Smart Electrical Grid System: Design Principle, Modernization, and Techniques

Description

Smart technologies, such as artificial intelligence and machine learning, play a vital role in modeling, analysis, performance prediction, effective control, and utilization of smart energy systems. This book presents novel concepts in the development of smart cities and smart grids as well as discusses the technologies involved in producing efficient and economically feasible energy technologies around the world. It comprehensively covers important topics, including optimization methods for smart grids, power converters, smart meters, load frequency control, automatic generation control, and power electronics for smart grids. This book focuses mainly on three areas of electrical engineering: control systems, power electronics, and renewable resources, including artificial intelligence for the development of smart electrical grids. Key Features • Clarifies how the smart grid plays an important role in modern smart technologies • Introduces the basic concepts of modernization of smart grid with the assumption of basic knowledge of mathematics and power systems • Describes the structure of technologies based on Internet of Things (IoT), which acts like a bridge to cover the gap between the physical and virtual worlds required for the realization of the smart grid • Includes practical examples of the smart grid and energy saving • Illustrates the integration of renewable energy sources with worked examples • Enables readers to engage with the immediate development of power systems by using smart approaches for future smart grids Cover Half Title Series Page Title Page Copyright Page Table of Contents Preface Editors Contributors Chapter 1 Internet of Things Based Modernization of Smart Electrical Grid 1.1 Introduction 1.2 Pros of Smart Electrical Grid (SEG) 1.3 Existing System 1.4 Research Gaps 1.5 Proposed System 1.6 Methodology 1.6.1 Framework 1.6.2 Component Requirement Analysis 1.7 Implementation 1.8 Functional Requirements 1.9 Results and Discussion 1.10 Conclusions References Chapter 2 Role of Non-Conventional Energy Resources in Today’s Environment 2.1 Introduction 2.2 Types of Power Plants 2.2.1 Nuclear Power Plants 2.2.2 Hydroelectric Power Plants 2.2.3 Coal-Fired Power Plants 2.2.4 Diesel-Fired Power Plants 2.3 Non-Conventional Energy Resources 2.4 Horizontal- and Vertical-Axis WT Designs 2.5 Wind Speed and Direction 2.5.1 Power in the Wind 2.6 Advantages and Disadvantages of Wind Energy 2.6.1 Advantages of Wind Energy 2.6.2 Disadvantages of Wind Energy 2.7 Wind Speed 2.7.1 Wind Water Pumping System 2.8 Wind Energy Generators 2.8.1 DC Generator 2.8.2 AC Synchronous Generator 2.8.3 Induction Generators 2.9 Conclusions References Chapter 3 Flexible Load and Renewable Energy Integration with Impact on Voltage Profile of a Large Size Grid 3.1 Introduction to Existing Scenario 3.2 Problem Identification 3.3 Simulation Model Design 3.4 Power Flow Studies of Multiple Simulated Cases 3.5 Dynamic Simulation Study Comparisons 3.6 Comparison of Existing Grid Generators 3.7 Assessment of Required VAR Support 3.8 Conclusions Acknowledgments References Chapter 4 Energy Storing Devices for Sustainable Environment 4.1 Introduction 4.2 Problem 4.3 Global Status of the Consumption of Energy 4.4 Need to Store Energy 4.5 Energy Storing Technologies 4.6 Classification of Energy Storage 4.6.1 Electrochemical Energy Storage (ECES) 4.6.1.1 Lithium-Ion Batteries (LIB) 4.6.1.2 Lithium–O[sub(2)] Batteries 4.6.1.3 Lithium Cobalt Oxide Batteries 4.6.1.4 Lithium Manganese Oxide Batteries 4.6.1.5 Lithium Nickel Manganese Cobalt Oxide Batteries 4.6.1.6 Lithium Iron Phosphate Batteries 4.6.1.7 Lithium-Titanate Batteries 4.6.1.8 Sodium–Sulfur Batteries 4.6.1.9 Lead–Acid Batteries (LABs) 4.6.1.10 Aluminum-Ion Batteries 4.6.1.11 Copper Zinc Batteries 4.6.1.12 Redox Flow Batteries 4.6.1.13 Vanadium-Based Flow Batteries 4.6.1.14 Metal–Air Batteries 4.6.2 Mechanical Storage 4.6.2.1 Hydroelectric Energy Storage 4.6.2.2 Flywheel 4.6.3 Chemical Storage 4.6.3.1 Hydrogen 4.6.3.2 Methane 4.6.4 Thermal Energy Storage 4.6.4.1 Sensible Heat Storage (SHS) 4.6.4.2 Latent Heat Storage 4.6.4.3 Thermochemical Energy Storage 4.7 Discussion and Analysis 4.8 Challenges and Prospects of Energy Storage Technologies 4.9 Conclusions Nomenclature References Chapter 5 Clean and Green Energy Fundamentals 5.1 Introduction 5.2 Solar Energy 5.2.1 Active and Passive Systems 5.2.2 Solar Photovoltaic Technology (SPVT) 5.3 Hydroelectric Energy 5.3.1 Hydropower in India 5.4 Biomass Energy 5.4.1 Biomass and Environment 5.4.2 Sources of Biomass Energy 5.5 Wind Energy 5.5.1 Short-Term Variability 5.5.1.1 Variations within a Minute 5.5.1.2 Variations within an Hour 5.5.1.3 Variations from Hour to Hour 5.5.2 Long-Term Variability 5.5.2.1 Monthly and Seasonal Variations 5.5.2.2 Inter-Annual Variations 5.5.2.3 Characteristics of Wind 5.5.2.4 Wind Speed 5.5.2.5 Weibull Distribution 5.5.2.6 Wind Turbulence 5.5.2.7 Wind Gust 5.5.2.8 Wind Direction 5.5.3 Challenges in Wind Power Generation 5.5.3.1 Impact on the Environmental Conditions 5.5.3.2 Wind Turbine Noise 5.5.3.3 Integration of Wind Power into Grid 5.5.4 Wind Energy Storage 5.5.5 Offshore wind turbines 5.6 Conclusions References Chapter 6 Evaluation of Sustainable Window for Energy Mitigation in an Electrical Grid Building 6.1 Introduction 6.2 Experimental Setup 6.3 Results and Discussion 6.4 Conclusions References Chapter 7 Filter Bank Multicarrier for Smart Grid Systems 7.1 Introduction 7.2 Filter Bank Multicarrier 7.3 Fast Fourier Transform as Multicarrier Modulator 7.4 FFT’s Filtering Effect 7.5 System Model and FBMC Formulation in Smart Grid 7.6 Self-Equalization Property of FBMC in Smart Grid 7.7 Choice of Filter Bank Structure 7.8 Goal of WP3 References Chapter 8 Recent Trends in Economic Scheduling and Emission Dispatch of Distributed Generators in Microgrids 8.1 Introduction 8.2 Microgrid Architecture 8.3 Economic Dispatch and Emission 8.4 Conclusions References Chapter 9 Forecasting of Tensile–Shear Strength of JSC 590RN Low-Carbon Steel Spot Welds Using Taguchi Technique Used in Electrical Grids 9.1 Introduction 9.2 Materials and Methods 9.2.1 Workpiece Design 9.2.2 Experimental Procedure 9.3 Results and Discussion 9.3.1 S/N Ratio 9.3.2 Analysis of Variance 9.3.3 Confirmation Test 9.4 Conclusions References Chapter 10 Experimental Analysis of Surface Integrity of Machined Stainless Steel (SS-304) by Taguchi Method Coupled with GRA Used in Electrical Grids 10.1 Introduction 10.2 Experimental Particulars 10.2.1 Materials 10.2.2 Experimental Setup 10.2.3 Experimental Design 10.2.4 Recording of Response Characteristics 10.3 Method 10.3.1 Taguchi Technique (Signal-to-Noise (S/N) Ratio) 10.3.2 Steps of Grey Relational Analysis (GRA) 10.3.2.1 Normalization 10.3.2.2 Analysis of GRG (Grey Relational Grade) and GRC (Grey Relational Coefficient) 10.4 Outcomes and Explanation 10.4.1 Best Suitable Combination of Parameters 10.4.1.1 Analysis of Variance (ANOVA) 10.4.1.2 Validation Test 10.5 Optical Micrographs 10.6 Conclusions References Chapter 11 Energy Storage Devices Based on 2D Phosphorene as an Electrode Material 11.1 Introduction 11.2 Fundamental Properties of Phosphorene 11.2.1 Band Structures 11.2.2 Carrier Transport 11.2.3 Optical Properties of Phosphorene 11.2.4 Thermal Properties 11.2.5 Mechanical Properties 11.3 Tunable Electronic Properties 11.3.1 Strain/Electric Field 11.3.2 Defect 11.3.3 Surface Functionalization 11.3.4 Heterostructures 11.4 Synthesis of Phosphorene 11.4.1 High-Pressure Route 11.4.2 Recrystallization from Bismuth Flux 11.4.3 Chemical Vapor Transport 11.4.4 Mechanical Milling 11.5 Energy Storage Devices—Phosphorene 11.5.1 Li-ion Batteries 11.5.2 Na-ion Batteries 11.5.3 K-ion Batteries 11.5.4 Li–S Batteries 11.5.5 Mg-ion Batteries 11.6 Conclusions References Chapter 12 Application and Performance Analysis of Various Nature-Inspired Algorithm in AGC Synthesis 12.1 Introduction 12.2 Modern AGC 12.3 Optimization Methods 12.3.1 Grey Wolf Optimization 12.3.2 Particle Swarm Optimization 12.3.3 Salp Swarm Optimization Algorithm 12.3.4 Whale Optimization Algorithm 12.4 Model Description 12.4.1 Controller Implementation 12.5 Results 12.6 Conclusions References Chapter 13 Unified Smith Predictor for MIMO Systems with Multiple Time Delays 13.1 Introduction 13.2 Literature Survey 13.3 Unified Smith Predictor for Multiple Time Delays 13.4 Parameterization of Two-DOF Controllers 13.4.1 Design of Feedback Controller 13.4.2 Design of Feedforward Controller 13.5 Simulation Example 13.6 Robust Stability Analysis 13.7 Conclusions References Chapter 14 Renewable Energy Sources and Small Hydro Power Scenario in Mountainous Regions of Himalayas 14.1 Introduction 14.2 Renewable Energy Sources 14.3 Hybrid Power Generation System 14.4 Solar Energy 14.5 Wind Energy 14.6 Geothermal Energy 14.7 Biomass Energy 14.8 Small Hydro Power Plants 14.8.1 Classification of Small Hydro Power Plants 14.8.2 Elements of Small Hydro Power Plants 14.8.3 Power Generated in Small Hydro Power Plants 14.8.4 Control Requirements in Small Hydro Power Plants 14.8.5 Pico-/Micro-Hydro Power Plants and Their Control 14.8.6 Water Discharge and Electrical Loading Pattern in Remote Mountainous Regions of Himalayas 14.9 Conclusions References Chapter 15 A Comprehensive Review on Energy Storage Systems 15.1 Introduction 15.2 Classification of ESS 15.2.1 Mechanical Energy Storage (MES) 15.2.1.1 Pumped Hydroelectric Energy Storage (PHES) 15.2.1.2 Compressed-Air Energy Storage (CAES) 15.2.1.3 Flywheel Energy Storage (FES) 15.2.2 Electrical Energy Storage (EES) 15.2.2.1 Superconducting Magnetic Energy Storage 15.2.2.2 Supercapacitor Energy Storage 15.2.3 Thermal Energy Storage (TES) 15.2.3.1 Sensible Heat Energy Storage 15.2.3.2 Latent Heat Energy Storage (LHES) 15.2.3.3 Thermochemical Energy Storage 15.2.4 Electrochemical and Battery Energy Storage 15.2.4.1 Flow Batteries 15.2.4.2 Secondary Batteries 15.2.5 Chemical Energy Storage 15.3 Applications of Energy Storage Systems 15.3.1 Bulk Energy Applications 15.3.1.1 Energy Arbitrage 15.3.1.2 Conservation for Peak Demand 15.3.2 Ancillary Service Applications 15.3.2.1 Load Tracking 15.3.2.2 Spinning Reserve 15.3.2.3 Voltage Support 15.3.2.4 Blackout 15.3.2.5 Frequency Regulation 15.3.3 Customer Energy Management Applications 15.3.3.1 Power Quality 15.3.3.2 Power Reliability 15.3.4 Renewable Energy Integration Applications 15.3.5 Applications Based on Usage 15.3.6 Self-Generation and Utilities 15.3.7 Transportation 15.3.8 General Applications 15.4 Technical Comparison of Various Types of Energy Storage Systems 15.5 Challenges and Issues in Deploying Energy Storage Systems 15.6 Future Research Directions 15.7 Conclusions References Chapter 16 Cyber and Theft Attacks on Smart Electric Metering Systems: An Overview of Defenses 16.1 Introduction 16.2 Vulnerabilities of Attacks in Smart Electrical Infrastructures 16.3 Summary of Safety Measures Needed in Smart Grids to Detect and prevent Cyberattacks 16.4 Architecture of Smart Grid 16.5 Proposed Solutions by Some of the Reseachers on Attacks 16.6 Attacker Types in Smart Grids 16.7 Attack Type 16.8 Classification of Cyberattacks 16.9 The Set of Detection and Prevention Systems in Smart Grid 16.9.1 Intrusion Detection System in SG 16.9.2 Use of Frequency Monitoring in Communication 16.9.3 Cognitive Radio-Based WRANs for Communication in SG 16.9.4 The Wireless Sensor Networks (WSNs) 16.9.5 False Data Injection Attacks 16.9.6 Abnormal Traffic-Indexed State Estimation (ATSE) 16.9.7 Use of IoTs 16.10 IPv6 and IBM Model 16.11 Use of 5G 16.12 Fault Detection is also Possible with Phasor Measurement Units 16.13 Using Machine Learning and Deep Learning for Cybersecurity 16.14 Game-Theoretic Approach Used to Model Attacks and Defenses 16.15 Use of a Practical Group Blind Signature Scheme 16.16 Blockchain Method 16.17 A Situation-Aware Scheme for Efficient Device Authentication 16.18 Key Management Protocol for Secure Communication 16.19 Detection of Attack in Smart Meter Using Protection Method 16.20 Detection of Attack Using Machine Learning Methods in Smart Meters Data 16.21 Deficit due to Energy Theft 16.22 Ensemble-Based Methods 16.23 Ensemble ML Algorithm 16.24 Conclusions References Index