COVER
TITLE PAGE
ABOUT THE AUTHORS
FOREWORD
ACKNOWLEDGMENT
1 MODELING OF THE SOLAR SOURCE
1. 1.1 INTRODUCTION
2. 1.2 MODELING OF THE SUN POSITION
3. 1.3 MODELING OF EXTRATERRESTRIAL SOLAR RADIATION
4. 1.4 MODELING OF GLOBAL SOLAR RADIATION ON A HORIZONTAL SURFACE
5. 1.5 MODELING OF GLOBAL SOLAR RADIATION ON A TILT SURFACE
6. 1.6 MODELING OF SOLAR RADIATION BASED ON GROUND MEASUREMENTS
7. 1.7 AI TECHNIQUES FOR MODELING OF SOLAR RADIATION
8. 1.8 MODELING OF SUN TRACKERS
9. FURTHER READING
2 MODELING OF PHOTOVOLTAIC SOURCE
1. 2.1 INTRODUCTION
2. 2.2 MODELING OF SOLAR CELL BASED ON STANDARD TESTING CONDITIONS
3. 2.3 MODELING OF SOLAR CELL TEMPERATURE
4. 2.4 EMPIRICAL MODELING OF PV PANELS BASED ON ACTUAL PERFORMANCE
5. 2.5 STATISTICAL MODELS FOR PV PANELS BASED ON ACTUAL PERFORMANCE
6. 2.6 CHARACTERIZATION OF PV PANELS BASED ON ACTUAL PERFORMANCE
7. 2.7 AI APPLICATION FOR MODELING OF PV PANELS
8. FURTHER READING
3 MODELING OF PV SYSTEM POWER ELECTRONIC FEATURES AND AUXILIARY POWER SOURCES
1. 3.1 INTRODUCTION
2. 3.2 MAXIMUM POWER POINT TRACKERS
3. 3.3 DC–AC INVERTERS
4. 3.4 STORAGE BATTERY
5. 3.5 MODELING OF WIND TURBINES
6. 3.6 MODELING OF DIESEL GENERATOR
7. 3.7 PV ARRAY TILT ANGLE
8. 3.8 MOTOR PUMP MODEL IN PV PUMPING SYSTEM
9. FURTHER READING
4 MODELING OF PHOTOVOLTAIC SYSTEM ENERGY FLOW
1. 4.1 INTRODUCTION
2. 4.2 ENERGY FLOW MODELING FOR STAND‐ALONE PV POWER SYSTEMS
3. 4.3 ENERGY FLOW MODELING FOR HYBRID PV/WIND POWER SYSTEMS
4. 4.4 ENERGY FLOW MODELING FOR HYBRID PV/DIESEL POWER SYSTEMS
5. 4.5 CURRENT‐BASED MODELING OF PV/DIESEL GENERATOR/BATTERY SYSTEM CONSIDERING TYPICAL CONTROL STRATEGIES
6. FURTHER READING
5 PV SYSTEMS IN THE ELECTRICAL POWER SYSTEM
1. 5.1 OVERVIEW OF SMART GRIDS
2. 5.2 OPTIMAL SIZING OF GRID‐CONNECTED PHOTOVOLTAIC SYSTEM’S INVERTER
3. 5.3 INTEGRATING PHOTOVOLTAIC SYSTEMS IN POWER SYSTEM
4. 5.4 RAPSim
5. FURTHER READING
6 PV SYSTEM SIZE OPTIMIZATION
1. 6.1 INTRODUCTION
2. 6.2 STAND‐ALONE PV SYSTEM SIZE OPTIMIZATION
3. 6.3 HYBRID PV SYSTEM SIZE OPTIMIZATION
4. 6.4 PV PUMPING SYSTEM SIZE OPTIMIZATION
5. FURTHER READING
INDEX
END USER LICENSE AGREEMENT

List of Tables

Chapter 02
1. TABLE 2.1 PV Module Datasheet
2. TABLE 2.2 Evaluation Statistics for All Models
3. TABLE 2.3 Statistical Values and Time Consumption of Methodologies in PV Output Current Prediction
Chapter 03
1. TABLE 3.1 Control Actions for Various Operating Points in the P&O Method
2. TABLE 3.2 The Characteristics of PMDC Motor
3. TABLE 3.3 The Characteristics of Pump
Chapter 04
1. TABLE 4.1 Comparison between Load‐Following and Cycle‐Charging Control Strategies of Hybrid PV/DG/Battery Systems
Chapter 05
1. TABLE 5.1 Inverter Models Coefficients

List of Illustrations

Chapter 01
1. FIGURE 1.1 Earth rotation orbit around the Sun.
2. FIGURE 1.2 The Sun’s altitude and azimuth angles.
3. FIGURE 1.3 Solar declination angle.
4. FIGURE 1.4 A day’s profile of the Sun’s altitude and azimuth angles (Example 1.2).
5. FIGURE 1.5 Spectral emissive power of a 288 K blackbody, for wavelengths in the range of (1–60) µm (Example 1.3).
6. FIGURE 1.6 Calculation of extraterrestrial solar radiation on a horizontal surface.
7. FIGURE 1.7 Daily extraterrestrial solar radiation for Nablus city (Example 1.4).
8. FIGURE 1.8 Components of global solar radiation on a horizontal surface.
9. FIGURE 1.9 Global solar radiation for Kuwait City (Example 1.5).
10. FIGURE 1.10 Solar radiation component on a tilted surface.
11. FIGURE 1.11 Global solar radiation on horizontal and tilted surfaces for Kuwait City (Example 1.6).
12. FIGURE 1.12 Modeling of global solar radiation on a horizontal surface using linear model.
13. FIGURE 1.13 Modeling of diffuse solar radiation on a horizontal surface using linear model.
14. FIGURE 1.14 Topology of the ANN used to model the global solar energy.
15. FIGURE 1.15 ANN model for diffuse solar energy prediction.
16. FIGURE 1.16 Hybrid ANN model for global and diffuse solar radiation prediction.
17. FIGURE 1.17 Prediction results of ANN model in Example 1.8.
18. FIGURE 1.18 GRNN model for solar radiation prediction.
19. FIGURE 1.19 CFNN model for solar radiation prediction.
20. FIGURE 1.20 Geometrical angles of the Sun’s projection.
21. FIGURE 1.21 Optimum tilt angle results (Example 1.9).
Chapter 02
1. FIGURE 2.1 Equivalent circuit of solar cell.
2. FIGURE 2.2 I–V characteristic curve of a solar cell.
3. FIGURE 2.3 I–V and P–V characteristic PV module at 1000 W/m² and 25°C (Example 2.1).
4. FIGURE 2.4 Double‐diode electrical equivalent circuit of solar cell.
5. FIGURE 2.5 Schematic diagram of a PV module.
6. FIGURE 2.6 I–V curve at two radiation values.
7. FIGURE 2.7 Effect of temperature on PV module.
8. FIGURE 2.8 Schematic diagram of GRNN.
9. FIGURE 2.9 Schematic diagram of CFNN.
10. FIGURE 2.10 Schematic diagram of FFNN.
11. FIGURE 2.11 Output current prediction for normal day in March using all models.
12. FIGURE 2.12 Output current prediction for cloudy day in March using all models.
13. FIGURE 2.13 Flowchart of the RF algorithm.
14. FIGURE 2.14 PV output current by ANN‐based model and RF model through 72 h.
15. FIGURE 2.15 I–V characterizing PV module using DE algorithm.
16. FIGURE 2.16 P–V characterizing PV module using DE algorithm.
Chapter 03
1. FIGURE 3.1 PV system operating points with varying loads.
2. FIGURE 3.2 I‐V curve under different values of radiation.
3. FIGURE 3.3 P‐V curve under different values of radiation.
4. FIGURE 3.4 P&O‐based MPPT method.
5. FIGURE 3.5 The basis of the IC method.
6. FIGURE 3.6 Incremental conductance method.
7. FIGURE 3.7 Typical efficiency curve for an inverter.
8. FIGURE 3.8 Inverter output model no. 1.
9. FIGURE 3.9 Inverter output model no. 2.
10. FIGURE 3.10 Physical model of the battery in the charging mode.
11. FIGURE 3.11 Wind turbine power characteristic curve.
12. FIGURE 3.12 Optimum monthly tilt angle for a PV array.
Chapter 04
1. FIGURE 4.1 Typical PV system components.
2. FIGURE 4.2 Flowchart for modeling a stand‐alone PV system.
3. FIGURE 4.3 Performance of the designed SAPV system.
4. FIGURE 4.4 Typical components of a hybrid PV/wind system.
5. FIGURE 4.5 Hybrid PV/diesel system configuration.
6. FIGURE 4.6 Flowchart for PV/diesel system simulation.
7. FIGURE 4.7 Performance of the designed hybrid PV/diesel system.
8. FIGURE 4.8 Flowchart of PV/DG/battery system model with load‐following dispatch strategy.
9. FIGURE 4.9 Performance of the designed hybrid PV/DG/battery system under fuzzy day (load following) no. 1.
10. FIGURE 4.10 Performance of the designed hybrid PV/DG/battery system under fuzzy day (load following) no. 2.
11. FIGURE 4.11 Performance of the designed hybrid PV/DG/battery system under clear sky day (load following) no. 1.
12. FIGURE 4.12 Performance of the designed hybrid PV/DG/battery system under clear sky day (load following) no. 2.
13. FIGURE 4.13 Flowchart of PV/DG/battery system model with cycle‐charging dispatch.
14. FIGURE 4.14 Performance of the designed hybrid PV/DG/battery system on a fuzzy day (cycle charging) no. 1.
15. FIGURE 4.15 Performance of the designed hybrid PV/DG/battery system on a fuzzy day (cycle charging) no. 2.
16. FIGURE 4.16 Performance of the designed hybrid PV/DG/battery system on a clear sky day (cycle charging) no. 1.
17. FIGURE 4.17 Performance of the designed hybrid PV/DG/battery system on a clear sky day (cycle charging) no. 2.
Chapter 05
1. FIGURE 5.1 Iterative method for determining the inverter size.
2. FIGURE 5.2 Searching for the optimum inverter size.
3. FIGURE 5.3 Schematic diagram of a grid‐connected PV system.
4. FIGURE 5.4 Flowchart of the general optimization technique for determining optimal placement and sizing of PVDG in a distribution system.
5. FIGURE 5.5 RAPSim graphical user interface.
6. FIGURE 5.6 A simple model.
7. FIGURE 5.7 Solution to Example 5.2.
8. FIGURE 5.8 Solution to Example 5.3.
9. FIGURE 5.9 Solution to Example 5.4.
Chapter 06
1. FIGURE 6.1 The proposed optimization algorithm for determining the design space at desired LLP.
2. FIGURE 6.2 The proposed cost function for obtaining optimal configuration.
3. FIGURE 6.3 Contour plot for different combinations of PV array and storage battery sizes at different LLP values.
4. FIGURE 6.4 Design space for the proposed SAPV system at an LLP of 0.01.
5. FIGURE 6.5 Building integrated hybrid PV/wind/diesel generating system.
6. FIGURE 6.6 The first phase of the proposed optimization algorithm.
7. FIGURE 6.7 The second phase of the proposed optimization algorithm.
8. FIGURE 6.8 Design space for the proposed hybrid PV/wind/diesel system subject to 1% LLP.
9. FIGURE 6.9 Flowchart of the proposed sizing method of PVPS.
10. FIGURE 6.10 Relationship between LCC and LLP for various PV array configurations.
11. FIGURE 6.11 Relationship between LLP and size of storage tank for 20 modules for PV array configuration.

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Library of Congress Cataloging‐in‐Publication Data

Names: Khatib, Tamer, 1985– | Elmenreich, Wilfried.
Title: Modeling of photovoltaic systems using MATLAB® : simplified green codes / by Tamer Khatib, Wilfried Elmenreich.
Description: Hoboken, New Jersey : John Wiley & Sons, Inc., [2016] | Includes bibliographical references and index.
Identifiers: LCCN 2016015707 | ISBN 9781119118107 (cloth) | ISBN 9781119118121 (epub)
Subjects: LCSH: Photovoltaic power generation–Design and construction–Data processing. | MATLAB.
Classification: LCC TK1087 .K53 2016 | DDC 621.31/244028553–dc23
LC record available at https://lccn.loc.gov/2016015707

ABOUT THE AUTHORS

Dr. Tamer Khatib, Energy Engineering and Environment Department, An‐Najah National University, Nablus, Palestine

Tamer is a photovoltaic power systems professional. He holds a B.Sc. degree in electrical power systems from An‐Najah National University, Palestine, as well as a M.Sc. and a Ph.D. degrees in photovoltaic power systems from National University of Malaysia, Malaysia. In addition, he holds a habilitation degree in renewable and sustainable energy from the University of Klagenfurt, Austria. Currently he is an assistant professor at Energy Engineering and Environment Department, An‐Najah National University, Nablus, Palestine. So far, he has published over 85 published research articles, meanwhile his current h‐index is 14. Moreover, he has supervised six Ph.D. and four M.Sc. researches. He is a senior member of IEEE Power and Energy Society and member of the International Solar Energy Society.

Professor Dr. Wilfried Elmenreich, Smart Grids Group, Alpen‐Adria‐Universität Klagenfurt, Klagenfurt, Austria

Wilfried Elmenreich studied computer science at the Vienna University of Technology where he received his master’s degree in 1998. He became a research and teaching assistant at the Institute of Computer Engineering at Vienna University of Technology in 1999. He received his doctoral degree on the topic of time‐triggered sensor fusion in 2002 with distinction. From 1999 to 2007 he was the chief developer of the time‐triggered fieldbus protocol TTP/A and the Smart Transducer Interface standard. Elmenreich was a visiting researcher at Vanderbilt University, Nashville, Tennessee, in 2005 and at the CISTER/IPP‐HURRAY Research Unit at the Polytechnic Institute of Porto in 2007. By the end of 2007, he moved to the Alpen‐Adria‐Universität Klagenfurt to become a senior researcher at the Institute of Networked and Embedded Systems. Working in the area of cooperative relaying, he published two patents together. In 2008, he received habilitation in the area of computer engineering from Vienna University of Technology. In winter term 2012/2013 he was professor of complex systems engineering at the University of Passau. Since April 2013, he holds a professorship for Smart Grids at Alpen‐Adria‐Universität Klagenfurt. His research projects affiliate him also with the Lakeside Labs research cluster in Klagenfurt. He is a member of the senate of the Alpen‐Adria‐Universität Klagenfurt, senior member of IEEE, and counselor of Klagenfurt’s IEEE student branch. In 2012, he organized the international Advent Programming Contest. Wilfried was editor of four books and published over 100 papers in the field of networked and embedded systems.

FOREWORD

Recently, photovoltaic system theory became an important aspect that is considered in educational and technical institutions. Therefore, the theory of photovoltaic systems has been assembled and introduced in a number of elegant books. In the meanwhile, the modeling methodology of these systems must be also given a focus as the simulation of these systems is an essential part of the educational and the technical processes in order to understand the dynamic behavior of these systems. Thus, this book aims to present simplified coded models for these systems’ component using Matlab. The choice of Matlab codes stands behind the desire of giving the student or the engineer the ability of modifying system configuration, parameters, and rating freely. This book comes with five chapters covering system’s component from the solar source until the end user including energy sources, storage, and power electronic devices. Moreover, common control methodologies applied to these systems are also modeled. In addition to that auxiliary components to these systems such as wind turbine, diesel generators and pumps are considered as well.

In general the readership of this book includes researchers, students, and engineers who work in the field of renewable energy and specifically in photovoltaic system. Moreover, the book can be used mainly or partially as a textbook for the following courses:

Modeling of photovoltaic systems
Modeling of solar radiation components
Computer application for photovoltaic systems
Photovoltaic theory

The authors of this book believe that this book will helpful for any researcher who is interested in developing Matlab codes for photovoltaic systems, whereas many of the basic parts of system models are provided.

MODELING OF PHOTOVOLTAIC SYSTEMS USING MATLAB®

Simplified Green Codes

ABOUT THE AUTHORS

FOREWORD

ACKNOWLEDGMENT