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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.
Audience
Engineers, scientists, managers, researchers, students, and other professionals working in the field of renewable energy.
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.
Preface xv
1 Optimization Algorithm for Renewable Energy Integration 1Bikash 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 41Souvik 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 79Soham 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
3.3.1.1 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
3.4.1.1 Advantages of Decision Tree 91
3.4.1.2 Disadvantages of Decision Tree 91
3.4.2 Artificial Neural Network 91
3.4.2.1 Advantages of Artificial Neural Network 93
3.4.2.2 Disadvantages of Artificial Neural Network 93
3.4.3 Fuzzy Logic 93
3.4.3.1 Advantages of Fuzzy Logic 94
3.4.3.2 Disadvantages of Fuzzy Logic 94
3.4.4 Artificial Neuro-Fuzzy Inference System 95
3.4.4.1 Advantages of Artificial Neuro-Fuzzy Inference System 95
3.4.4.2 Disadvantages of Artificial Neuro-Fuzzy Inference System 96
3.4.5 Static Vector Machine 96
3.4.5.1 Advantages of Support Vector Machine 97
3.4.5.2 Disadvantages of Support Vector Machine 97
3.4.6 Random Forest 97
3.4.6.1 Advantages of Random Forest 98
3.4.6.2 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 111Ritesh 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
4.2.4.1 Centralized Configurations 115
4.2.4.2 Master Slave Configurations 115
4.2.4.3 String Configurations 115
4.2.4.4 Modular Configurations 115
4.2.5 Inverter Integration in Grid Solar PV System 115
4.2.5.1 Voltage Source Inverter 116
4.2.5.2 Current Source Inverter 117
4.3 Control Strategies for Grid Connected Solar PV System 117
4.3.1 Grid Solar PV System Controller 117
4.3.1.1 Linear Controllers 117
4.3.1.2 Non-Linear Controllers 117
4.3.1.3 Robust Controllers 118
4.3.1.4 Adaptive Controllers 118
4.3.1.5 Predictive Controllers 118
4.3.1.6 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 131Megha 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 157Busireddy Hemanth Kumar and Vivekanandan Subburaj
6.1 Introduction 158
6.2 Multilevel Inverter Topologies 158
6.2.1 Cascaded H-Bridge (CHB) Topology 159
6.2.1.1 Neutral Point Clamped (NPC) Topology 160
6.2.1.2 Flying Capacitor (FC) Topology 160
6.3 Multilevel Inverter Modulation Techniques 161
6.3.1 Fundamental Switching Frequency (FSF) 162
6.3.1.1 Selective Harmonic Elimination Technique for MLIs 162
6.3.1.2 Nearest Level Control Technique 163
6.3.1.3 Nearest Vector Control Technique 164
6.3.2 Mixed Switching Frequency PWM 164
6.3.3 High Level Frequency PWM 164
6.3.3.1 CBPWM Techniques for MLI 164
6.3.3.2 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 179Raunak 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 199Surajit 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
8.6.3.1 Solution Quality 234
8.6.3.2 Computational Time 234
8.6.3.3 Failure Rate 234
8.6.3.4 Convergence Characteristics 234
8.6.3.5 Wilcoxon Signed Rank Test (WSRT) 236
8.7 Conclusion 237
References 239
9 User Interactive GUI for Integrated Design of PV Systems 243SushmitaSarkar, 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
9.2.1.1 Panel Size Calculations 246
9.2.1.2 Battery Sizing 247
9.2.1.3 Inverter Design 248
9.2.1.4 Loss of Load 249
9.2.1.5 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 267Kunjabihari 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 287Dr 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 307Dr 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 345Nagesh 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
13.2.1.1 Simulation Results with Suboptimal Controller Parameters 349
13.2.1.2 PI Controller Without Nonlinear State Variable Feedback 349
13.2.1.3 PI Controller with Nonlinear State Variable Feedback 351
13.2.2 Structure of Type-1 Controller for 3-Level STACOM 354
13.2.2.1 Transient Simulation with Suboptimal Controller Parameters 357
13.2.3 Application of Genetic Algorithm for Optimization of Controller Parameters 357
13.2.3.1 Boundaries of Type-2 Controller Parameters in GA Optimization 359
13.2.3.2 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
13.2.4.1 Transient Simulation with GA Optimized Controller Parameters 361
13.2.5 Optimization Results of Three Level STATCOM with Optimal Controller Parameters 362
13.2.5.1 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
13.4.1.1 Converter Controller 374
13.4.2 A Case Study 375
13.4.2.1 Transient Simulation with Suboptimal Controller Parameters 376
13.4.3 Design of Controller Using GA and Simulation Results 378
13.4.3.1 Description of Optimization Problem and Application of GA 378
13.4.3.2 Transient Simulation 379
13.4.3.3 Eigenvalue Analysis 379
13.5 Conclusion 379
References 386
14 Short Term Load Forecasting for CPP Using ANN 391Kirti 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 409Tripti 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
15.4.1.1 Early Morning 425
15.4.1.2 Forenoon 425
15.4.1.3 Afternoon 426
15.4.2 Influence on Number of Charged EV 426
15.5 Conclusion 428
References 428
About the Editors 435
Index 437
Bikash Das1, SoumyabrataBarik2*, Debapriya Das3 and V. Mukherjee4
1Department of Electrical Engineering, Govt. College of Engineering and Textile Technology, Berhampore, West Bengal, India 2Department of Electrical and Electronics Engineering, Birla Institute of Technology and Science Pilani, K. K. Birla Goa Campus, Goa, India 3Department of Electrical Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India 4Department of Electrical Engineering, Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand, India
*Corresponding author: soumyabratab@goa.bits-pilani.ac.in
Abstract
With the development of society, the electrical power demand is increasing day by day. To overcome the increasing load demand, renewable energy resources play an important role. The common examples of renewable energy resources are solar photovoltaic (PV), wind energy, biomass, fuel-cell, etc. Due to the various benefits of the renewable energy, the incorporation of renewable energy resources into the distribution network becomes an important topic in the field of the modern power system. The incorporation of renewable energy resources may reduce the network loss, improve voltage profile, and improve the reliability of the network. In this current research work, optimum placements of renewable distributed generations (RDGs) (viz. biomass and solar PV) and shunt capacitors have been highlighted. For the optimization of the locations and the sizes of the RDGs and the shunt capacitors, a multi-objective optimization problem is considered in this book chapter in presence of various equality and inequality constraints. The multi-objective optimization problem is solved using a novel mixed-discrete student psychology-based optimization algorithm, where the key inspiration comes from the behaviour of a student in a class to be the best one and the performance of the student is measured in terms of the grades/marks he/she scored in the examination and the efficacy of the proposed method is analyzed and compared with different other optimization methods available in the literature. The multi-objective DG and capacitor placement is formulated with reduction of active power loss, improvement of voltage profile, and reduction of annual effective installation cost. The placement of RDGs and shunt capacitors with the novel proposed method is implemented on two different distribution networks in this book chapter.
Keywords: Renewable energy integration, shunt capacitors, distributed generation, mixed discrete student psychology-based optimization algorithm, distribution networks
In order to satisfy the increasing electricity load demand, electrical power generation needs to be scheduled properly [1-25]. Electrical power sources can be classified into two categories named as non-renewable and renewable sources. Non-renewable sources mainly include fossil fuels [26-45]. To generate electrical power from fossil-fuels, the fossil-fuels need to be burned. But the combustion of fossil-fuel causes pollution which affects the atmosphere. On the other hand, renewable energy resources cause zero or very little pollution. The main drawback of renewable energy resources is that the extraction of energy is dependent on nature [46-55]. In spite of having the disadvantages, the renewable energy resources are gaining more and more interest in the extraction of electrical power and to satisfy the increasing load demand.
To get better benefits, the placement of distributed generation (DG) to the distribution network needs proper strategy and planning [56-71]. Improper placement of DG may lead to increase in network loss, as well as may cause instability to the network. DG injects power into the distribution network. Based on the power injection, the DG sources can be classified into three categories viz.:
UPF DG injects active power only to the network whereas LPF DG injects both active and reactive power to the distribution network. On the other hand, reactive power DG generates only reactive power. An example of UPF DG is solar photovoltaic cells. Biomass and wind turbines can be considered as an example of LPF DG. The shunt capacitor injects reactive power to the network and it can be said as the reactive power DG.
Proper incorporation of renewable distributed generation (RDG) may reduce the network power loss, improve the voltage profile, improve the voltage stability index (VSI), improve reliability, etc. Due to the various benefits of the incorporation of distributed generation to the distribution network, various researchers have considered this topic as their research interest. The literature review reveals that for the placement of DG to the distribution network, various researchers have considered different approaches to optimize the location and the size of the DG sources. The approaches include analytical, classical optimization methods as well as the metaheuristic optimization algorithm. In order to reduce the active power loss of the distribution network, Acharya et al. [1] have proposed an analytical approach to optimize the size and location of the DG source. Gozel and Hocaoglu [2] have proposed another analytical expression to determine the optimum size and location of DG. This approach is based on the current injection method with an objective to reduce the network power loss. Wang and Nehair [3] have proposed an analytical approach to optimize the size of UPF DG [3]. Wang and Nehair have considered different types of load demands of the distribution network [3]. On the other hand, Hung et al. [4] have proposed an analytical expression to optimize the location and size of LPF DG which is capable of supplying both active and reactive power to the distribution network. It may be observed that most of the researchers have developed analytical expressions to determine the optimum size of the DG in order to reduce the network loss. Aman et al. [5] have proposed an analytical approach for optimum placement of DG considering active power loss reduction and improvement of voltage profile of the network. They have considered a voltage sensitivity analysis approach based on the power stability index to determine the size of DG using a stepwise iterative approach. Some researchers [6] have also considered classical optimization methods to optimize the size of DG. At that same time, an analytical approach to determine the optimum location of DG in the distribution network may also be seen in [7]. Analytical methods can be implemented easily and take less computation time. But the direct formulation of complex problems using the analytical method is quite difficult. The application of an analytical approach, to solve complex problems, may lead to inaccurate solutions due to the assumptions made during the problem formulation process.
To overcome the problem, some of the researchers have adopted the linear and nonlinear programming approach for optimizing the DG size [8]. A considerable number of researchers have applied the metaheuristic optimization algorithm for the DG placement problem. Different nature-inspired algorithms like genetic algorithm (GA) [9], tabu search [41], particle swarm optimization (PSO) [42, 43], combined GA-PSO [44], artificial bee colony (ABC) algorithm [45], harmony search algorithm [46, 47], differential evolution (DE) [48], teaching-learning based optimization (TLBO) [49] may be found in the literature. The application of different hybrid optimization algorithms to solve the DG placement problem may also be noticed in the literature [50-53]. In [54], the authors proposed a multi-objective GA-based approach for the optimal positioning of multiple types of DG to reduce investment costs, cost due to annual energy loss and increase the system reliability. Many of the researchers have taken into account the VSI as the primary objective function while optimizing the DG size [55, 56]. Various objective functions have been considered by different researchers to determine the size and the locations of the DG. In [57], considering the probabilistic behavior of renewable resources, the authors attempted to solve the DG optimization problem. Singh and Goswami [58] have considered nodal pricing methodology for optimizing the DG locations and capacity. They have also studied the economical aspect of DG incorporation into the distribution network. At that same time, in [59], the authors have studied the power penetration by the DG sources considering the average hourly load demand. Some other research works on the optimum DG placement problem may also be noticed in the literature [60-65].
In this current book chapter, placement of DG sources including RDGs (such as biomass, solar PV) and shunt capacitor has been considered for the study purpose. The study has been performed by considering a multi-objective function that includes reduction of active power loss, the betterment of voltage profile, and minimization of effective annual installation cost. To optimize the locations of considered DG sources, a novel optimizing technique named mixed-discrete student psychology-based optimization (SPBO) algorithm is used. The proposed algorithm is inspired by the natural behaviour of the students to be the best student in the class. The criteria to be the best student is to perform well in...
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