 2009
Intermediate
Clear
Overcast
10.62
10.63
10.41
10.45
10.85
10.66
11.05
Minimum
3.20
0.72
9.48
0.19
3.10
9.97
4.68
Maximum
12.05
11.56
11.15
10.64
11.99
11.39
Clear
11.07
10.74
10.10
9.97
12.20
11.58
cr
Average
Intermediate
ip t
506
us
12.63
507
Rated
Type of PV
Output
Final
M
Location
an
Table 6 Performance study comparison of PV systems in different countries
508
System PR(%)
Ref
technology
yield(kWh/kp)
yield(kWh/kW・d)
efficiency(%)
India
190
Polycrystalline Si
812.76
2.23
12.3
74
[16]
Ireland
1.72
Monocrystalline Si
885.1
2.4
12.6
81.5
[19]
Ac ce p
te
d
capacity(kWp)
Greece
171.36
Polycrystalline Si
1336.4
1.96-5.07
-
67.3
[21]
Singapore
142.5
Polycrystalline Si
1018.95
3.12
11.2
81
[25]
Poland
1
Amorphous-Si
830
2.3
4.0-5.0
60.8
[26]
Germany
-
Polycrystalline Si
680
1.9
-
66.5
[27]
China
3
Polycrystalline Si
1063.04
2.86
10.99
80.6
Present study
509 510 27
Page 27 of 43
Ac ce pt e
d
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Figure(s)
Page 28 of 43
an
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Figure(s)
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Page 29 of 43
Figure(s)
30
ć
2
007 year 2
5
year 2008
20
year 2009
1
5
5
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2
3
4
5
6 7 month
10
8
9
10
11
us
Ͳ
1
12
cr
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Page 30 of 43
Figure(s)
45
year 2007
ć
40 35
year 2008
30
year 2009
25 20
ip t
15 10
1
2
3
4
5
6 7 month
8
9
10
11
12
us
0
cr
5
Ac ce pt e
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Page 31 of 43
Figure(s)
ć
35 year 2007
30
year 2008
25
year 2009
20 15
ip t
10
1
2
3
4
5
6 7 month
8
9
10
11
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0
cr
5
12
Ac ce pt e
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M
an
Page 32 of 43
Ac
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ed
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Figure(s)
Page 33 of 43
Figure(s)
u t pu oyg re n
hl t
ye n
mo
m o n thl
yen e r gyou t pu t
pr edi
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p t yea r l yen e r gyou t u
m ae sur ed
m o n thl
yen e r gyou t pu t
m ae sur ed
p t yea r l yen e r gyou t u 3230
h) 3220 W k( t 3210 u t pu 3200 yo g
1
2
3
4
5
6
7
8
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10
11
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12
3190 ren e 3180 l y ar 3170 ye
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400 350 300 250 200 150 100 50 0
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pr edi
Page 34 of 43
Figure(s)
900 .000 os l
ar
ir
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W/ m
ec (
tp
ou
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k W (
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2.500
800 .000
)
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700 .000
2
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600 .000 1.500
( 500 .000
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100 .000
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Page 35 of 43
Figure(s)
1200 .000 os l
ar
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W/ m
ec (
tp
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2.500 )
1000 .000 2.000
)
2
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800 .000 1.500
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k(
600 .000 i an
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t u tp uo
400 .000
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0 .500
200 .000
0 .000
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06 :
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80 :
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4
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Page 36 of 43
Figure(s)
350 .000
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150 .000 0 .15
a i r r
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09 :
0 1:
0 1:
4
0
0
2
0 0
1 1:
2 1:
2 1:
4
0
0
2
0 0
13 :
4 1:
4 1:
4
0
2
0 0
51 :
16 :
16 :
4
t eim
0
2
0 0
17 :
18 :
18 :
4
Ac
ce pt
ed
M
an
us
th
0
cr
0
2
0 0
0 0
Page 37 of 43
Figure(s)
so l
ar
ri
da i
an
2)
/W m
ec (
tp
ou
u
t
(
k W
)
600 .000
1.6
1.4 )
500 .000 2
1.2 / m W
400 .000 )
1
( ec 300 .000
0 .8
i na d
(k
W
t u
0 .6
a
ou
r
l ar o s
tp
ip t
i r
200 .000 0 .4
100 .000
0 .2
0 .000
0
4
70 :
80 :
80 :
0
0
06 :
06 :
4
0
0
0
2
0 0
09 :
0 1:
0 1:
4
0
0
2
0 0
1 1:
2 1:
2 1:
ti
4
0
0
2
0 0
13 :
4 1:
4 1:
4
2
0 0
40
20
0 0
51 :
16 :
61 :
71 :
18 :
me
4
18 :
Ac
ce pt
ed
M
an
us
th
0
cr
0
2
0 0
0 0
Page 38 of 43
Figure(s)
1.2
y d a y o n 17t h , J u n e s u n y d a y o n 24t h , J u n e r a in
1 0.8 0.6
0.2
53 60 :
0 1 07 :
54 70 :
0 2
08 :
5 5 80 :
0 3
09 :
05 0 1:
04 0 1:
51 1: 1
0 15 : 1
5 2 21 :
00 3 1:
5 3 31 :
0 1 4 1:
54 4 1:
0 2 15 :
55 15 :
0 3
16 :
05 17 :
04
17 :
51 18 :
0 5 18 :
cr
00 06 :
Ac
ce pt
ed
M
an
us
0
ip t
0.4
Page 39 of 43
Figure(s)
6
reference yield(h/d) A rray yield(h/d) final yield(h/d)
5 4 3
1 1
2
3
4
5
6
7
8
9
10
11
12
Ac
ce pt
ed
M
an
us
cr
0
ip t
2
Page 40 of 43
Figure(s)
f in a l y i e l d h( / d ) ac p ture l oss h( / d ) sy s tem l oss h( / d ) ( W h / m 2) m o n th l y sol a r ra d i a tio n k ( W )h m o n th l y o utputy i e l d k
4.5 3.5 3 2.5 2 1.5
ie ly na
0.5
dl
/ ca
0
1
2
3
4
5
6 7 m on th
8
9
10
11
12
Ac
ce pt
ed
M
an
us
if
1
n 350 i t o ia 300 d ra r 250 l a o /s 200 dl ie 150 t yu to 100 u ho 50 t n mo 0
ip t
/ ca s l so er t up
4
cr
s l so er t up
400
Page 41 of 43
Figure(s)
final y iela(h/d)
3.93%
capture loss(h/d)
15.74%
Ac
ce pt
ed
M
an
us
cr
80.33%
ip t
system loss(h/d)
Page 42 of 43
Figure(s)
83
p e r of mr
a n ec
r at i o
sy
f i ci e n c y
te m e
ar
12
a y e f i ci e n c y
82
11.5
81
i t ao
yn c ie ffi c em
r 80
11
79 f ro per 78
te 10.5 s / sy ya r 10 ar
ip t
en acm
1
2
3
4
5
6
7
8
9
10
11
12
9.5
Ac
ce pt
ed
M
an
us
76
cr
77
Page 43 of 43