Оглавление
Группа авторов. Active Electrical Distribution Network
Active Electrical Distribution Network. A Smart Approach
Table of Contents
List of Illustrations
List of Tables
Guide
Pages
Foreword
Preface
Acknowledgments
List of Contributors
List of Abbreviations
1 Electricity Distribution Structures and Business Models Considering Smart Grid Perspectives
1.1 Introduction
1.2 Importance of the Business Model in the Smart Grid
1.3 Electricity Distribution Structures and Business Models
1.3.1 Government-Owned DISCOM
1.3.2 Privately Owned DISCOMs
1.3.3 Public–Private Partnership (PPP) Model
1.3.4 Distribution Franchisee (DF) Model
1.4 Need for a Novel Business Model for Power Distribution under Smart Grid Environment
1.4.1 Description of the Novel Business Model
1.4.2 Physical Infrastructure Unit (PIU)
1.4.2.1 DISCOM
1.4.2.2 MCOM
1.4.2.3 ITCOM
1.4.2.4 CCOM
1.4.2.5 ICOM
1.4.2.6 IR
1.4.2.7 PFA
1.4.3 Distribution Management and Maintenance Unit (DMMU)
1.4.4 Control, Operation, and Revenue Management Unit (CORMU)
1.4.4.1 RME
1.4.4.2 DSO
1.4.4.3 SNE
1.4.4.4 TI
1.5 Conclusion
References
2 Existing Problems Related to Electrical Distribution Network, Part 1 Distribution Feeder Segregation
2.1 Introduction
2.2 Review of Status of Research and Development in the Subject of Feeder Segregation
2.3 Indian Experiences with Agriculture Feeder Segregation
2.4 Importance of Feeder Segregation in the Context of Current Status
2.5 Threats with Feeder Segregation and Possible Solutions
2.6 Feeder Reconfiguration
2.6.1 Objective Function
2.6.2 Constraints
2.6.3 Algorithm
2.7 Conclusion
References
3 Existing Problems Related to Electrical Distribution Network, Part 2 Technical, Economical, and Environmental
3.1 Introduction
3.2 What Is the Distribution System?
3.3 Existing Problems Related to the ElectricalDistribution Network
3.3.1 Technical Problems
3.3.1.1 Distribution Losses
3.3.1.2 Reliability of the System
3.3.1.3 Contingency Analysis
3.3.1.4 Reverse Power Flow Due to Inappropriate Allocation of Distributed Generators
3.3.1.5 Reactive Power Management
3.3.1.6 Voltage Profile Management
3.3.1.7 Network Restructuring
3.3.1.8 Impacts of Distributed Generator Insertion
3.3.1.9 Grid Security
3.3.1.10 Stability of the System
3.3.2 Economic Problems. 3.3.2.1 Inadequacy of the Traditional Distribution System Structure to Cope with the Day-by-Day Enhancement of the Load Requirement
3.3.2.2 Economic Operation of the System
3.3.3 Environmental Problems. 3.3.3.1 Deterioration of the Grid with the Course of Time
3.3.3.2 Impact of Worsened Climatic Conditions
3.4 Conclusion
References
4 Power Quality Mitigation in a Distribution Network Using a Battery Energy Storage System
4.1 Introduction
4.2 System Configuration of D-STATCOM with BESS
4.3 BESS Operation and Its Control
4.4 Discussion of the Simulation Results
4.5 Results Analysis under a Balanced System
4.5.1 Response of DSTATCOM-BESS under a Balanced Linear Load
4.5.2 Response of DSTATCOM-BESS under a Nonlinear Load
4.5.3 Response of DSTATCOM-BESS under an Induction Motor (IM) Load
4.6 Results Analysis under an Unbalanced System. 4.6.1 Response of DSTATCOM-BESS under an Unbalanced Linear Load
4.6.2 Response of DSTATCOM-BESS under an Unbalanced Nonlinear Load
4.6.3 Response of DSTATCOM-BESS under an Unbalanced IM Load
4.7 Conclusion
References
5 Grid Power Quality Improvement Using a Bidirectional Off-Board EV Battery Charger in Smart City Scenario
5.1 Introduction
5.2 EV Battery Charging Model
5.3 Control Strategy
5.4 Simulation Results and Discussion
5.4.1 Linear Loading
5.4.2 Nonlinear Loading
5.5 Conclusion
References
6 Smart Distribution of Electrical Energy
6.1 Introduction
6.2 Generation, Transmission, and Distribution
6.3 Smart Energy
6.4 Energy Independence and Security Act of 2007
6.5 Challenges in the Existing Grid
6.5.1 Technology Development
6.5.2 Quality Power
6.5.3 Reducing the Losses of Transmission and Distribution
6.5.4 Renewable Energy Integration
6.5.5 Customer Support
6.6 Smart Grid (SG)
6.6.1 Need for Smart Grid
6.6.2 Traditional Grid vs Smart Grid Comparison
6.6.3 Smart Grid Implementation
6.6.4 Smart Grid Architecture
6.6.4.1 Power Grid Functions
6.6.4.2 Network Functions
6.6.4.3 Smart Metering Functions
6.6.4.4 Energy Control Functions
6.6.4.5 End-User Functions
6.6.4.6 Application Functions
6.6.4.7 Management Functions
6.6.4.8 Security Functions
6.6.5 Summary of the Smart Grid Introductory Section
6.7 Role of Communication and Network Technology in a Smart Grid. 6.7.1 Introduction
6.7.2 Comparability of Communication Technologies between the Traditional Grid and the Smart Grid
6.7.3 Communication Standards of Smart Grid
6.7.4 Characteristics of the Smart Grid
6.7.4.1 Digitalization
6.7.4.2 Flexibility
6.7.4.3 Intelligence
6.7.4.4 Resiliency
6.7.4.5 Sustainability
6.7.4.6 New Materials and Alternative Clean Energy Resources
6.7.4.7 Advanced Power Electronics and Devices
6.7.4.8 Sensing and Measurement
6.7.4.9 Advanced Computing and Control Methodologies
6.7.4.10 Intelligent Technologies
6.7.5 Communication Network Architecture of the Smart Grid
6.7.5.1 Home Area Network (HAN)
6.7.5.2 Neighborhood Area Network (NAN)
6.7.5.3 Wide Area Network (WAN)
6.7.6 Various Communication Technologies for the Smart Grid
6.7.6.1 Wired Communication
6.7.6.2 Wireless Communication
6.7.7 Monitoring, PMU, Smart Meters, and Measurement Technology
6.7.7.1 Wide Area Monitoring Systems (WAMSs)
6.7.7.2 Phasor Measurement Units (PMUs)
6.7.7.3 Smart Meters
6.7.8 Future Scope
6.7.9 Summary of Communication and Network Technology Section
6.8 Load Flow Studies on the Smart Grid. 6.8.1 Introduction
6.8.2 Need for Low Flow Studies
6.8.3 Smart Grid Load Flow Enhancement Capabilities
6.8.4 Load Flow Problems in the Smart Grid
6.8.5 Methods for Performing Load Flow Studies
6.8.5.1 Gauss-Siedel Method
6.8.5.2 Newton-Raphson Method (NR)
6.8.5.3 Fast Decoupled Method
6.8.6 Analytical Comparison Between Different Methods of Load Flow Studies
6.8.7 Enhanced Load Flow Capabilities in a Smart Grid
6.8.8 Summary of Load Flow Studies Section
6.9 Stability Analysis for a Smart Grid. 6.9.1 Introduction
6.9.2 Stability Assessment
6.9.3 Reliability Assessment
6.9.3.1 System Average Interruption Duration Index (SAIDI)
6.9.3.2 System Average Interruption Frequency Index (SAIFI)
6.9.3.3 Customer Average Interruption Duration Index (CAIDI)
6.9.3.4 Momentary Average Interruption Frequency Index (MAIFI)
6.9.4 Resilience Assessment
6.9.5 Smart Grid Protection Issues
6.9.6 Summary of the Stability Analysis Section
6.10 Renewable Energy in the Smart Grid. 6.10.1 Introduction
6.10.2 Smart Grid Renewable Energy System
6.10.3 Advantages and Challenges of a Smart Grid Renewable System
6.10.4 Renewable Energy and Distributed Generation in the Smart Grid
6.10.5 Different Types of Renewable Energy Sources. 6.10.5.1 Solar Energy
6.10.5.2 Wind Energy
6.10.5.3 Biomass-Bio Energy
6.10.5.4 Hydropower
6.10.5.5 Fuel Cells
6.10.5.6 Geothermal Heat Pumps
6.10.6 Electric Energy Storage Applications. 6.10.6.1 Pumped Hydro Storage
6.10.6.2 Compressed to Air Energy Storage
6.10.6.3 Batteries
6.10.7 Renewable Energy Pricing
6.10.7.1 Pricing
6.10.7.2 Load Pattern
6.10.7.3 Revenue Requirement of the Service Providers
6.10.7.4 Social Criteria
6.10.8 Summary of the Renewable Energy Section
6.11 Interoperability in the Smart Grid. 6.11.1 Introduction
6.11.2 Smart Grid Interoperability Platform (SGIP)
6.11.3 Functional and Non-functional Requirements
6.11.4 Smart Grid Interoperability Panel (SGIP) Architecture
6.11.5 Validation Requirements
6.11.5.1 Validation of Functional Requirements
6.11.5.2 Validation of Non-Functional Requirements
6.11.5.3 Validation of Data-Driven Interoperability
6.11.5.4 Validation of Communication-Driven Interoperability
6.11.6 Summary of the Interoperability Section
6.12 Smart Grid Standards. 6.12.1 Introduction
6.12.2 Standards for Smart Metering
6.12.2.1 Iec 62056
6.12.2.2 ANSI C12.22
6.12.3 Smart Grid Communication Standards. 6.12.3.1 Ieee P2030
6.12.3.2 Ieee P1901
6.12.3.3 Iec 62351
6.12.3.4 Plc G3
6.12.4 Summary of Smart Grid Standards
6.13 Cyber Security in Smart Grids. 6.13.1 Introduction
6.13.2 Cyber Security Standards
6.13.2.1 Ieee 1686
6.13.3 Cyber Security Risks Through Mitigation
6.13.4 Summary of Cyber Security Section
References
7 Active Distribution Management System
7.1 Introduction
7.2 The Comparison of DMS and EMS
7.3 DMS Functions
7.3.1 System Monitoring
7.3.1.1 State Estimation
7.3.1.2 Power Flow
7.3.2 Decision Support
7.3.2.1 Distribution System Modeler
7.3.2.2 Load Estimation
7.3.2.3 Short-Circuit Analysis
7.3.2.4 Short-Term Load Forecasting (STLF)
7.3.2.5 Volt-VAR Optimization (VVO)
7.3.2.6 Optimal Network Reconfiguration (ONR)
7.3.2.7 Fault Location, Isolation, and Service Restoration
7.3.2.8 Tagging
7.3.3 DMS Control Actions
7.4 DMS Architecture
7.4.1 Hardware Overview
7.4.2 Software Overview
7.4.2.1 SCADA
7.4.2.2 Data Management
7.4.2.3 Network Modeling
7.4.2.4 State Estimation
7.4.2.5 Load Flow
7.4.2.6 Forecasting
7.4.2.7 Congestion Management
7.4.2.8 Volt/Var Control
7.4.2.9 Fault Management and System Restoration
7.4.2.10 Reliability Analysis
7.4.2.11 System Operation and Security Management
7.4.2.12 System Planning Management
7.4.3 Characteristics of the DMS Architecture
7.4.3.1 Internet of Things (IoT)
7.4.3.2 Machine Learning and Data Analysis Techniques
7.4.3.3 Novel Local Market Models
7.4.3.4 Decentralized Management
7.4.4 DMS Interaction with Other Smart Grid Subsystems
7.4.4.1 Advanced Metering System
7.4.4.2 Energy Management System (EMS)
7.4.4.3 Geographical Information System (GIS)
7.5 DMS Challenges and Its New Requirements
7.5.1 State Estimation
7.5.2 Load Estimation
7.5.3 Load GIS
7.5.4 Synthetic Inertia
7.5.5 Energy Storage Monitoring
7.5.6 Vehicles to Grid
7.5.7 Scaling the DMS for Supervisory Control
7.5.7.1 Increased Observability
7.5.7.2 Active Network Management
7.5.7.3 Responsibility (Control) Transferring to DSOs
7.5.7.4 Self-Responsible Distribution System Operation
7.5.8 Multiarea Implementation of the DMS
References
8 Role of Volt-VAr-W Control in Energy Management
8.1 Introduction
8.2 OCP Integration with CVR
8.3 DG Integration with CVR
8.4 Conclusion
References
9 Active Management of Distribution Networks
9.1 Overview
9.2 Distribution Network Challenges
9.2.1 Operational Challenges
9.2.2 Network Reinforcement and Planning Challenges
9.3 Active Management for Planning and Operation of Future Distribution Networks
9.3.1 Planning of Active Distribution Networks
9.3.2 Operation of Active Distribution Networks
9.3.3 Voltage Control
9.3.4 Active Control of Inverter-Based Distributed Generation
9.3.5 Adaptive Droop Control of Distributed Energy Resources
9.3.6 Adaptive OLTC Control
9.3.7 Adaptive Active and Reactive Power Control Limits Between Voltage Levels
9.3.8 Traffic Light Concept
9.3.8.1 Green State
9.3.8.2 Yellow State
9.3.8.3 Red State
9.3.9 Adaptive Scheduling of Flexible Energy Resources
9.3.10 Increased Cooperation Between Transmission and Distribution Networks
9.4 Application of Energy Storage Systems in Active Network Management. 9.4.1 Integration of Renewable Energy Resources
9.4.2 Load Leveling
9.4.3 Peak Shaving
9.4.4 Power Quality Improvement
9.4.5 Active Power Management
9.5 Microgrids and Active Network Management
9.5.1 Challenges of Microgrids
9.6 Summary
References
10 Enhancing the Performance of the State Estimation Algorithm Through Optimally Placed Phasor Measurement Units
10.1 Introduction
10.2 Methodology
10.2.1 SE with Conventional Measurements
10.2.2 SE with Synchronized Phasor Measurements
10.2.3 Optimal Placement of PMU for the Performance Enhancement of SE
10.2.3.1 System Observability in State Estimation Type 2
10.2.4 Problem Formulation
10.2.4.1 Solution Algorithm
10.2.4.2 Least-Squares Equations Formulation
10.3 Results Analysis and Discussion
10.3.1 IEEE 14-Bus Power System
10.3.2 Southern Region 21-bus Network
10.4 Conclusion
References
11 Smart Microgrid Integration and Optimization
11.1 Introduction
11.2 Architecture and Traits of the Smart Grid
11.2.1 Layer Architecture of the Smart Grid
11.2.1.1 Power Layer
11.2.1.2 Operation and Access Control Layer
11.2.1.3 Market and Service Layer
11.2.1.4 Application Layer
11.3 Smart Microgrids
11.3.1 Architecture of the Smart Microgrid System
11.3.2 Operating Modes of a Smart Microgrid
11.3.3 Topologies for an Intelligent Microgrid
11.4 Demand Response
11.5 Demand Side Management
11.6 Agents in Smart Grids
11.6.1 Smart Grid Agent-Based Modeling
11.6.2 Role of Agents in the Smart Grid. 11.6.2.1 Regulation of the Smart Grid
11.6.2.2 Fault Management and Self-Healing
11.6.2.3 Energy Balance Management
11.7 Integrating Renewable Energy Sources in a Smart Microgrid
11.8 Modeling of Smart Microgrid Components
11.8.1 Objective Function. 11.8.1.1 Overall Cost
11.8.1.2 Cost of the Grid Supply
11.8.1.3 DG Operation and Maintenance Cost
11.8.2 Constraints
11.8.2.1 Electrical Demand Balance
11.8.2.2 Dispatchable DG Constraints
11.8.2.3 BES Constraints
11.8.2.3.1 Discharging Mode of Battery Energy Storage
11.2.3.2 Charging Mode of Battery Energy Storage
11.8.2.4 Grid Constraints
11.8.2.5 Diesel Generator Constraints
11.8.2.6 Battery Electric Vehicle (BEV) Constraints
11.8.2.6.1 Discharging Mode of a Battery Electric Vehicle
11.8.2.6.2 Charging Mode of a Battery Electric Vehicle
11.8.2.7 PHEV Constraints
11.8.2.7.1 Discharging Mode of a Plug-in Battery Electric Vehicle
11.8.2.7.2 Charging Mode of a Plug-in Battery Electric Vehicle
11.8.2.8 Operating Reserve Constraints
11.9 Optimization Techniques
11.9.1 Overview of the Optimization Platform
11.9.1.1 Genetic Algorithm
11.9.1.2 Particle Swarm Optimization
11.9.1.3 Harmony Search Algorithm
11.9.1.4 Tabu Search Algorithm
References
12 Control Algorithms for Energy Storage Systems to Reduce Distribution Power Loss of Microgrids
12.1 Introduction
12.2 ADE-Based Hierarchical Control for DBS in Standalone DC Microgrids
12.2.1 Overview of ADE-Based Hierarchical Control
12.2.2 Proposed ADE Algorithm
12.2.3 Simulation-Based Case Studies
12.3 PSO-Based Hierarchical Control for DBS in Standalone AC Microgrids
12.3.1 Overview of PSO-Based Hierarchical Control
12.3.2 Simulation-Based Case Studies
12.4 A CMPC-Based Hierarchical Control for ES in Standalone DC Microgrids
12.4.1 Overview of CMPC-Based Hierarchical Control
12.4.2 Simulation-Based Case Studies
12.5 A Predictive Control-Based Hierarchical Control for ES in Standalone AC Microgrids
12.5.1 Overview of Predictive Control-Based Hierarchical Control
12.5.2 Experiment-Based Case Studies
12.6 Summary
References
13 Higher Levels of Wind Energy Penetration into the Remote Grid Challenges and Solutions
13.1 Introduction
13.2 Present Scenario of WE Integration
13.2.1 Type 1 WEG System
13.2.2 Type 2 WEG System
13.2.3 Type 3 WEG System
13.2.4 Type 4 WEG System
13.3 Emerging Challenges Associated with WE Integration
13.3.1 Power Forecasting Challenge
13.3.2 Low-Voltage Ride-Through Challenge
13.3.3 Power Quality Challenges. 13.3.3.1 Voltage/Reactive Power Support
13.3.3.2 Frequency Support
13.3.3.3 Harmonics
13.3.3.4 Flicker
13.3.4 Angular Stability Challenge
13.3.5 Protection Challenge
13.3.6 Environmental Challenge
13.4 Smart Solutions Associated with WE Integration
13.4.1 Without Energy Storage-Based Solution
13.4.2 With Energy Storage-Based Solution
13.4.3 Additional or Separate Grid Side Converter-Based Solution
13.4.4 Other Possible Solutions
13.5 Conclusions
References
14 Internet of Things and Machine Learning for Improving Solar-PV Plant Efficiency Forecasting Aspects
14.1 Introduction
14.2 Forecasting
14.2.1 Factors Affecting Forecasting
14.2.2 Significant Challenges Associated with Forecasting
14.3 Internet of Things
14.4 Machine Learning
14.4.1 Supervised Learning
14.4.2 Unsupervised Learning
14.4.3 Reinforcement Learning
14.5 Forecasting and Efficiency Enhancement
14.5.1 Door 1: System Consideration
14.5.2 Door 2: Data Collection – IoT
14.5.3 Door 3: Machine Learning
14.5.4 Door 4: Learning about System Dependencies – Forecasting
14.5.5 Door 5: Improving Performance
14.6 Real-Time Application in a Solar-PV System
14.6.1 SRRA Station (Jodhpur, India)
14.6.2 SRRA Data Collection and Prediction
14.7 Conclusion
References
15 Modular Design of Nonlinear Controllers for Photovoltaic Distributed Generation Systems
15.1 Introduction
15.2 System Description and Modeling
15.2.1 Basic Blocks
15.2.2 Hierarchical Control Structure
15.2.3 State-Space Model
15.3 Secondary Level References
15.4 Backstepping Controller Design
15.4.1 Controller Design for PV Subsystem
15.4.2 Controller Design for the Battery Subsystem
15.5 Results
15.5.1 Case 1: Variation in Irradiance
15.5.2 Case 2: Variation in Temperature
15.5.3 Case 3: Variation in Load
15.6 Conclusion
References
16 Vehicle-to-Grid Challenges and Potential Benefits for Smart Microgrids
16.1 Introduction
16.2 Microgrid and Role of EV
16.3 V2G Components and Realization Models
16.4 Applications and Potential Benefits of a Vehicle to Grid
16.4.1 Technological Advantages. 16.4.1.1 V2G for Ancillary Services Market, Renewable Integration, and Other Possible Services
16.4.1.2 Economic Energy Storage System
16.4.2 Social Advantages
16.5 Challenges
16.5.1 Battery Degradation
16.5.2 Charger Efficiency and Energy Losses
16.5.3 Communication, Data, and Security
16.5.4 High Initial Investment Cost
16.5.5 Social Challenges
16.6 Conclusion
References
17 Reconfiguration of Radial Distribution Systems Test System
17.1 Introduction
17.2 Test Systems
17.2.1 System 1: 7-Bus Distribution Network
17.2.2 System 2: 12-Bus Distribution Network
17.2.3 System 3: 16-Bus Distribution Network
17.2.4 System 4: 28-Bus Distribution Network
17.2.5 System 5: 30-Bus Distribution Network
17.2.6 System 6: 33-Bus Distribution System
17.2.7 System 7: 49-Bus Distribution System
17.2.8 System 8: 59-Bus Distribution System
17.2.9 System 9: 69-Bus Distribution System
17.2.10 System 10: 70-Bus Distribution System
17.2.11 System 11: Distribution System of Taiwan Power Company (TPC)
17.2.12 System 12: 119-Bus Distribution System
17.2.13 System 13: 136-Bus Real Distribution System
17.2.14 System 14: 203-Bus Real Test System
17.2.15 System 15: 417-Bus Real Distribution System
17.3 Conclusion
References
18 Distribution System Reconfiguration: Case Studies
18.1 Introduction
18.2 Mathematical Modeling of the DSR Problem
18.2.1 Objective Function
18.2.2 Constraints
18.3 Case Studies
18.3.1 Case 1
18.3.2 Case 2
18.3.3 Case 3
18.3.4 Case 4
18.3.5 Case 5
18.3.6 Case 6
18.3.7 Case 7
18.3.8 Case 8
18.3.9 Case 9
18.3.10 Case 10
18.3.11 Case 11
18.3.12 Case 12
18.3.13 Case 13
18.3.14 Case 14
18.4 Conclusion
References
19 Genetic Algorithm Application in Distribution System Reconfiguration
19.1 Introduction
19.2 DSR Mathematical Model
19.2.1 Basic Model
19.2.2 GA-Based Model
19.3 Genetic Algorithm
19.3.1 Binary Codification GA (BCGA)
19.3.2 Decimal Codification GA (DCGA)
19.3.3 Improved DCGA (IDCGA)
19.3.4 Efficient DCGA (EDCGA)
19.4 Examples
19.5 Conclusion
References
20 Demand Response Techniques and Smart Home Energy Management Systems
20.1 Introduction
20.2 Different Demand Response (DR) Schemes
20.3 Demand Response-Based Flexibility Services
20.3.1 Peak-Load Shaving
20.3.2 Other Potential Services for Distribution System Operators (DSOs)
20.3.3 System-Wide Flexibility Services for TSOs
20.4 Smart Home and Home Energy Management System (HEMS)
20.5 Demand Response and a HEMS
20.5.1 Scheduling Residential Appliances
20.5.2 Participation in DR Programs with the HEMS
20.6 Scheduling Techniques for HEMS
20.6.1 Rule-Based Scheduling
20.6.2 Artificial Intelligence (AI)-Based Scheduling
20.6.3 Optimization-Based Scheduling
20.7 Summary
References
21 A Sustainable Building Lightning Solution for Energy Conservation in Different Geographical Conditions
21.1 Introduction
21.2 Energy Modeling
21.2.1 Green Building
21.2.2 Building Lighting
21.2.3 Orientation of a Window
21.2.4 Glazing
21.2.4.1 Single-Glazed Windows
21.2.4.1.1 Advantages of Single Glazing
21.2.4.1.2 Disadvantages of single glazing
21.2.4.2 Double-Glazed Windows
21.2.5 Selection of Glass
21.3 Methodology
21.4 Case Study
21.5 Result Analysis
21.5.1 Sun-Path Analysis
21.5.1.1 New Delhi – India
21.5.1.2 Rome – Italy
21.5.1.3 Reykjavik – Iceland
21.5.1.4 Nairobi – Kenya
21.5.1.6 Sydney – Australia
21.5.1.7 Summary
21.5.2 Impact of Orientation of a Window
21.5.2.1 New Delhi – India
21.5.2.2 Rome – Italy
21.5.2.3 Reykjavik – Iceland
21.5.2.4 Nairobi – Kenya
21.5.2.5 Sydney – Australia
21.5.2.6 Summary
21.5.3 Impact of Pane
21.5.4 Impact of Glass Type
21.6 Conclusion
References
22 Smart Metering Transforming from One-Way to Two-Way Communication
22.1 Introduction
22.2 Existing/Traditional System
22.2.1 Obstacles of a Traditional Meter
22.3 Smart Grid
22.4 Smart Meter
22.4.1 Development of a Meter System
22.4.2 Comparison Between a Traditional Meter and an SM
22.4.3 Smart Meter Architecture
22.4.4 SM Functionalities
22.4.5 SM Operating Principles
22.4.6 SM Components
22.4.7 Benefits of SMs
22.4.8 Barriers for SM
22.4.9 Summary of Smart Meter Section
22.5 Requirements for SM Applications
22.5.1 Vigorous Costing
22.5.2 Duplex Transmission
22.5.3 Isolated Service
22.5.4 Error Finding
22.5.5 Peak Demand Decreases
22.5.6 Analyzing the Efficiency of Delivery
22.5.7 Software Program Upgradation
22.5.8 Reclosure
22.6 Introduction to the AMI System
22.6.1 Meter Data Management System (MDMS)
22.6.2 Data Concentrator
22.6.3 Advantages of AMI
22.6.4 Protection in AMI
22.6.5 Protection of Confidential Information at the Consumer End
22.6.6 Protection Against Cyber Threats or Manual Attacks
22.6.7 Issues and Solutions in AMI
22.6.7.1 Trouble in Finding Failures
22.6.7.2 Dependence Among Grids and Transmission Modules of AMI
22.6.7.3 Issues to Find Network Constructed Attacks
22.6.7.4 Interruption Recognition, Protection, and Regaining for AMI
22.6.7.5 Vital Organization Practices for AMI
22.6.8 Safety Analysis
22.6.8.1 Safety Standards
22.6.8.2 Quantum Key Distribution (QKD) in AMI
22.6.9 Technologies Required for SM
22.6.9.1 Signal Acquirement
22.6.9.2 Signal Conditioning
22.6.9.3 Analog-to-Digital Conversion
22.6.9.4 Estimation
22.6.9.5 Input/Output
22.6.10 Summary of the AMI Section
22.7 Transmission Architecture
22.7.1 Various Transmission Architectures
22.7.1.1 Han
22.7.1.2 Nan
22.7.1.3 Wan
22.7.2 Transmission Technologies for AMI
22.7.2.1 Wired Transmission Technologies
22.7.2.2 Wireless Transmission Technologies
22.7.3 Summary of Transmission Architecture
References
Index
WILEY END USER LICENSE AGREEMENT
