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Intelligent Renewable Energy Systems Integrating Artificial Intelligence Techniques and Optimization Algorithms

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  • Edition: 1st
  • Format: Hardcover
  • Copyright: 2022-01-19
  • Publisher: Wiley-Scrivener
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Supplemental Materials

What is included with this book?



This collection of papers on artificial intelligence and other methods for improving renewable energy systems, written by industry experts, is a reflection of the state of the art, a must-have for engineers, maintenance personnel, students, and anyone else wanting to stay abreast with current energy systems concepts and technology.

Renewable energy is one of the most important subjects being studied, researched, and advanced in today’s world. From a macro level, like the stabilization of the entire world’s economy, to the micro level, like how you are going to heat or cool your home tonight, energy, specifically renewable energy, is on the forefront of the discussion.

This book illustrates modelling, simulation, design and control of renewable energy systems employed with recent artificial intelligence (AI) and optimization techniques for performance enhancement. Current renewable energy sources have less power conversion efficiency because of its intermittent and fluctuating behavior. Therefore, in this regard, the recent AI and optimization techniques are able to deal with data ambiguity, noise, imprecision, and nonlinear behavior of renewable energy sources more efficiently compared to classical soft computing techniques.

This book provides an extensive analysis of recent state of the art AI and optimization techniques applied to green energy systems. Subsequently, researchers, industry persons, undergraduate and graduate students involved in green energy will greatly benefit from this comprehensive volume, a must-have for any library.


Engineers, scientists, managers, researchers, students, and other professionals working in the field of renewable energy.

Author Biography

Neeraj Priyadarshi, PhD, works in the Department of Energy Technology, Aalborg University, Denmark, from which he also received a post doctorate. He received his M. Tech. degree in power electronics and drives in 2010 from the Vellore Institute of Technology (VIT), Vellore, India, and his PhD from the Government College of Technology and Engineering, Udaipur, Rajasthan, India. He has published over 60 papers in scientific and technical journals and conferences and has organized several international workshops.  He is a reviewer for a number of technical journals, and he is the lead editor for four edited books, including Scrivener Publishing.

Akash Kumar Bhoi, PhD, is an assistant professor in the Department of Electrical and Electronics Engineering at Sikkim Manipal Institute of Technology (SMIT), India. He is also a research associate at Wireless Networks (WN) Research Laboratory, Institute of Information Science and Technologies, National Research Council (ISTI-CRN) Pisa, Italy. He is a member of several technical associations and is an editorial board member for a number of journals. He has published several papers in scientific journals and conferences and is currently working on several edited volumes for various publishers, including Scrivener Publishing.

Sanjeevikumar Padmanaban, PhD, is a faculty member with the Department of Energy Technology, Aalborg University, Esbjerg, Denmark and works with CTIF Global Capsule (CGC), Department of Business Development and Technology, Aarhus University, Denmark. He received his PhD in electrical engineering from the University of Bologna, Italy.  He has almost ten years of teaching, research and industrial experience and is an associate editor on a number of international scientific refereed journals. He has published more than 300 research papers and has won numerous awards for his research and teaching. 

S. Balamurugan is the Head of Research and Development, QUANTS IS & Consultancy Services, India. He has authored or edited 40 books, more than 200 papers in scientific and technical journals and conferences and has 15 patents to his credit. He is either the editor-in-chief, associate editor, guest editor, or editor for many scientific and technical journals, from many well-respected publishers around the world.  He has won numerous awards, and he is a member of several technical societies.

Jens Bo Holm-Nielsen currently works at the Department of Energy Technology, Aalborg University and is head of the Esbjerg Energy Section. He helped establish the Center for Bioenergy and Green Engineering in 2009 and served as the head of the research group. He has served as technical advisor for many companies in this industry, and he has executed many large-scale European Union and United Nation projects.  He has authored more than 300 scientific papers and has participated in over 500 various international conferences.

Table of Contents

Preface xv

1 Optimization Algorithm for Renewable Energy Integration 1
Bikash Das, SoumyabrataBarik, Debapriya Das and V Mukherjee

1.1 Introduction 2

1.2 Mixed Discrete SPBO 5

1.2.1 SPBO Algorithm 5

1.2.2 Performance of SPBO for Solving Benchmark Functions 8

1.2.3 Mixed Discrete SPBO 11

1.3 Problem Formulation 12

1.3.1 Objective Functions 12

1.3.2 Technical Constraints Considered 14

1.4 Comparison of the SPBO Algorithm in Terms of CEC-2005 Benchmark Functions 17

1.5 Optimum Placement of RDG and Shunt Capacitor to the Distribution Network 18

1.5.1 Optimum Placement of RDGs and Shunt

Capacitors to 33-Bus Distribution Network 25

1.5.2 Optimum Placement of RDGs and Shunt Capacitors to 69-Bus Distribution Network 29

1.6 Conclusions 33

References 34

2 Chaotic PSO for PV System Modelling 41
Souvik Ganguli, Jyoti Gupta and Parag Nijhawan

2.1 Introduction 42

2.2 Proposed Method 43

2.3 Results and Discussions 43

2.4 Conclusions 72

References 72

3 Application of Artificial Intelligence and Machine Learning Techniques in Island Detection in a Smart Grid 79
Soham Dutta, Pradip Kumar Sadhu, Murthy Cherikuri and Dusmanta Kumar Mohanta

3.1 Introduction 80

3.1.1 Distributed Generation Technology in Smart Grid 81

3.1.2 Microgrids 81 Problems with Microgrids 81

3.2 Islanding in Power System 82

3.3 Island Detection Methods 83

3.3.1 Passive Methods 83

3.3.2 Active Methods 85

3.3.3 Hybrid Methods 86

3.3.4 Local Methods 87

3.3.5 Signal Processing Methods 87

3.3.6 Classifer Methods 88

3.4 Application of Machine Learning and Artificial Intelligence Algorithms in Island Detection Methods 89

3.4.1 Decision Tree 89 Advantages of Decision Tree 91 Disadvantages of Decision Tree 91

3.4.2 Artificial Neural Network 91 Advantages of Artificial Neural Network 93 Disadvantages of Artificial Neural Network 93

3.4.3 Fuzzy Logic 93 Advantages of Fuzzy Logic 94 Disadvantages of Fuzzy Logic 94

3.4.4 Artificial Neuro-Fuzzy Inference System 95 Advantages of Artificial Neuro-Fuzzy Inference System 95 Disadvantages of Artificial Neuro-Fuzzy Inference System 96

3.4.5 Static Vector Machine 96 Advantages of Support Vector Machine 97 Disadvantages of Support Vector Machine 97

3.4.6 Random Forest 97 Advantages of Random Forest 98 Disadvantages of Random Forest 98

3.4.7 Comparison of Machine Learning and Artificial Intelligence Based Island Detection Methods with Other Methods 99

3.5 Conclusion 99

References 101

4 Intelligent Control Technique for Reduction of Converter Generated EMI in DG Environment 111
Ritesh Tirole, R R Joshi, Vinod Kumar Yadav, Jai Kumar Maherchandani and Shripati Vyas

4.1 Introduction 112

4.2 Grid Connected Solar PV System 113

4.2.1 Grid Connected Solar PV System 113

4.2.2 PhotoVoltaic Cell 114

4.2.3 PhotoVoltaic Array 114

4.2.4 PhotoVoltaic System Configurations 114 Centralized Configurations 115 Master Slave Configurations 115 String Configurations 115 Modular Configurations 115

4.2.5 Inverter Integration in Grid Solar PV System 115 Voltage Source Inverter 116 Current Source Inverter 117

4.3 Control Strategies for Grid Connected Solar PV System 117

4.3.1 Grid Solar PV System Controller 117 Linear Controllers 117 Non-Linear Controllers 117 Robust Controllers 118 Adaptive Controllers 118 Predictive Controllers 118 Intelligent Controllers 118

4.4 Electromagnetic Interference 118

4.4.1 Mechanisms of Electromagnetic Interference 119

4.4.2 Effect of Electromagnetic Interference 120

4.5 Intelligent Controller for Grid Connected Solar PV System 120

4.5.1 Fuzzy Logic Controller 120

4.6 Results and Discussion 121

4.6.1 Generated EMI at the Input Side of Grid SPV System 122

4.7 Conclusion 125

References 125

5 A Review of Algorithms for Control and Optimization for Energy Management of Hybrid Renewable Energy Systems 131
Megha Vyas, Vinod Kumar Yadav, Shripati Vyas, R.R Joshi and Ritesh Tirole

5.1 Introduction 132

5.2 Optimization and Control of HRES 134

5.3 Optimization Techniques/Algorithms 135

5.3.1 Genetic Algorithms (GA) 136

5.4 Use of Ga In Solar Power Forecasting 140

5.5 PV Power Forecasting 142

5.5.1 Short-Term Forecasting 143

5.5.2 Medium Term Forecasting 144

5.5.3 Long Term Forecasting 144

5.6 Advantages 145

5.7 Disadvantages 146

5.8 Conclusion 146

Appendix A: List of Abbreviations 146

References 147

6 Integration of RES with MPPT by SVPWM Scheme 157
Busireddy Hemanth Kumar and Vivekanandan Subburaj

6.1 Introduction 158

6.2 Multilevel Inverter Topologies 158

6.2.1 Cascaded H-Bridge (CHB) Topology 159 Neutral Point Clamped (NPC) Topology 160 Flying Capacitor (FC) Topology 160

6.3 Multilevel Inverter Modulation Techniques 161

6.3.1 Fundamental Switching Frequency (FSF) 162 Selective Harmonic Elimination Technique for MLIs 162 Nearest Level Control Technique 163 Nearest Vector Control Technique 164

6.3.2 Mixed Switching Frequency PWM 164

6.3.3 High Level Frequency PWM 164 CBPWM Techniques for MLI 164 Pulse Width Modulation Algorithms Using Space Vector Techniques for Multilevel Inverters 167

6.4 Grid Integration of Renewable Energy Sources (RES) 167

6.4.1 Solar PV Array 167

6.4.2 Maximum Power Point Tracking (MPPT) 169

6.4.3 Power Control Scheme 170

6.5 Simulation Results 171

6.6 Conclusion 176

References 176

7 Energy Management of Standalone Hybrid Wind-PV System 179
Raunak Jangid, Jai Kumar Maherchandani, Vinod Kumar and Raju Kumar Swami

7.1 Introduction 180

7.2 Hybrid Renewable Energy System Configuration & Modeling 180

7.3 PV System Modeling 181

7.4 Wind System Modeling 183

7.5 Modeling of Batteries 185

7.6 Energy Management Controller 186

7.7 Simulation Results and Discussion 186

7.7.1 Simulation Response at Impulse Change in Wind Speed, Successive Increase in Irradiance Level and Impulse Change in Load 187

7.8 Conclusion 193

References 194

8 Optimization Technique Based Distribution Network Planning Incorporating Intermittent Renewable Energy Sources 199
Surajit Sannigrahi and Parimal Acharjee

8.1 Introduction 200

8.2 Load and WTDG Modeling 204

8.2.1 Modeling of Load Demand 204

8.2.2 Modeling of WTDG 205

8.3 Objective Functions 207

8.3.1 System Voltage Enhancement Index (SVEI) 208

8.3.2 Economic Feasibility Index (EFI) 208

8.3.3 Emission Cost Reduction Index (ECRI) 211

8.4 Mathematical Formulation Based on Fuzzy Logic 212

8.4.1 Fuzzy MF for SVEI 212

8.4.2 Fuzzy MF for EFI 213

8.4.3 Fuzzy MF for ECRI 214

8.5 Solution Algorithm 215

8.5.1 Standard RTO Technique 215

8.5.2 Discrete RTO (DRTO) Algorithm 217

8.5.3 Computational Flow 219

8.6 Simulation Results and Analysis 221

8.6.1 Obtained Results for Different Planning Cases 223

8.6.2 Analysis of Voltage Profile and Power Flow Under the Worst Case Scenarios: 230

8.6.3 Comparison Between Different Algorithms 231 Solution Quality 234 Computational Time 234 Failure Rate 234 Convergence Characteristics 234 Wilcoxon Signed Rank Test (WSRT) 236

8.7 Conclusion 237

References 239

9 User Interactive GUI for Integrated Design of PV Systems 243
SushmitaSarkar, K UmaRao, Prema V, Anirudh Sharma C A, Jayanth Bhargav and ShrikeshSheshaprasad

9.1 Introduction 244

9.2 PV System Design 245

9.2.1 Design of a Stand-Alone PV System 245 Panel Size Calculations 246 Battery Sizing 247 Inverter Design 248 Loss of Load 249 Average Daily Units Generated 249

9.2.2 Design of a Grid-Tied PV System 250

9.2.3 Design of a Large-Scale Power Plant 251

9.3 Economic Considerations 252

9.4 PV System Standards 252

9.5 Design of GUI 252

9.6 Results 255

9.6.1 Design of a Stand-Alone System Using GUI 255

9.6.2 GUI for a Grid-Tied System 257

9.6.3 GUI for a Large PV Plant 259

9.7 Discussions 260

9.8 Conclusion and Future Scope 260

9.9 Acknowledgment 261

References 261

10 Situational Awareness of Micro-Grid Using Micro-PMU and Learning Vector Quantization Algorithm 267
Kunjabihari Swain and Murthy Cherukuri

10.1 Introduction 268

10.2 Micro Grid 269

10.3 Phasor Measurement Unit and Micro PMU 270

10.4 Situational Awareness: Perception, Comprehension and Prediction 272

10.4.1 Perception 273

10.4.2 Comprehension 274

10.4.3 Projection 280

10.5 Conclusion 280

References 280

11 AI and ML for the Smart Grid 287
Dr M K Khedkar and B Ramesh

Abbreviations 288

11.1 Introduction 288

11.2 AI Techniques 291

11.2.1 Expert Systems (ES) 291

11.2.2 Artificial Neural Networks (ANN) 291

11.2.3 Fuzzy Logic (FL) 292

11.2.4 Genetic Algorithm (GA) 292

11.3 Machine Learning (ML) 293

11.4 Home Energy Management System (HEMS) 294

11.5 Load Forecasting (LF) in Smart Grid 295

11.6 Adaptive Protection (AP) 297

11.7 Energy Trading in Smart Grid 298

11.8 AI Based Smart Energy Meter (AI-SEM) 300

References 302

12 Energy Loss Allocation in Distribution Systems with Distributed Generations 307
Dr Kushal Manohar Jagtap

12.1 Introduction 308

12.2 Load Modelling 311

12.3 Mathematicl Model 312

12.4 Solution Algorithm 317

12.5 Results and Discussion 317

12.6 Conclusion 341

References 341

13 Enhancement of Transient Response of Statcom and VSC Based HVDC with GA and PSO Based Controllers 345
Nagesh Prabhu, R Thirumalaivasan and M.Janaki

13.1 Introduction 346

13.2 Design of Genetic Algorithm Based Controller for STATCOM 347

13.2.1 Two Level STACOM with Type-2 Controller 348 Simulation Results with Suboptimal Controller Parameters 349 PI Controller Without Nonlinear State Variable Feedback 349 PI Controller with Nonlinear State Variable Feedback 351

13.2.2 Structure of Type-1 Controller for 3-Level STACOM 354 Transient Simulation with Suboptimal Controller Parameters 357

13.2.3 Application of Genetic Algorithm for Optimization of Controller Parameters 357 Boundaries of Type-2 Controller Parameters in GA Optimization 359 Boundaries of Type-1 Controller Parameters in GA Optimization 360

13.2.4 Optimization Results of Two Level STATCOM with GA Optimized Controller Parameters 360 Transient Simulation with GA Optimized Controller Parameters 361

13.2.5 Optimization Results of Three Level STATCOM with Optimal Controller Parameters 362 Transient Simulation with GA Optimized Controller Parameters 363

13.3 Design of Particle Swarm Optimization Based Controller for STATCOM 364

13.3.1 Optimization Results of Two Level STATCOM with GA and PSO Optimized Parameters 365

13.4 Design of Genetic Algorithm Based Type-1 Controller for VSCHVDC 371

13.4.1 Modeling of VSC HVDC 371 Converter Controller 374

13.4.2 A Case Study 375 Transient Simulation with Suboptimal Controller Parameters 376

13.4.3 Design of Controller Using GA and Simulation Results 378 Description of Optimization Problem and Application of GA 378 Transient Simulation 379 Eigenvalue Analysis 379

13.5 Conclusion 379

References 386

14 Short Term Load Forecasting for CPP Using ANN 391
Kirti Pal and Vidhi Tiwari

14.1 Introduction 392

14.1.1 Captive Power Plant 394

14.1.2 Gas Turbine 394

14.2 Working of Combined Cycle Power Plant 395

14.3 Implementation of ANN for Captive Power Plant 396

14.4 Training and Testing Results 397

14.4.1 Regression Plot 397

14.4.2 The Performance Plot 398

14.4.3 Error Histogram 399

14.4.4 Training State Plot 399

14.4.5 Comparison between the Predicted Load and Actual Load 401

14.5 Conclusion 403

14.6 Acknowlegdement 403

References 404

15 Real-Time EVCS Scheduling Scheme by Using GA 409
Tripti Kunj and Kirti Pal

15.1 Introduction 410

15.2 EV Charging Station Modeling 413

15.2.1 Parts of the System 413

15.2.2 Proposed EV Charging Station 414

15.2.3 Proposed Charging Scheme Cycle 414

15.3 Real Time System Modeling for EVCS 415

15.3.1 Scenario 1 415

15.3.2 Design of Scenario 1 418

15.3.3 Scenario 2 419

15.3.4 Design of Scenario 2 421

15.3.5 Simulation Settings 422

15.4 Results and Discussion 424

15.4.1 Influence on Average Waiting Time 424 Early Morning 425 Forenoon 425 Afternoon 426

15.4.2 Influence on Number of Charged EV 426

15.5 Conclusion 428

References 428

About the Editors 435

Index 437

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