Big Data Analytics Framework for Smart Grids

The text comprehensively discusses smart grid operations and the use of big data analytics in overcoming the existing challenges. It covers smart power generation, transmission, and distribution, explains energy management systems, artificial intelligence, and machine learning–based computing. •Presents a detailed state-of-the-art analysis of big data analytics and its uses in power grids. • Describes how the big data analytics framework has been used to display energy in two scenarios including a single house and a smart grid with thousands of smart meters. •Explores the role of the internet of things, artificial intelligence, and machine learning in smart grids. • Discusses edge analytics for integration of generation technologies, and decision-making approaches in detail. • Examines research limitations and presents recommendations for further research to incorporate big data analytics into power system design and operational frameworks. The text presents a comprehensive study and assessment of the state-of-the-art research and development related to the unique needs of electrical utility grids, including operational technology, storage, processing, and communication systems. It further discusses important topics such as complex adaptive power system, self-healing power system, smart transmission, and distribution networks, and smart metering infrastructure. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics and communications engineering, computer engineering, and information technology. The text comprehensively discusses smart grid operations and the use of big data analytics in overcoming the existing challenges. It covers smart power generation, transmission, and distribution, explains energy management systems, artificial intelligence, and machine learning–based computing.Presents a detailed state-of-the-art analysis of big data analytics and its uses in power grids.Describes how the big data analytics framework has been used to display energy in two scenarios including a single house and a smart grid with thousands of smart meters.Explores the role of the internet of things, artificial intelligence, and machine learning in smart grids.Discusses edge analytics for integration of generation technologies, and decision-making approaches in detail.Examines research limitations and presents recommendations for further research to incorporate big data analytics into power system design and operational frameworks., The text presents a comprehensive study and assessment of the state-of-the-art research and development related to the unique needs of electrical utility grids, including operational technology, storage, processing, and communication systems. It further discusses important topics such as complex adaptive power system, self-healing power system, smart transmission, and distribution networks, and smart metering infrastructure. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the areas such as electrical engineering, electronics and communications engineering, computer engineering, and information technology. Cover 1 Half Title 2 Series 3 Title 4 Copyright 5 Contents 6 Preface 14 About the Editors 16 List of Contributors 18 1 Necessities of Big Data in Smart Grid 20 1.1 Introduction 20 1.1.1 Indian Old Grid 21 1.1.2 New Progression in Grids 21 1.1.3 Smart Grid Present and Future Drift 22 1.1.4 Brief Description for the Necessity of Big Data in Smart Grid 22 1.2 Notion for Today’s Electricity Grid 23 1.2.1 Scenario of Today’s Grid 23 1.2.2 Determination of Operating Challenges 24 1.2.3 Example of Challenges like Blackouts and Cyber Theft 25 1.3 Futuristic Smart Grids and Other Energy Systems 26 1.3.1 Need of Smart Grid 28 1.3.2 Practices to Overcome the Active Challenges 28 1.3.3 Comparison Table of Existing Grid and Smart Grid 29 1.3.4 Future Direction in Operation of Smart Grid 30 1.3.5 Inclusion of Renewable Energy and Other Energy Systems 30 1.4 Energy in Numbers 31 1.4.1 Need of Energy Analysis 31 1.4.2 Energy and Data 32 1.4.3 Huge Data Generation in Grid 33 1.4.4 Data Analysis Requirement in Grid 33 1.5 Self-Healing and Adaptiveness 33 1.5.1 Smart Grid Major Advantages 34 1.5.2 Self-Healing Purpose 34 1.5.3 Smart Grid Adaptiveness in Operation 35 1.6 Major Necessities of Big Data in Smart Grid 37 1.6.1 Cyber Security and Privacy 38 1.6.2 Edge Analytics Involvement 38 1.7 Recommendations and Future Directions 41 1.8 Conclusion 42 2 Challenges and Opportunities in the Development of a Smart Grid System in India 45 2.1 Introduction 45 2.2 Components of a Smart Grid 47 2.3 Smart Grid (SG) Technologies 47 2.4 Traditional Electricity Grid System vs. Smart Grid System 49 2.4.1 Traditional Electricity Grid System 50 2.4.2 Smart Grid System 50 2.5 Development of Smart Grid in India 53 2.5.1 Smart Grid Pilot Projects in India 53 2.6 Challenges in Development of Smart Grid System 55 2.6.1 Sociocultural 56 2.6.2 Collaboration of Stakeholders 56 2.6.3 Role of Government 56 2.7 Conclusion 57 3 Why Big Data for Smart Cities? 60 3.1 Introduction: Big Data 60 3.2 IOT – Internet of Things 63 3.3 Smart Environment 66 3.4 Smart Cities 67 3.5 New Forms of Urban Communication: The Rise of the Smart City 69 3.6 How Big Data Impacts Smart Cities 70 3.7 Sustainable Development in Smart Cities and Big Data 74 3.8 Energy Management in Smart Cities 75 3.9 Energy Storage in Smart Cities 77 3.10 Renewable Energy Source in Smart Cities 78 3.11 Electricity Consumption in Smart Cities 79 3.12 Smart Grids 79 3.13 Conclusion 80 4 Big Data for Smart Grid: A Way Forward 83 4.1 Introduction: Concept of Smart Grids 83 4.2 Smart Grid Architecture Model (SGAM) 84 4.3 Need of Data Analytics in Smart Grid 86 4.4 Current Use of Data Analytics in the Smart Grid 87 4.5 Big Data Characteristics in Smart Grid 88 4.6 Evolution Cloud Computing 89 4.7 Edge Computing in Smart Cities Using Big Data 90 4.8 Cloud Computing for Smart Grid and Big Data 91 4.9 Data Sources in Smart Grids 93 4.10 Intelligent Processing Techniques for Big Data 95 4.11 Conclusion 96 5 Advanced Machine Learning Methods for Big Data Analytics Used in Smart Grid 98 5.1 Data Generation in Smart Grid 98 5.2 Big Data Attributes 101 5.3 Components of Big Data 105 5.3.1 Ingestion 105 5.3.2 Storage 105 5.3.3 Analysis 106 5.3.4 Consumption 106 5.4 Selection, Visualization, Correlation, Forecasting, Classification, and Clustering of Data 106 5.5 Advancement in Data Mining Methods 107 5.6 AI and Machine Learning for Big Data Analytics 109 5.7 Advanced Machine Learning for Data Analytics 111 5.8 Data Science, Cloud, and Edge Computing 111 5.9 Application in Smart Grid 112 5.10 Conclusion 113 6 Perspective of Cybersecurity and Ethical Hacking with Vulnerability Assessment and Exploitation Tools 117 6.1 Introduction 117 6.2 Why Do We Need Cybersecurity? 117 6.2.1 White Hat Hackers 118 6.2.2 Black Hat Hackers 118 6.2.3 Grey Hat Hackers 119 6.3 Cybersecurity and Ethical Hacking 119 6.4 Comparison 120 6.5 History of Cybersecurity 121 6.6 Problems Arising during COVID-19 121 6.7 Cybersecurity Vulnerability Assessment 122 6.8 Vulnerability Assessment Tools 123 6.8.1 Burp Suite 123 6.9 Nikto 124 6.9.1 OWASP Zed Attack Proxy (ZAP) 125 6.9.2 Metasploit 126 6.9.3 SQL Map 128 6.9.4 Microgrid cybersecurity 129 6.10 Conclusion 129 7 Communication and Measurement Technologies for Smart Grid 131 7.1 Introduction: Communication and Measurement Technologies 131 7.2 Smart Grid Communication Technology 131 7.2.1 Wired Communication Technologies 132 7.2.2 Wireless Communication Technologies 132 7.3 Smart Grid Communication Network Infrastructure 133 7.3.1 Classification of Network Architecture Layers Based on Communication Characteristics 134 7.3.2 Classification of Network Architecture Layers Based on Geographical Coverage 134 7.4 Smart Grid Measurement Technologies 135 7.4.1 Synchrophasor Technology 135 7.4.2 Wide Area Measurement System (WAMS) 138 7.4.3 Smart Metering System 139 7.4.4 Wireless Sensor Network 140 7.4.5 Internet of Things 141 7.5 Optimal Integration of Renewable Generations, Storages, and EVs in Smart Grid 142 7.5.1 Optimal Planning Framework 142 7.6 Real-Time Price (RTP) Algorithms 145 7.6.1 Scopes of Different RTP Algorithms 146 7.6.2 Difficulties to Implement RTP Algorithms 152 7.7 Dynamic Energy Management System (DEMS) 153 7.7.1 Energy Management Framework 153 7.7.2 Control Strategy 155 7.8 Conclusion 158 8 Big Data for Smart Grid: A Case Study 161 8.1 Introduction 161 8.2 Conventional/Traditional Electric Power Grid 161 8.2.1 Generation 161 8.2.2 Transmission 162 8.2.3 Distribution 162 8.2.4 Consumption 162 8.2.5 Problems Associated with Traditional Power Grids 162 8.3 Smart Grid 164 8.3.1 Conventional Power Grids 164 8.3.2 Production of Power 165 8.3.3 The Infrastructure of the Grid 165 8.3.4 Demand Response 166 8.3.5 The Framework of Smart Grid 166 8.3.6 The Subsystem of Smart Grid 166 8.4 Types of Cyberattacks in Smart Grid 167 8.4.1 False Data Injection Attack 169 8.4.2 DoS Attack 169 8.4.3 Jamming Attack 169 8.4.4 Man-in-the-Middle Attack 169 8.4.5 Replay Attack 169 8.4.6 Spoofing Attack 170 8.4.7 Detection and Mitigation of Cyberattacks 172 8.4.8 Related Works 175 8.5 Blockchain Overview 180 8.5.1 Blockchain Applications in Smart Grid 181 8.5.2 Peer-to-Peer Trading Infrastructure 181 8.5.3 Blockchain Applications in Microgrid Operations 183 8.5.4 Energy Trading in Electric Vehicles 183 8.5.5 Security and Privacy-Protecting Strategies 183 8.6 Cryptographic Overview 184 8.6.1 Cryptographic ZKP 187 8.6.2 Zero-Knowledge Cryptography Characteristics 187 8.6.3 Advantages of Zero-Knowledge Cryptography 187 8.6.4 Applications of Zero-Knowledge Proof Cryptography 188 8.6.5 Blockchain Technologies with Cryptography 188 8.6.6 Role of Cryptography in Blockchain 188 8.6.7 Cryptography Hash Function in Blockchain 189 8.7 Advantages and Disadvantages of Blockchain and Cryptography in Smart Grid 189 8.7.1 The Disadvantages Include the Following 189 8.7.2 Risks and Downsides Associated with Blockchain and Cryptography Technology 190 8.8 Conclusion 190 9 Big Data and Smart Grid: Implementation-Based Case Study 200 9.1 Introduction 200 9.2 Utility of Big Data 202 9.2.1 Role in Social Media Analysis 202 9.2.2 Role in Biological Network Analysis 203 9.2.3 Role in Health Care 204 9.3 Big Data Platforms 204 9.3.1 Microsoft Azure 204 9.3.2 IBM Cloud Database for MongoDB 205 9.3.3 Hadoop HDFS Architecture 206 9.4 Case Study of Smart Grid 206 9.4.1 Identifying Linkages and Forecasting the Spread of Faults in Cyber-Physical Systems 206 9.4.2 A Sophisticated Architecture for Detecting Power Theft in a Smart Grid: EnsembleNTLDetect 209 9.4.3 BI&DA for a Solar Power System 210 9.5 Conclusion 211 10 Big Data Analytics: A Holistic Assessment of Paradigm Shift Challenges and Opportunities for Future Smart Grid 214 10.1 Introduction: Smart Grid 214 10.2 Energy in Numbers 219 10.3 Core Components of Big Data 220 10.4 Smart Grid: Big Data Applications 226 10.4.1 Wide area situational awareness 227 10.4.2 State estimation 228 10.4.3 Event classification and detections 229 10.5 Classification of Techniques Used for Big Data Analytics in Smart Grid 230 10.5.1 Categorization of smart grid data analytics 231 10.5.2 Big data analytics platforms 234 10.6 Edge Computing and Smart Grid: A Way Forward 239 10.6.1 Tier-Based Edge Computing Architecture for Smart Grids 240 10.7 Challenges and Opportunities 241 10.8 Conclusion 244 Index 250 Electric,Vehicles;,Cloud,Computing;,Smart,Transmission,and,Distribution,Networks;,Smart,Metering,Infrastructure;,Intelligent,Processing,Techniques;,Dynamic,Energy,Management,System Electric Vehicles,Cloud Computing,Smart Transmission and Distribution Networks,Smart Metering Infrastructure,Intelligent Processing Techniques,Dynamic Energy Management System