Photovoltaic Sources Modeling

Giovanni Petrone received a Ph.D. degree in Electrical Engineering from the University "Federico II" of Naples in 2004. He is Associate Professor of Electrical Engineering at the University of Salerno. He is a Member of the Technical Committee on Renewable Energy Systems of the IEEE Industrial Electronics Society and Associate Editor of the IEEE Journal of Photovoltaics. Giovanni has been coordinator of research projects in the field of power electronics for renewable sources. He is co-author of five international patents, of one book and of more than 40 papers published in international journals. He has been included in the 2015 list of Most Influential Minds from Thomson Reuters.Carlos Andres Ramos-Paja received his Ph.D. degree in power electronics from the Universitat Rovira i Virgili-Spain in 2009. He is Professor at the Universidad Nacional de Colombia where he is an IEEE senior member. He was recognized with the “research merit award” from his faculty, and he is the principal investigator of some research projects funded by the Colombian government. He is co-author of one international patent and of more than 50 papers published in international journals.Giovanni Spagnuolo received a Ph.D. degree in Electrical Engineering from the University "Federico II" of Naples in 1998. He is Full Professor of Electrical Engineering at the University of Salerno and an IEEE Fellow "for contributions to control of photovoltaic systems". He is also a Member of the Steering Committee and Editor of the IEEE Journal of Photovoltaics and Associate Editor of the IEEE Transactions on Industrial Electronics. From 2012 to 2014 he chaired the Technical Committee on Renewable Energy Systems of the IEEE Industrial Electronics Society. He is the principal investigator of industrial research projects and, for his University, of some FP7 and H2020 projects. He is co-author of five international patents, of one book and of more than 60 papers published in international journals. He is in the 2015 list of Most Influential Minds from Thomson Reuters.

• Acknowledgements xiIntroduction xiiiTables of Symbols and Acronyms xv1 PV Models 11.1 Introduction 11.2 Modeling: Granularity and Accuracy 11.3 The Double-diode Model 21.4 The Single-diode Model 41.4.1 Effect of the SDM Parameters on the I–V Curve 51.5 Models of PV Array for Circuit Simulator 61.5.1 The Single-diode Model based on the Lambert W-function 101.6 PV Dynamic Models 111.7 PV Small-signal Models and Dynamic-resistance Modelling 14References 172 Single-diode Model Parameter Identification 212.1 Introduction 212.2 PV Parameter Identification from Datasheet Information 212.2.1 Exact Numerical Methods 212.2.2 Approximate Explicit Solution for Calculating SDM Parameters 242.2.3 Validation of the Approximate Explicit Solution 272.3 Single-diode Model Simplification 302.3.1 Five-parameter versus Four-parameter Simplification 322.3.2 Explicit Equations for Calculating the Four SDM Parameters 342.4 Improved Models for Amorphous and Organic PV Technologies 372.4.1 Modified SDM for Amorphous PV Cells 372.4.2 Five-parameter Calculation for Amorphous Silicon PV Panels 382.4.3 Modified Model for Organic PV Cells 40References 433 PV Simulation under Homogeneous Conditions 453.1 Introduction 453.2 Irradiance- and Temperature-dependence of the PV Model 453.2.1 Direct Effects of Irradiance and Temperature 453.2.2 Equations for “Translating” SDM Parameters 493.2.3 Iterative Procedure proposed by Villalva et al. 513.2.4 Modified PV Model proposed by Lo Brano et al. 523.2.5 Translating Equations proposed by Marion et al. 533.2.6 Modified Translational Equation proposed by Picault et al. 533.2.7 PV Electrical Model proposed by King et al. 563.2.8 Using the King Equation for Estimating the SDM Parameter Drift 593.3 Simplified PV Models for Long-term Simulations 613.3.1 King Equations for Long-term Simulations 633.3.2 Performance Prediction Model based on the Fill Factor 683.3.3 PV Modeling based on Artificial Neural Networks 693.4 Real-time Simulation of PV Arrays 713.4.1 Simplified Models including the Power Conversion Stage 723.5 Summary of PV Models 75References 774 PV Arrays in Non-homogeneous Conditions 814.1 Mismatching Effects: Sources and Consequences 814.1.1 Manufacturing Tolerances 814.1.2 Aging 824.1.3 Soiling and Snow 834.1.4 Shadowing 834.1.5 Module Temperature 864.2 Bypass Diode Failure 874.3 Hot spots and Bypass Diodes 894.4 Effect of Aging Failures and Malfunctioning on the PV Energy Yield 90References 945 Models of PV Arrays under Non-homogeneous Conditions 975.1 The use of the Lambert W-Function 985.2 Application Examples 1025.2.1 The Entire I–V Curve of a Mismatched PV String 1025.2.2 The Operating Point of a Mismatched PV String 1045.3 Guess Solution by Inflection-point Detection 1065.4 Real-time Simulation of Mismatched PV Arrays 1085.5 Estimation of the Energy Production of Mismatched PV Arrays 109References 1116 PV array Modeling at Cell Level under Non-homogeneous Conditions 1136.1 PV Cell Modeling at Negative Voltage Values 1136.1.1 The Bishop Term 1136.1.2 Silicon Cells Type and Reverse Behavior 1156.2 Cell and Subcell Modeling: Occurrence of Hot Spots 1166.2.1 Cell Modeling 1176.3 Simulation Example 1216.4 Subcell PV Model 1236.5 Concluding Remarks on PV String Modeling 124References 1247 Modeling the PV Power Conversion Chain 1277.1 Introduction 1277.2 Review of Basic Concepts for Modeling Power Converters 1297.2.1 Steady-state Analysis 1327.2.1.1 Steady-state Values 1337.2.1.2 Ripple Magnitudes 1337.2.2 Converter Dynamics Analysis 1347.3 Effects of the Converter in the Power Conversion Chain 1367.3.1 Steady-state Model of the Power Conversion Chain 1367.3.2 Analysis and Simulation using the Steady-state Model 1397.3.3 Voltage Ripple at the Generator Terminals 1437.3.4 I–V Curve of the Power Conversion Chain 1487.4 Modelling the Dynamics of the Power Conversion Chain 1517.5 Additional Examples 1597.5.1 MIU based on a Buck Converter 1597.5.2 MIU based on a Buck–Boost Converter 1617.6 Summary 162References 1638 Control of the Power Conversion Chain 1658.1 Introduction 1658.2 Linear Controller 1668.3 Sliding-mode Controller 1728.3.1 Inductor Current Control 1738.3.2 Capacitor Current Control 1798.4 Summary 183References 184Index 000