Enhancement of Grid-Connected Photovoltaic Systems Using Artificial Intelligence

Description

Preface Contents Abbreviations Symbols List of Figures List of Tables Chapter 1: Introduction 1.1 Distributed Generation 1.2 Photovoltaic-Based Distributed Generation 1.3 Distributed Static Compensator (D-STATCOM) 1.4 Main Objectives of Book 1.5 Book Outline Chapter 2: Literature Review and Power Quality Issues 2.1 Literature Survey 2.2 Distributed Generation Background 2.2.1 Definition of Distributed Generation 2.3 The Cost Structure of Distributed Generation Technologies 2.4 Capital Costs 2.5 Distributed Generation Applications and Technology 2.5.1 Distributed Generation Applications 2.5.1.1 Continuous Power 2.5.1.2 Combined Heat and Power (CHP) 2.5.1.3 Peaking Power 2.5.1.4 Green Power 2.5.1.5 Premium Power 2.5.1.6 Emergency Power System 2.5.1.7 Standby Power System 2.5.1.8 True Premium Power System 2.5.1.9 Transmission and Distribution Deferral 2.5.1.10 Ancillary Service Power 2.5.2 Distributed Generation Technologies 2.5.2.1 Reciprocating Engines 2.5.2.2 Microturbines 2.5.2.3 Industrial Combustion Turbines 2.5.2.4 Photovoltaic 2.5.2.5 Fuel Cells 2.5.2.6 Wind Turbine Systems 2.6 Power Quality Impact of PV-DG 2.7 Distributed Static Compensator (D-STATCOM) 2.8 Photovoltaic Energy Systems 2.8.1 Solar Photovoltaic 2.8.2 Grid-Connected PV System 2.9 FACTS Devices 2.9.1 Types of FACTS Controllers 2.9.2 Shunt Compensation 2.9.3 Series Compensation 2.9.4 PV-DG Operation Modes 2.10 D-FACTS Devices 2.11 Integration of DGs with Distribution Networks 2.12 Power Quality 2.13 Power Quality Disturbances Classification 2.14 Power Quality Issues 2.15 Classification of Power Quality Disturbances 2.15.1 Transient Power Quality Disturbance 2.15.2 Short-Duration Voltage Variations 2.15.3 Long-Duration Voltage Variations 2.15.4 Waveform Distortion 2.15.5 Flicker 2.15.6 Frequency Variations 2.16 Questions Chapter 3: Stochastic Optimal Planning of Distribution System Considering Integrated Photovoltaic-Based DG and D-STATCOM 3.1 Distributed Network 3.2 Backward/Forward Sweep (BFS) Algorithm 3.3 Forward/Backward Power Flow 3.4 Problem Formulation 3.4.1 The Objective Functions 3.4.1.1 Single Objective Function 3.4.1.1.1 Voltage Stability Index 3.4.1.1.2 Voltage Deviation 3.4.1.1.3 The Total Annual Cost 3.4.1.2 Two-Objective Function 3.4.1.2.1 Multi-Objective Function 3.4.1.2.2 The Total Annual Cost 3.5 System Constraints 3.5.1 Equality Constraints 3.5.2 Inequality Constraints 3.5.2.1 Bus Voltage Constraints 3.5.2.2 Line Capacity Limits 3.5.2.3 Penetration Level 3.5.2.4 Load Demand Modeling 3.6 Uncertainty Modeling 3.6.1 Modeling of the Solar Irradiance 3.6.2 Load Demand Modeling 3.7 Sensitivity Analysis Chapter 4: Optimal Allocation of Distributed Energy Resources Using Modern Optimization Techniques 4.1 Metaheuristic Optimization Techniques 4.1.1 Marine Predators Algorithm (MPA) 4.1.2 Initialization 4.1.3 Assigning the Top Predator 4.1.4 The Brownian and Lévy Flight Orientations 4.1.5 FDAs Effect and Eddy Formation 4.1.6 Marine Memory 4.2 Equilibrium Optimizer (EO) 4.3 Lightning Attachment Procedure Optimization (LAPO) 4.4 Sine Cosine Algorithm (SCA) 4.4.1 Enhanced Sine Cosine Algorithm (ESCA) 4.5 Ant Lion Optimizer (ALO) 4.5.1 Random Movement of an Ant 4.5.2 Trapping in Antlion Pits 4.5.3 Sliding Ants Toward Antlions 4.5.4 Elitism 4.5.5 Catching Prey and Rebuilding the Pit 4.6 Modified Ant Lion Optimizer (MALO) 4.7 Whale Optimization Algorithm (WOA) 4.7.1 Inspiration 4.7.2 Mathematical Model and Optimization Algorithm 4.7.2.1 Circling Prey 4.7.2.2 Bubble-Net Attacking Method 4.7.2.3 Shrinking Encircling Mechanism 4.7.2.4 Spiral Updating Position 4.7.2.5 Search for Prey 4.8 Slime Mold Algorithm (SMA) 4.8.1 Approach Food 4.8.2 Wrap Food 4.8.3 Grabble Food Chapter 5: Results and Discussion 5.1 IEEE-30 Bus Radial Distribution System 5.1.1 Optimal Allocation of PV and D-STATCOM in RDS Using ALO Algorithm 5.2 IEEE-69 Bus and IEEE-118 Bus Radial Distribution System 5.3 Optimal Allocation of DER in RDS Using ALO and MALO 5.3.1 Case 1: Optimal Installation of PV System Under the Deterministic Condition 5.3.2 Case 2: Optimal Planning Under Uncertainties of System 5.3.2.1 IEEE-69 Bus System 5.3.2.2 IEEE-118 Bus System 5.4 Optimal Allocation of DER in RDS Using LAPO and EO Compared with WOA and SCA 5.4.1 IEEE-118 Bus Radial Distribution System 5.4.1.1 Case 1: Single PV and Single (D-STATCOM) 5.4.1.2 Case 2: Two PV Units and Two (D-STATCOMs) 5.5 Optimal Allocation of DER in RDS Using MPA and PSO 5.5.1 IEEE-94 Bus Radial Distribution System 5.6 Case 1: Allocation of the Hybrid System Without Considering the Uncertainty 5.6.1 Single Hybrid PV-DG and D-STATCOM 5.6.2 Two Hybrid PV-DG and D-STATCOM Systems 5.7 Case 2: Allocation of the Hybrid System Considering the Uncertainty 5.7.1 Single Hybrid PV-DG and D-STATCOM Considering the Uncertainties 5.7.2 Two Hybrid PV-DG and D-STATCOM Considering the Uncertainties 5.8 Optimal Allocation of DER in RDS Using SMA 5.8.1 IEEE-30 Bus Radial Distribution System 5.9 Optimal Allocation of DER in RDS Using ESCA and SCA 5.9.1 IEEE-33 Bus Radial Distribution System 5.9.2 The First Case Study - 33-Bus System 5.9.3 The Second Case Study - 69-Bus System 5.9.4 Optimal Integral of PV-DG along with D-STATCOMS 5.10 A Comparison Between the Proposed and Conventional Algorithms 5.10.1 IEEE-118 Bus System Chapter 6: Conclusions and Future Work 6.1 Conclusions Appendix Appendix A Appendix B Appendix C Appendix D Appendix E Appendix F Appendix G References Index