Providing an accurate method for obtaining the efficiency of a photovoltaic solar module

Abstract A widely-used correlation to obtain efficiency is considered, and the hypothesis of dividing a photovoltaic module into a number of sub-regions and calculating efficiency of each region employing the temperature of that part to determine the efficiency of the module more precisely is proposed and examined. Experimental data recorded during a year are employed to check the accuracy of the proposed hypothesis and the original method (suggestion of the 61215 standard, where one temperature is considered for the whole module). Comparison of the both hourly and monthly profiles of performance criteria shows that the proposed hypothesis is much more accurate than the original method. It is found that in July, as the sample month for which the hourly profiles are investigated, the average values of the error in prediction of efficiency, produced power, and generated energy are 5.70, 7.72, and 6.07% for the original approach whereas using the proposed hypothesis reduces them to only 1.45, 1.75, and 1.35%, respectively. Moreover, the values of annual average of error in prediction of the three aforementioned performance criteria are improved from 4.14, 5.45, and 4.55% in the original method to 1.05, 1.25, and 1.09% for the proposed hypothesis, which is a huge achievement.

[1]  E. Skoplaki,et al.  A simple correlation for the operating temperature of photovoltaic modules of arbitrary mounting , 2008 .

[2]  S. R. Williams,et al.  Influences on the energy delivery of thin film photovoltaic modules , 2013 .

[3]  Gilles Notton,et al.  Modelling of a double-glass photovoltaic module using finite differences , 2005 .

[4]  Constantinos A. Balaras,et al.  Energy efficiency of PV panels under real outdoor conditions–An experimental assessment in Athens, Greece , 2017 .

[5]  Bin-Juine Huang,et al.  Solar cell junction temperature measurement of PV module , 2011 .

[6]  A. Sohani,et al.  Determination of Hildebrand solubility parameter of pure 1-alkanols up to high pressures , 2020 .

[7]  Agata Zdyb,et al.  Experimental Efficiency Analysis of a Photovoltaic System with Different Module Technologies under Temperate Climate Conditions , 2019, Applied Sciences.

[8]  Guiqiang Li,et al.  Performance comparison of photovoltaic/thermal solar water heating systems with direct-coupled photovoltaic pump, traditional pump and natural circulation , 2019, Renewable Energy.

[9]  Li Xingcai,et al.  Effectively predict the solar radiation transmittance of dusty photovoltaic panels through Lambert-Beer law , 2018 .

[10]  A. Sohani,et al.  Comparative study of the conventional types of heat and mass exchangers to achieve the best design of dew point evaporative coolers at diverse climatic conditions , 2018 .

[11]  Hoseyn Sayyaadi,et al.  Life cycle comparison of potential scenarios to achieve the foremost performance for an off-grid photovoltaic electrification system , 2020 .

[12]  E. Rocco,et al.  Hybrid solar power system versus photovoltaic plant: A comparative analysis through a life cycle approach , 2019, Renewable Energy.

[13]  Zhichun Ni,et al.  Mechanical analysis of photovoltaic panels with various boundary condition , 2019, Renewable Energy.

[14]  Zuhal Oktay,et al.  Sensitivity analysis of implicit correlations for photovoltaic module temperature: A review , 2017 .

[15]  H. Hassan,et al.  An experimental work on the performance of new integration of photovoltaic panel with solar still in semi-arid climate conditions , 2020 .

[16]  Majid Dehghani,et al.  The Effect of Temperature on Photovoltaic Cell Efficiency , 2011 .

[17]  Hoseyn Sayyaadi,et al.  End-users’ and policymakers’ impacts on optimal characteristics of a dew-point cooler , 2020 .

[18]  Camel Tanougast,et al.  Forseeing energy photovoltaic output determination by a statistical model using real module temperature in the north east of France , 2018 .

[19]  David L. King,et al.  Photovoltaic module and array performance characterization methods for all system operating conditions , 1996 .

[20]  Honglu Zhu,et al.  Online Modelling and Calculation for Operating Temperature of Silicon-Based PV Modules Based on BP-ANN , 2017 .

[21]  Mark D. Semon,et al.  POSTUSE REVIEW: An Introduction to Error Analysis: The Study of Uncertainties in Physical Measurements , 1982 .

[22]  A. Sohani,et al.  Application based multi-objective performance optimization of a proton exchange membrane fuel cell , 2020 .

[23]  S. P. Lester,et al.  Computational study and experimental validation of a solar photovoltaics and thermal technology , 2019 .

[24]  Naveed ur Rehman,et al.  An optical-energy model for optimizing the geometrical layout of solar photovoltaic arrays in a constrained field , 2020 .

[25]  Hoseyn Sayyaadi,et al.  Employing static and dynamic optimization approaches on a desiccant-enhanced indirect evaporative cooling system , 2019, Energy Conversion and Management.

[26]  Seth B. Dworkin,et al.  A methodology for predicting hybrid solar panel performance in different operating modes , 2019, Renewable Energy.

[27]  Tugba Memisoglu,et al.  Optimal site selection for solar photovoltaic (PV) power plants using GIS and AHP: A case study of Malatya Province, Turkey , 2020 .

[28]  M. Flota-Banuelos,et al.  A new passive PV heatsink design to reduce efficiency losses: A computational and experimental evaluation , 2020 .

[29]  Peng Wang,et al.  Solar energy potential assessment: A framework to integrate geographic, technological, and economic indices for a potential analysis , 2020 .

[30]  O. S. Sastry,et al.  Solar photovoltaics pumps operating head selection for the optimum efficiency , 2019, Renewable Energy.

[31]  Ming Li,et al.  Online extraction of physical parameters of photovoltaic modules in a building-integrated photovoltaic system , 2019 .

[32]  Amirhossein Fathi,et al.  Experimental analysis of a cooling system effect on photovoltaic panels' efficiency and its preheating water production , 2019, Renewable Energy.

[33]  Joshua M. Pearce,et al.  The potential for grid defection of small and medium sized enterprises using solar photovoltaic, battery and generator hybrid systems , 2020 .

[34]  Wolf Fichtner,et al.  Assessment of rooftop photovoltaic potentials at the urban level using publicly available geodata and image recognition techniques , 2017 .

[35]  A. John Mallinckrodt,et al.  Data Reduction and Error Analysis for the Physical Sciences , 1993 .

[36]  J. Carretero,et al.  Models to predict the operating temperature of different photovoltaic modules in outdoor conditions , 2015 .

[37]  H. Singh,et al.  Modelling and experimental analysis of low concentrating solar panels for use in building integrated and applied photovoltaic (BIPV/BAPV) systems , 2019, Renewable Energy.

[38]  A. Sohani,et al.  Modeling and multi-objective optimization of an M-cycle cross-flow indirect evaporative cooler using the GMDH type neural network , 2016 .