Intelligent paradigms for smart grid and renewable energy systems

Description

Preface Contents About the Editors Artificial Swarm Intelligence—A Paradigm Shift in Prediction, Decision-Making and Diagnosis 1 Introduction to Swarm Intelligence 2 Artificial Swarm Intelligence 2.1 Key Components 2.2 Potential of ASI for a Possible Application in Smart Grid and Renewable Energy Resources Systems 3 Collection to Convergence 3.1 Smart Grid and Renewable Energy Systems 3.2 Swarm Intelligence Algorithms 4 Optimization—Prediction, Decision-Making and Diagnosis 4.1 Possibility and Probability 4.2 Research Perspective of ASI 5 Conclusions References Robust Optimal Tuning of UPFC Using Single Objective Optimization Algorithm 1 Introduction 2 UPFC 3 Proposed UPFC Damping Controller 4 Particle Swarm Optimization 5 Results and Discussion 5.1 Nominal Load Operating Condition 5.2 Heavy Load Operating Condition 5.3 Light Load Operating Condition 5.4 Stability Analysis 6 Conclusion References Hybrid Sensorless MPPT Algorithm for Detecting Fast Irradiation Changes and Partial Shading Conditions on PV Systems 1 Introduction 2 PV Characteristics 3 Partial Shading 4 MPPT Algorithms 4.1 Perturb and Observe (P&O) MPPT Algorithm 4.2 Incremental Conductance (INC) MPPT Algorithm 4.3 Proposed Ideology 5 Proposed Algorithm 6 Dual-Mode Single-Stage PV Inverter 7 Performance Validation 7.1 Case 1 7.2 Case 2 8 Conclusion References A New Model of Demand Response in Smart Grid 1 Introduction 2 Restructuring and Deregulation in the Electricity Industry 3 The Effect of Changes in the Electricity Market and the Formation of Demand-Side Management (DSM) 3.1 Energy Efficiency Programs (EE) 3.2 Demand Response Programs (DR) 4 Types of Demand Response Programs 4.1 Incentive-Based Demand Response (IBDR) 4.2 Time-Based Demand Response (TBDR)/Price-Based Demand Response (PBDR) 5 The Benefits of Customer Presence in the Electricity Markets of Smart Grid 5.1 Customer Benefits 5.2 Smart Grid Benefits 5.3 Other Benefits 6 Restrictions on the Use of Demand Response Programs 7 Barriers to the Implementation of Demand Response Programs in the Conventional Non-smart Power System 8 The Effect of Smart Grid and Advanced Infrastructures in the Implementation of DR Programs 9 Presenting a New Method TOU in Smart Grid 9.1 Description of the Proposed New Model 9.2 Case Study 10 Conclusions References A Smart Bidirectional Power Interface Between Smart Grid and Electric Vehicle 1 Introduction 2 Vehicle to Grid 2.1 Smart Grid 3 EV Battery Chargers: Construction and Working of the Power Stages 4 Vehicle Electrification Through Wireless Charging Systems 4.1 Wireless Power Transfer 4.2 Stationary WPT Charging 4.3 Dynamic WPT Charging 4.4 WPT Control Through Field Exposure Control 5 Vehicle-To-Vehicle Power Transfer 5.1 Power Transfer Using the Front-End Power Converters 5.2 V2V Power Transfer Using the Back-End Power Converters 6 Integration of Renewable Energy (RE) for V2G Services 6.1 Spinning Reserve: Ancillary or Auxiliary Service 6.2 Time Shifting 6.3 Active Power Assistance 7 Optimization Techniques for V2G Power Transfer 7.1 Optimization Objective Function(s) for Renewable Energy Integration in V2G Service 8 A Smart Interface: Smart Grid, Smart Homes and EVs 8.1 Innovation in Smart Homes: Different Modes and Approaches: On-Board Mode—EVBC 8.2 Off-Board EV Battery Charger 9 Conclusion References Nature-Inspired Optimization Algorithms for Renewable Energy Generation, Distribution and Management—A Comprehensive Review 1 Introduction 2 Challenges in the Renewable Power Industry 2.1 Hydro Power 2.2 Wind Power 2.3 Solar PV 2.4 Geothermal Power 2.5 Biofuel 2.6 Integrated and Hybrid Optimization of Renewables 3 Role of Nature-Inspired Algorithms for Renewable Power Optimization 3.1 Nature-Inspired Algorithms—A Brief Overview 3.2 Legacy of Nature-Inspired Algorithms—PSO and GA 3.3 Role of Other Nature Inspired Algorithms in Renewable Energy Optimization 4 Role of Other Meta-Heuristics and Optimization Techniques in Renewable Power Sector 5 Conclusion References Learning Automata and Soft Computing Techniques Based Maximum Power Point Tracking for Solar PV Systems 1 Introduction 2 Components of Solar PV System 3 Maximum Power Point Tracking Techniques 3.1 Online MPPT Techniques 3.2 Offline MPPT Techniques 3.3 Hybrid MPPT Techniques 4 Learning Automata 4.1 Learning Automaton 4.2 n-arm Bandit Problem 4.3 Optimization Using Learning Automata 5 Hybrid MPPT Algorithm Using Perturb and Observe Algorithm and Learning Automata 5.1 P&O MPPT Technique 5.2 Variable Step Size P&O MPPT 5.3 Optimization of MPPT Using Learning Automata 6 Performance Analysis of MPPT Algorithms 7 Soft Computing MPPT Techniques 7.1 Fuzzy MPPT for SPV System 7.2 Adaptive Neuro-fuzzy Inference System (ANFIS)-Based MPPT 7.3 GA-Optimized Fuzzy MPPT 8 Conclusions References Fuzzy Logic Controller Based Plug-In EV Battery Charger 1 Introduction 2 Description of the Proposed Charger 2.1 Mode 1: (G2V) 2.2 Mode 2: (V2G) 3 Working of the Controller 4 Simulation Studies of the Proposed Charger 5 Conclusion References Nature-Inspired Algorithms for Maximum Power Point Tracking in Photovoltaic Systems Under Partially Shaded Conditions 1 Introduction 2 Modelling and Description of PV System Under Study 2.1 Modelling of PV System 2.2 Experimental Setup 3 Formulation of the Optimization Problem 4 MPPT Through Nature-Inspired Algorithms 4.1 Particle Swarm Optimization (PSO) 4.2 Artificial Bee Colony Algorithm (ABC) 4.3 Cuckoo Search Algorithm (CSA) 4.4 Ant Colony Optimization (ACO) 4.5 Genetic Algorithm (GA) 4.6 Firefly Algorithm (FA) 5 Performance Evaluation 5.1 Computed and Measured Results 5.2 Performance Measures 5.3 Statistical Analysis 5.4 Discussion of Parameters of the Algorithm 6 Conclusion References Soft Computing Techniques-Based Low Voltage Ride Through Control of Doubly Fed Induction Wind Generator 1 Introduction 1.1 Wind Power System Requirements and Limitations 1.2 Low-Voltage Ride Through 2 Modeling of DFIG and Conventional Vector Control 2.1 Structure and Basic Principle of DFIG 2.2 Modeling of Wind Turbine 2.3 Implementation of MPPT Control 2.4 Modeling of RSC and GSC Control 3 Soft Computing Techniques Based LVRT Enhancement of DFIG 3.1 Description of Proposed Controller 3.2 Design of Fault Confrontation Controller with Fuzzy Logic 3.3 Fault Confrontation Controller Design with Back Propagation Network 3.4 Design of Fault Confrontation Controller with ANFIS 3.5 ANFIS with Triangular Membership Function 4 ANFIS Controller Implementation for LVRT Enhancement 5 Results and Discussion 5.1 Fault Correction Parameter Prediction with Fuzzy/BPN/ANFIS 6 Conclusion References Harmonic Current Estimation of a Non-linear Load Using Artificial Neural Network 1 Introduction 2 Need for Harmonic Estimation 3 Conventional Technique for Harmonic Estimation 4 Artificial Neural Network Technique for Harmonic Estimation 4.1 Cascaded Neural Architecture 4.2 Feedforward Neural Architecture 5 Training of ANN for Harmonic Current Estimation 6 Testing of ANN for Harmonic Current Estimation 7 Hardware Implementation Aspects of ANN Based Estimator 3 8 Conclusion References Demand Response in Smart Residential Buildings 1 Introduction 2 Types of Household Appliances 2.1 Non-deferrable Essential Loads (NDELs) 2.2 Non-deferrable Interruptible Loads (NDILs) 2.3 Deferrable Loads (DLs) 3 Model of Household Components 3.1 Model of NDELs 3.2 Model of NDILs 3.3 Model of DLs 3.4 Model of Battery 3.5 Model of RER 4 Optimal Sizing of RER 4.1 Capital Investment Cost Constraint 4.2 Elements Size Constraint 4.3 Generation and Demand Constraint 5 Novel Residential Energy Management System 5.1 Controlling of NDELs 5.2 Controlling of NDILs 5.3 Controlling of DLs 5.4 Controlling of Battery 6 Case Study 6.1 Optimal Sizing of RER and Battery 6.2 Economic Benefit by NREMS 7 Conclusions References