Big Data Analytics Framework for Smart Grids

Cover
Half Title
Series
Title
Copyright
Contents
Preface
About the Editors
List of Contributors
1 Necessities of Big Data in Smart Grid
	1.1 Introduction
		1.1.1 Indian Old Grid
		1.1.2 New Progression in Grids
		1.1.3 Smart Grid Present and Future Drift
		1.1.4 Brief Description for the Necessity of Big Data in Smart Grid
	1.2 Notion for Today’s Electricity Grid
		1.2.1 Scenario of Today’s Grid
		1.2.2 Determination of Operating Challenges
		1.2.3 Example of Challenges like Blackouts and Cyber Theft
	1.3 Futuristic Smart Grids and Other Energy Systems
		1.3.1 Need of Smart Grid
		1.3.2 Practices to Overcome the Active Challenges
		1.3.3 Comparison Table of Existing Grid and Smart Grid
		1.3.4 Future Direction in Operation of Smart Grid
		1.3.5 Inclusion of Renewable Energy and Other Energy Systems
	1.4 Energy in Numbers
		1.4.1 Need of Energy Analysis
		1.4.2 Energy and Data
		1.4.3 Huge Data Generation in Grid
		1.4.4 Data Analysis Requirement in Grid
	1.5 Self-Healing and Adaptiveness
		1.5.1 Smart Grid Major Advantages
		1.5.2 Self-Healing Purpose
		1.5.3 Smart Grid Adaptiveness in Operation
	1.6 Major Necessities of Big Data in Smart Grid
		1.6.1 Cyber Security and Privacy
		1.6.2 Edge Analytics Involvement
	1.7 Recommendations and Future Directions
	1.8 Conclusion
2 Challenges and Opportunities in the Development of a Smart Grid System in India
	2.1 Introduction
	2.2 Components of a Smart Grid
	2.3 Smart Grid (SG) Technologies
	2.4 Traditional Electricity Grid System vs. Smart Grid System
		2.4.1 Traditional Electricity Grid System
		2.4.2 Smart Grid System
	2.5 Development of Smart Grid in India
		2.5.1 Smart Grid Pilot Projects in India
	2.6 Challenges in Development of Smart Grid System
		2.6.1 Sociocultural
		2.6.2 Collaboration of Stakeholders
		2.6.3 Role of Government
	2.7 Conclusion
3 Why Big Data for Smart Cities?
	3.1 Introduction: Big Data
	3.2 IOT – Internet of Things
	3.3 Smart Environment
	3.4 Smart Cities
	3.5 New Forms of Urban Communication: The Rise of the Smart City
	3.6 How Big Data Impacts Smart Cities
	3.7 Sustainable Development in Smart Cities and Big Data
	3.8 Energy Management in Smart Cities
	3.9 Energy Storage in Smart Cities
	3.10 Renewable Energy Source in Smart Cities
	3.11 Electricity Consumption in Smart Cities
	3.12 Smart Grids
	3.13 Conclusion
4 Big Data for Smart Grid: A Way Forward
	4.1 Introduction: Concept of Smart Grids
	4.2 Smart Grid Architecture Model (SGAM)
	4.3 Need of Data Analytics in Smart Grid
	4.4 Current Use of Data Analytics in the Smart Grid
	4.5 Big Data Characteristics in Smart Grid
	4.6 Evolution Cloud Computing
	4.7 Edge Computing in Smart Cities Using Big Data
	4.8 Cloud Computing for Smart Grid and Big Data
	4.9 Data Sources in Smart Grids
	4.10 Intelligent Processing Techniques for Big Data
	4.11 Conclusion
5 Advanced Machine Learning Methods for Big Data Analytics Used in Smart Grid
	5.1 Data Generation in Smart Grid
	5.2 Big Data Attributes
	5.3 Components of Big Data
		5.3.1 Ingestion
		5.3.2 Storage
		5.3.3 Analysis
		5.3.4 Consumption
	5.4 Selection, Visualization, Correlation, Forecasting, Classification, and Clustering of Data
	5.5 Advancement in Data Mining Methods
	5.6 AI and Machine Learning for Big Data Analytics
	5.7 Advanced Machine Learning for Data Analytics
	5.8 Data Science, Cloud, and Edge Computing
	5.9 Application in Smart Grid
	5.10 Conclusion
6 Perspective of Cybersecurity and Ethical Hacking with Vulnerability Assessment and Exploitation Tools
	6.1 Introduction
	6.2 Why Do We Need Cybersecurity?
		6.2.1 White Hat Hackers
		6.2.2 Black Hat Hackers
		6.2.3 Grey Hat Hackers
	6.3 Cybersecurity and Ethical Hacking
	6.4 Comparison
	6.5 History of Cybersecurity
	6.6 Problems Arising during COVID-19
	6.7 Cybersecurity Vulnerability Assessment
	6.8 Vulnerability Assessment Tools
		6.8.1 Burp Suite
	6.9 Nikto
		6.9.1 OWASP Zed Attack Proxy (ZAP)
		6.9.2 Metasploit
		6.9.3 SQL Map
		6.9.4 Microgrid cybersecurity
	6.10 Conclusion
7 Communication and Measurement Technologies for Smart Grid
	7.1 Introduction: Communication and Measurement Technologies
	7.2 Smart Grid Communication Technology
		7.2.1 Wired Communication Technologies
		7.2.2 Wireless Communication Technologies
	7.3 Smart Grid Communication Network Infrastructure
		7.3.1 Classification of Network Architecture Layers Based on Communication Characteristics
		7.3.2 Classification of Network Architecture Layers Based on Geographical Coverage
	7.4 Smart Grid Measurement Technologies
		7.4.1 Synchrophasor Technology
		7.4.2 Wide Area Measurement System (WAMS)
		7.4.3 Smart Metering System
		7.4.4 Wireless Sensor Network
		7.4.5 Internet of Things
	7.5 Optimal Integration of Renewable Generations, Storages, and EVs in Smart Grid
		7.5.1 Optimal Planning Framework
	7.6 Real-Time Price (RTP) Algorithms
		7.6.1 Scopes of Different RTP Algorithms
		7.6.2 Difficulties to Implement RTP Algorithms
	7.7 Dynamic Energy Management System (DEMS)
		7.7.1 Energy Management Framework
		7.7.2 Control Strategy
	7.8 Conclusion
8 Big Data for Smart Grid: A Case Study
	8.1 Introduction
	8.2 Conventional/Traditional Electric Power Grid
		8.2.1 Generation
		8.2.2 Transmission
		8.2.3 Distribution
		8.2.4 Consumption
		8.2.5 Problems Associated with Traditional Power Grids
	8.3 Smart Grid
		8.3.1 Conventional Power Grids
		8.3.2 Production of Power
		8.3.3 The Infrastructure of the Grid
		8.3.4 Demand Response
		8.3.5 The Framework of Smart Grid
		8.3.6 The Subsystem of Smart Grid
	8.4 Types of Cyberattacks in Smart Grid
		8.4.1 False Data Injection Attack
		8.4.2 DoS Attack
		8.4.3 Jamming Attack
		8.4.4 Man-in-the-Middle Attack
		8.4.5 Replay Attack
		8.4.6 Spoofing Attack
		8.4.7 Detection and Mitigation of Cyberattacks
		8.4.8 Related Works
	8.5 Blockchain Overview
		8.5.1 Blockchain Applications in Smart Grid
		8.5.2 Peer-to-Peer Trading Infrastructure
		8.5.3 Blockchain Applications in Microgrid Operations
		8.5.4 Energy Trading in Electric Vehicles
		8.5.5 Security and Privacy-Protecting Strategies
	8.6 Cryptographic Overview
		8.6.1 Cryptographic ZKP
		8.6.2 Zero-Knowledge Cryptography Characteristics
		8.6.3 Advantages of Zero-Knowledge Cryptography
		8.6.4 Applications of Zero-Knowledge Proof Cryptography
		8.6.5 Blockchain Technologies with Cryptography
		8.6.6 Role of Cryptography in Blockchain
		8.6.7 Cryptography Hash Function in Blockchain
	8.7 Advantages and Disadvantages of Blockchain and Cryptography in Smart Grid
		8.7.1 The Disadvantages Include the Following
		8.7.2 Risks and Downsides Associated with Blockchain and Cryptography Technology
	8.8 Conclusion
9 Big Data and Smart Grid: Implementation-Based Case Study
	9.1 Introduction
	9.2 Utility of Big Data
		9.2.1 Role in Social Media Analysis
		9.2.2 Role in Biological Network Analysis
		9.2.3 Role in Health Care
	9.3 Big Data Platforms
		9.3.1 Microsoft Azure
		9.3.2 IBM Cloud Database for MongoDB
		9.3.3 Hadoop HDFS Architecture
	9.4 Case Study of Smart Grid
		9.4.1 Identifying Linkages and Forecasting the Spread of Faults in Cyber-Physical Systems
		9.4.2 A Sophisticated Architecture for Detecting Power Theft in a Smart Grid: EnsembleNTLDetect
		9.4.3 BI&DA for a Solar Power System
	9.5 Conclusion
10 Big Data Analytics: A Holistic Assessment of Paradigm Shift Challenges and Opportunities for Future Smart Grid
	10.1 Introduction: Smart Grid
	10.2 Energy in Numbers
	10.3 Core Components of Big Data
	10.4 Smart Grid: Big Data Applications
		10.4.1 Wide area situational awareness
		10.4.2 State estimation
		10.4.3 Event classification and detections
	10.5 Classification of Techniques Used for Big Data Analytics in Smart Grid
		10.5.1 Categorization of smart grid data analytics
		10.5.2 Big data analytics platforms
	10.6 Edge Computing and Smart Grid: A Way Forward
		10.6.1 Tier-Based Edge Computing Architecture for Smart Grids
	10.7 Challenges and Opportunities
	10.8 Conclusion
Index