Renewable Energy Systems

Sanjay Kumar, PhD, is an assistant professor at the University Institute of Technology, Himachal Pradesh University, Shimla, India. He earned his PhD from the Department of Electrical Engineering at Punjab Engineering College Deemed to be University Chandigarh, India in December 2019. Nikita Gupta, PhD, is a professor in the Department of Electrical Engineering, University Institute of Technology, Himachal Pradesh University, India. She earned her PhD from the Department of Electrical Engineering at Delhi Technological University, Delhi, India, in 2018. She has received multiple awards for her research and is a reviewer of various international conferences and scientific journals. Sandeep Kumar, PhD, is a professor in the Department of Computer Science and Engineering, K L Deemed To Be University, Vijayawada, Andhra Pradesh, India. He completed his postdoc from Pentagram Pvt. Ltd. in August 2021. He has six patents to his credit, with several others pending. Subho Upadhyay, PhD, is a assistant professor at Dayalbagh Educational Institute, Agra, India. He earned his PhD from the Indian Institute of Technology, Roorkee, India in August 2017. He has published several research papers in various journals and conferences and is a reviewer for various international scientific journals and conferences.

• 1 Importance of Hybrid Energy System in Reducing Greenhouse Emissions 1Rupan Das, Somudeep Bhattacharjee and Uttara Das1.1 Introduction 21.2 Scenario of Climate Change in the World 51.3 Role of a Hybrid Framework Based on Renewable Energy 71.4 Proposed Model Description 101.5 Mathematical Model of Hybrid System 111.5.1 Solar PV System 111.5.2 Wind Energy System 121.5.3 Diesel Generator 131.5.4 Renewable Voltage Stabilizing Controller 141.5.5 Inverter 141.6 Simulation Model of the Hybrid Energy System 151.6.1 Solar PV System Simulation 161.6.2 Wind Energy System Simulation 171.6.3 Diesel Generator Simulation 171.6.4 Renewable Voltage Stabilizing Controller Simulation 171.7 Results of Simulation Analysis 191.7.1 Hybrid Renewable Energy System Simulation Results 191.7.2 Solar PV Simulation Results 191.7.3 Wind Generation System Simulation Results 201.7.4 Inverter Simulation Result 211.8 Conclusion and Discussion 22Acknowledgments 23References 232 Experimental Study on Tilt Angle and Orientation of Rooftop PV Modules for Maximising Power Output for Chandigarh, India 29Tarlochan Kaur, Isha Arora, Jaimala Gambhir, Ravneet Kaur and Ayush Gera2.1 Introduction 302.2 Literature Review 322.3 Experimental Setup 372.3.1 Location Under Study 372.3.2 Experimental Setup 382.3.3 Methodology Used 402.4 Experimental Results and Discussion 402.4.1 Orientation Optimisation of PV Modules 402.4.2 Tilt Angle Optimisation of PV Modules 432.4.2.1 Absolute Maximum Monthly Energy Values Method 432.4.2.2 Weighted Frequency Count (WFC) Method 432.4.2.3 Weighted Maximum Energy (WME) Method 442.4.3 Mutual Shading of PV Modules on Account of Row Spacing 452.5 Latitude and Optimal Tilt Angle 522.6 Conclusions and Future Scope 54Acknowledgment 55References 563 Biodiesel, Challenges and Solutions 61Mukesh Kumar and Mahendra Pal Sharma3.1 Introduction 623.2 Significant Challenges Faced by Biodiesel 623.2.1 Low Oil Yields and Slow Growth Rate 623.2.2 Selection of Potential Feedstocks 633.3 Conversion of Microalgae into Biodiesel 663.3.1 Transesterification 663.3.2 Direct (In Situ) Transesterification 743.4 Microalgae Biodiesel 763.5 Conclusion 81References 824 Comparative Overview of a Novel Configuration of a DC-AC Converter with Reduced Components 91Himanshu Sharma, Kamaldeep and Rahul Dogra4.1 Introduction 914.2 The Novel Topology 944.2.1 State of Operation of the Proposed Inverter 954.2.1.1 First Operating Mode 954.2.1.2 Second Operating Mode 964.2.1.3 Third Operating Mode 974.2.2 Boost Factor Calculation 974.2.3 RMS Value of the Output Voltage 984.3 Performance Characteristics 984.3.1 Boost Factor and Shoot-Through Duty Ratio Variation 984.3.2 Output Voltage Variation with Shoot-Through Duty Ratio 994.3.3 Boost Factor and THD Variation 1004.3.4 Capacitor Voltage Stress 1044.4 Modulation Technique 1044.5 Simulation Results 1064.5.1 Simulation Results with MATLAB 1064.5.2 Simulation Results with Real-Time Simulator 1094.6 Critical Analysis of Proposed Topology with the Conventional Z-Source Inverter 1114.7 Conclusion 113References 1145 Intelligent Sliding Mode Controller for Wind Energy Powered DC Nanogrid 117Saurabh Kumar, Vijayakumar K., Ashok Bhupathi Kumar Mukkapati and Rajvir Kaur5.1 Introduction 1185.2 Overview of Wind Energy Conversion System 1225.3 System Description 1245.4 Controller Description 1255.4.1 Particle Swarm Optimization 1305.5 Results and Analysis 1315.5.1 Comparative Study 1335.6 Conclusion 135References 1366 Grid Integration of Renewable Energy Systems 139Pallavi Verma, Rachana Garg and Priya Mahajan6.1 Introduction 1396.2 Modelling of Grid-Interconnected Solar PV System 1416.2.1 SPV System 1426.2.2 DC-DC Converter 1436.2.3 PV Inverter 1446.3 Design of Grid-Interconnected Solar PV System 1446.3.1 Design of Solar PV Array 1446.3.2 Inductor for Boost Converter (Lb) 1446.3.3 Selection of Diode and IGBT for Boost Converter 1456.3.4 Choice of DC-Link Voltage (Vdc) 1456.3.5 Sizing of DC-Link Capacitor (Cdc) 1466.3.6 Interfacing Inductors (Lr) 1466.4 PV Inverter Control Techniques 1476.4.1 Synchronous Reference Frame Theory 1476.4.2 Unit Template-Based Control Algorithm 1496.4.3 Fuzzy Logic Control (FLC) Algorithm 1506.4.3.1 Fuzzification 1506.4.3.2 Inference Process 1506.4.3.3 Defuzzification 1516.4.4 LMS-Based Adaptive Control Algorithm 1516.5 MATLAB/Simulink Results and Discussion 1546.5.1 Linear/Non-Linear Load Under Steady-State Condition 1546.5.2 Linear/Non-Linear Load Under Dynamic Condition 1566.5.3 Linear/Non-Linear Load with Change in Irradiation 1586.5.4 Linear/Non-Linear Unbalanced Loading Condition 1606.5.5 Comparison of LMS-Based Adaptive Control Algorithm with Other Control Algorithms in Terms of Total Harmonics Distortion (THD) 1616.6 Conclusion 162Appendix 162References 1637 Modeling and Analysis of Autonomous Hybrid Green Microgrid System for the Electrification of Rural Area 167Sumit Sharma, Yog Raj Sood, Ankur Maheshwari and Pallav7.1 Introduction 1677.2 Renewable Energy Technologies 1747.3 Economic Evaluation 1757.4 Microgrid Protection 1777.5 Simulation Results and Discussion 1797.5.1 MIC – A: SPV/Wind/Biomass Generator/ Hydro/Battery/Converter 1827.5.2 MIC – B: SPV/Wind/Diesel Generator/ Hydro/Battery/Converter 1827.6 Conclusion 185References 1868 Performance Optimization of a Pine Oil-Fueled Agricultural Engine Using Grey – Taguchi Approach 191Rajesh Kumar, Manoj Gwalwanshi, Vikas Verma, Rahul Tarodiya and Manoj Kumar8.1 Introduction 1928.1.1 Taguchi Method 1968.1.2 Grey Relational Analysis 1978.2 Experimental Setup and Procedure 1988.2.1 Experimental Setup 1988.2.2 Error Analysis 2008.3 Grey-Taguchi Analysis 2008.4 Taguchi – SN Ratio 2078.4.1 Analysis of Variance (ANOVA) 2088.4.2 Confirmatory Experiments 2098.5 Results and Discussion 2108.6 Conclusion 211Acknowledgment 211References 2119 Nonlinear Mathematical Modeling and Energy Optimization of Multiple-Stage Evaporator Amalgamated with Thermo-Vapor Compressor 217Smitarani Pati, Om Prakash Verma, Varun Sharma and Tarun Kumar Sharma9.1 Introduction 2199.2 Process Description 2239.3 Nonlinear Energy Modeling 2249.3.1 Material Balance Equations 2269.3.2 Energy Balance Equations 2269.3.3 Thermo-Vapor Compressor (TVC) 2289.4 Formulation of the Objective Function 2299.5 Solution Approach 2309.6 Result and Discussion 2329.7 Validity of the Proposed Model 2349.8 Conclusion 242References 24310 Fuel Cell Fed Shunt Active Power Filter for Power Quality Issue by Electric Vehicle Charging 247Ravinder Kumar and Hari Om Bansal10.1 Introduction 24710.2 Specification of the Fuel Cell Integrated SAPF 24910.2.1 Proton Exchange Membrane Fuel Cell 25010.3 Reference Current Generation 25210.3.1 ANFIS-Based Control Algorithm 25410.4 Discussion and Simulation Findings 25510.5 Results and Discussion in Real Time 25810.6 Conclusions 261References 26111 In-Depth Analysis of Various Aspects of Charging Station Infrastructure for Electric Vehicle 265Shubham Mishra, Shrey Verma, Gaurav Dwivedi and Subho Upadhyay11.1 Introduction 26511.2 Classification of Electric Vehicles 26811.2.1 Hybrid Electric Vehicles (HEVs) 26911.2.2 Plug-In Electric Vehicles (PEVs) 26911.2.3 Fuel Cell Electric Vehicles (FCEVs) 26911.3 Energy Storage Technologies Used in EVs 26911.3.1 Battery 27011.3.2 Super Capacitor (SC) 27111.3.3 Flywheel 27111.3.4 Hydrogen Storage 27111.4 Types of Electric Vehicle Charging Station (EVCS) 27111.5 Aspects and Challenges in the Development of EV Charging Infrastructure 27111.5.1 Determining the Optimal Location for Establishing Ev Charging Stations 27311.5.2 Ensuring an Optimized and Well-Planned Operation Management 27311.5.3 Reducing EV Charging Time by Establishment of High-Class Charging Techniques and Battery Swapping Method 27411.5.4 Strategically Handling the Queues of EVs at the Charging Station 27511.5.5 Establishing a Promising Structure for Integration with Grid 27511.5.6 A Proper Communication Channel for Managing the Grid Operation 27511.5.7 Impact on the Environment by EV Charging Station Infrastructure 27611.5.8 Impact on Power System Expansion by an Increased Rate of EV Adoption 27611.5.9 Proper Sizing of Energy Storage Technologies 27611.5.10 Sizing and Proper Methodology for the Use of Renewable Energy Technologies that will Fulfill the Electricity Demand of the Charging Station with or Without Integrating with the Power Grid 27711.5.11 Use of Energy Storage Technologies and Charging Techniques to Enhance Stability 27811.5.12 Determining the Peak Hours for Managing the Charging Load Demand on the Grid for Stable Operation 27911.5.13 Estimating a Customer-Friendly as well as Profit-Making Charging Rate 28011.6 Developments in the Sector of Electric Vehicles and its Charging Stations in India 28111.7 Conclusion 283References 28412 Optimization of PV Electrolyzer for Hydrogen Production 295Sudipta Saikia, Vikas Verma, Sivasakthivel Thangavel, Rahul Tarodiya and Rajesh Kumar12.1 Introduction 29612.2 Hydrogen as a Potential Fuel for the Future 29712.3 Properties of Hydrogen 29812.4 Fundamental Concepts of Hydrogen Production Processes 29912.4.1 Water Electrolysis – Thermodynamic Reactions 30012.4.2 Factors Impacting the Rate of Efficiency of Water Electrolysis 30212.4.3 Classification of Electrolyzers 30312.4.4 Selection Criterion of Electrodes 30512.4.5 Effects of Changing Operating Parameters, Sizes and Electrolytic Concentration 30612.5 System Description and Components 30712.6 Electrochemical Equations 30812.7 Methodology 31012.7.1 Taguchi Technique 31012.7.2 Taguchi – Design of Experiments 31112.7.3 Steps of Taguchi Technique 31212.8 Results and Discussion 31412.8.1 Taguchi Process – Operating Factors for the Perforated Electrolyzer 31412.8.2 Taguchi Process – Result of Signal-to-Noise (S/N) Ratio 31712.8.3 Taguchi Process – Analysis of Variance (anova) 31912.8.4 Confirmation Test 319Conclusions 322References 32313 Assessment of GAMS in Power Network Applications Including Wind Renewable Energy Source 327Vineet Kumar, R. Naresh, Veena Sharma and Vineet Kumar13.1 Introduction 32813.1.1 General Background and Motivation 32913.1.2 Goal and Challenging Focus 33013.2 Importance and a User’s View on GAMS Software 33313.2.1 Models for Academic Research 33413.2.2 Models for Domain Expert 33513.2.3 Black Box Models 33613.3 The Basic Structure in the GAMS Environment 33713.3.1 Input Command 33913.3.2 Output Command 34013.4 Power System Applications Using GAMS Software 34013.4.1 Multi-Area Economic Dispatch (ED) 34113.4.2 AC Optimal Power Flow 34413.5 Development Trends in GAMS 35513.6 Conclusion 357Acknowledgments 358References 35814 Multi-Objective Design of Fractional Order Robust Controllers for Load Frequency Control 365Nitish Katal and Sanjay Kumar Singh14.1 Introduction 36614.2 Mathematical Model of Single Area Load Frequency Control 36714.3 Background 36814.3.1 Fractional-Order PID Controllers 36814.3.2 Multiverse Optimizer 36914.4 Proposed Method to Tune PID Controller 37014.4.1 Formulation of Optimization Problem 37014.4.1.1 Formulation of Objective Function Related to Time-Domain Response 37014.4.1.2 Formulation of Objective Function Related to Robust Control 37114.5 Results and Discussions 37114.5.1 Optimal Controller Synthesis Using Time Domain Approaches 37214.5.2 Optimal Robust Controller Synthesis 37214.6 Frequency Deviation for 0.02 p.u. Load Change 37514.7 Conclusions 376Nomenclature 376References 37715 Challenges and Remedies of Grid-Integrated Renewable Energy Resources 379Subho Upadhyay and Ashwini Kumar Nayak15.1 Introduction 38015.2 Developing a Cost-Effective and Adequate Stand-Alone or Grid-Connected Generation System in a Hilly Area 38115.3 Challenges of Grid-Connected Hybrid Energy System 38315.4 Energy Management 38515.4.1 Cycle Charging Strategy 38615.4.2 Load Following Strategy 38615.4.3 Peak Shaving Strategy 38715.5 Frequency Deviation 38715.6 Voltage Deviation 38915.7 Adequacy Assessment of Intermittent Sources 38915.7.1 Failure Rate of PV System 39015.7.1.1 Configuration of PV Plant 39015.7.1.2 Calculation of Forced Outage Rate of Solar PV System 39315.7.2 Failure Rate of Wind System 39315.7.2.1 WTG Output as a Function of Wind Speed 39315.7.2.2 Determination of DAFOR Using Apportioning Method 39415.7.2.3 Reducing Multistate WECS Using the Apportioning Method 39515.7.3 Power System Planning 39615.8 Conclusion 398References 39916 Solar Radiations Prediction Model Using Most Influential Climatic Parameters for Selected Indian Cities 403Anand Mohan and Gopal Singh16.1 Introduction 40316.2 Introduction to Solar Energy 40416.3 Energy Status 40516.3.1 World Energy Status 40516.3.2 India Energy Status 40516.3.3 Himachal Pradesh Energy Status 40616.4 Existing Solar Technologies 40716.4.1 Solar Thermoelectric Technology 40716.4.2 Photovoltaic Technology 40716.4.2.1 High Efficiency 40816.4.2.2 Thin Films 40816.4.2.3 Organic and Dye-Sensitised 40816.5 Existing Solar Modeling Techniques 40816.5.1 Angstrom Model 40816.5.2 Angstrom-Prescott Model 40916.5.3 Lieu and Jordan Model 41016.6 Relevance for Solar Electrification in Himachal Pradesh 41416.7 Literature Review 41416.7.1 Related Researches 41416.7.2 Gaps in Research Drawn from Literature 41816.7.3 Estimation of Solar Radiation Potential 41816.7.4 Objectives of the Research 41916.8 Methodology Used 42016.8.1 Prediction Model Developed Using Artificial Neural Networks 42016.8.2 Potential Assessment Using ANN 42016.8.3 Identification of Most Influential Parameters 42016.8.4 Artificial Neural Network – A Better Prediction Tool 42016.8.5 Artificial Neural Networks vs. Regression 42416.9 Prediction Model Using Adaptive Neuro-Fuzzy Inference System (ANFIS) 42416.9.1 Potential Assessment Using ANFIS 42516.10 Different Input Variables 42616.10.1 Most Relevant Input Data Selection 42616.10.2 Development of a Database for Different Models 42616.10.3 Designing of Different Models 42716.10.4 Calculation of Maximum Absolute Percentage Error 42816.10.5 Selection of Most Suitable Models 42816.11 Prediction Model for Ten Selected Cities of Himachal Pradesh 42816.11.1 Selection of Input Variables Used for Prediction Model Using ANN 42816.11.2 ANN Dependent Solar Radiation Estimation Models 43116.12 Sensitivity Test and Error Evaluation of SRPM Models 43116.13 Results and Discussion of ANN Model 43216.14 Selection of Inputs Used for Prediction Model Using ANFIS 44216.15 ANFIS-Based Solar Radiation Prediction Models 44216.16 Results and Discussion of ANFIS Model 447References 44717 Quality Improvement by Eliminating Harmonic Using Nature-Based Optimization Technique 453Kamaldeep, Himanshu Sharma, Sanjay Kumar, Arjun Tyagi and Rahul Dogra17.1 Introduction 45417.2 Cascaded H-Bridge Multilevel Inverter 45517.3 Harmonic Elimination 45617.4 Particle Swarm Optimization (PSO) 45817.5 Simulation Results 46217.6 Conclusion 466References 46718 Effect of Degradations and Their Possible Outcomes in PV Cells 469Neha Kumari, Sanjay Kumar Singh and Sanjay Kumar18.1 Introduction 47018.1.1 Photovoltaic Cells – An Approach to a Greener World 47018.2 Basics of Photovoltaic Cell 47218.2.1 History of Semiconductors 47318.2.2 Basics of Semiconductors 47318.2.3 Photovoltaic Effect 47418.2.4 Photovoltaic Cell Efficiency 47518.3 Photovoltaic Technology 47618.3.1 First-Generation Technology – Photovoltaic Cells Based on Crystalline Silicon Wafer 47618.3.1.1 Monocrystalline Silicon Solar Cells (mc-Si) 47718.3.1.2 Polycrystalline Silicon Solar Cells (pc-Si) 47718.3.1.3 Heterojunction Solar Cells (HIT) 47718.3.1.4 PERC Solar Cells 47718.3.2 Second-Generation Technology – Photovoltaic Cells Based on Thin Films 47718.3.2.1 Amorphous Silicon Solar Cells (a-Si) 47818.3.2.2 Cadmium Telluride Solar Cells (CdTe) 47818.3.2.3 Copper Indium Gallium Selenium Solar Cells (CIGS) 47918.3.3 Third-Generation Technology – Photovoltaic Cells Based on Innovative Technology 47918.3.3.1 Organic Solar Cells 48018.3.4 Emerging Technologies 48118.4 Degradation in Photovoltaics 48118.4.1 What is Degradation? 48118.4.2 Types of Degradation in Photovoltaic Cells and Its Consequences 49118.4.2.1 Hotspots 49118.4.2.2 Mechanical Stressing and Cracks 49318.4.3 Other Types of Degradations 49418.4.3.1 Corrosion 49418.4.3.2 Delamination in Photovoltaic Module 49518.4.3.3 Discoloration in Photovoltaic Module 49618.4.3.4 Potential Induced Degradation (PID) 49618.4.3.5 Light-Induced Degradation (LID) 49718.4.3.6 Interconnection Degradation 49718.4.3.7 Packaging Material Degradation 49818.4.3.8 Snail Trails 49818.5 Current Status and Challenges in Photovoltaic Technologies 49918.5.1 Crystalline Silicon Photovoltaic Cells 49918.5.1.1 Current Status and Degradation Level 50018.5.1.2 Challenges 50018.5.2 Thin-Film Photovoltaic Cells 50018.5.2.1 Current Status and Degradation Level 50118.5.2.2 Challenges 50218.5.3 The Innovative Technology 50318.5.3.1 Current Status and Degradation Level 50318.5.3.2 Challenges 50418.6 Cost and Efficiency Trends in Photovoltaics Over the Past Decade 50418.7 Impedance Spectroscopy (IS) – Technique to Identify Degradations in Photovoltaics 50518.7.1 AC Equivalent Model of Solar Cell 50618.7.2 Impedance Spectroscopy 50718.7.3 Procedure for Impedance Spectroscopy 50718.8 Conclusion 510References 511Index 517