Chapter
Chapter 2: Iranian smart grid: road map and metering program
1. Smart grid technology roadmap in Iran
1.2. Economic, social, and environmental requirements of smart grid development
1.4. Vision of Iran smart grid
1.7. Technology development, strategies, and measures
1.8. Financing and resource allocation
1.9. Updating and evaluation of the road map
1.9.1. Evaluation indices
1.9.2. Evaluation reports
1.10. Deployment strategy
2. National smart meter program
2.2. Goals and benefits of AMI implementation in Iran
2.3. System components and interfaces
2.4. Communication profile
2.4.1. MI1-CI1 (electricity meter-concentrator)
2.4.2. MI2-SI2 (electricity meter-CAS)
2.4.3. CI2-SI1 (concentrator-CAS)
2.4.4. CI3 (data concentrator to the smart grid devices)
2.4.5. MI3 (multiutility meter-electricity meter/communication hub)
2.6. ICT architecture and CAS communications
2.8.1. Security assumptions
2.8.2. Foundational security requirements
2.9.1. Use case 1: Provide periodic meter reads
2.9.2. Use case 2: Provide load profile
2.9.3. Use case 3: Provide power quality information
2.9.4. Use case 4: Provide interruption information
2.9.5. Use case 5: Provide tamper history (tamper detection)
2.9.6. Use case 6: Apply electricity threshold and load management
2.10. Application systems
Chapter 3: Intelligent control and protection in the Russian electric power system
1.1. Intelligent energy system as Russian vision of smart grid
1.2. Informational support of IESAAN control problems
1.3. Intelligent operation and smart emergency protection
1.4. Smart grid clusters in Russia
2. Intelligent energy system as Russian vision of smart grid
2.1. Technological platform, intelligent energy system of Russia
2.2. Intelligent electric power system with an active and adaptive network (IESAAN)
2.3. Control system of IESAAN
3. Informational support of IESAAN control problems
3.2. The electric power system state estimation problem. Specific features of state estimation for the control of IESAAN
3.3. The main directions in the development of SE methods and technologies of their application to control of IESAAN
3.3.1. Phasor measurements in the state estimation problem
3.3.1.1. The use of TEs for validation of measurements
3.3.1.2. Systematic errors in PMU measurements
3.3.2. State estimation of electric power system involving FACTS models
3.3.3. Dynamic state estimation and its application
3.3.3.1. Criteria for the estimate accuracy
3.3.3.2. Detection of bad data in measurements by the methods of dynamic EPS state estimation
3.3.3.3. Description of the devised method
3.3.4. Supporting cyber-physical security of the electric power system by the state estimation technique
3.3.4.1. Cybersecurity of SCADA systems and WAMS
3.3.4.2. Technique analysis of the cybersecurity of SCADA and WAMS in a two-level state estimation
3.3.4.3. Methodology of cyberattack identification
4. Intelligent operation and smart emergency protection
4.1. Emergency control system in Russia
4.2. Requirements for new emergency protection and operation systems
4.3. The system of monitoring, forecasting, and control of power systems
4.3.3. Security monitoring and control
4.4. Artificial intelligence applications
4.4.1. Forecast of state variables based on the dynamic state estimation method
4.4.2. Forecast of power system parameters based on a hybrid data-driven approach
4.4.3. Total transfer capability estimation method
4.4.4. Automatic decision tree-based system for online voltage security control of power systems
4.4.5. Multiagent coordination of emergency control devices
4.4.6. Intelligent system for preventing large-scale emergencies in power system
5. Smart grid clusters in Russia
5.1. Smart grid clusters in the east interconnected power system
5.1.1. Smart grid clusters
5.1.2. Pilot project for creation of territorial smart grid cluster in Russky and Popov Islands
5.2. Smart grid clusters in northwest interconnected power system
5.3. Pilot project on electricity supply to the Skolkovo innovation center
Chapter 4: Demand response: An enabling technology to achieve energy efficiency in a smart grid
2. Demand response development in the United States
2.1. Demand response at consumer-premise level
2.2. Demand response at utilities level
2.3. Demand response at ISO/RTO level
2.3.3. PJM interconnection
2.4. Incentive-based approaches vs. pricing-based approaches for residential DR
3. A distributed direct load-control mechanism for residential DR
3.1. Two-layer communication-based direct load-control architecture
3.1.1. Load information update phase
3.1.2. Target update phase
3.1.3. Admission control phase
3.2. Distributed demand target allocation in upper-layer EMC network
3.3. Lower-layer communication and admission control scheme
3.3.1. Load information update
3.3.2. Admission control mechanism
3.4. Nonintrusive operation for appliances
3.4.1. Customer override option
3.4.2. Preventing frequent ON/OFF switching
3.4.3. Operation deadline constraint
4.2. Effects of EMC network size
4.3. Effects of DR resources
Chapter 5: Development of a residential microgrid using home energy management systems
2. Home energy system overview
2.1. Communication protocol
2.2. System hardware configuration
2.3. System software configuration
2.4. Scheduling methodology
2.5. Case studies and results
3. Smart buildings/smart residential community
3.1. Communication protocol
3.2. System hardware/software control configuration
3.3. Scheduling methodology
3.4. Case studies and results
Part Three: South America
Chapter 6: Case studies in saving electricity in Brazil
1. Introduction—Brazilian motivation
2. Smart Grid perspective in Brazil
3. Main Smart Grid projects in Brazil
3.1. Cities of the future
3.5. Búzios Intelligent City
3.6. Fernando de Noronha Archipelago Smart Grid project
3.10. Paraná Smart Grid pilot
3.11. Elektro Smart Grid project
3.12. Summary of the 11 Smart Grid projects
4. Centers for research development and innovation (CRD&I)
5. Smart Grid roadmap—Brazilian case
6. Lessons learned, diagnostics, and barriers
Chapter 7: Automation for smart grids in Europe
1.1. Distribution system operators challenges and needs in the EU
1.1.1. Regulations about service continuity
1.1.2. Regulations about voltage quality
1.2. Smart grid automation demos in Europe
2.1. DSO control hierarchy
2.2. Commercial aggregator control hierarchy and interaction with DSO
3.1. Unareti field demonstrator
3.2. TUT laboratory demonstrator
3.3. RWTH laboratory demonstrator
4. Monitoring and forecast
4.1. Performance of the communication network for the LV monitoring
4.2. Analysis of data from the LV monitoring system
5. State estimation and voltage control
5.1. State estimation results
5.2. Secondary voltage control results
6. The role of the aggregator in the IDE4L automation architecture
Chapter 8: Smart distribution networks, demand side response, and community energy systems: Field trial experiences and s ...
1. The UK electricity context
1.1. Overview and future scenarios
1.2. Energy markets and key actors
1.2.1. Electricity markets and mechanisms
1.3. Distribution networks
1.4. The consumption side
2.1. Smart distribution networks
2.1.2. Commercial arrangements
2.1.3. Network security and reliability
2.2. Smart community energy systems
2.2.2. Commercial arrangements
3.1. Smart distribution networks
3.2. Smart community energy systems
4. Case studies and field trials
4.1. Smart distribution networks
4.1.1. Smart distribution network applications
4.1.2. Wider network implications
4.2. Smart community multienergy systems
4.2.1. Smart community applications
4.2.2. Wider system implications
Chapter 9: Impact of smart meter implementation on saving electricity in distribution networks in Romania
1. Overview: Romania and the European situation
1.2. Romania versus European countries
1.3. Configuration and key functionalities of the smart metering system in Romania
2. The current status of smart metering in Romania
2.2. The implementation of smart metering by DNOs
2.2.4. E.ON (DELGAZ grid)
3. Impact of smart metering implementation on saving electricity in distribution networks in Romania
3.2.1. Phase balancing using smart metering
3.2.2. Reduction of commercial losses based on smart metering
3.2.3. Smart metering-based decision making for replacement of distribution transformers
4. Conclusions and future trends
Chapter 10: Smart grid digitalization in Germany by standardized advanced metering infrastructure and green button
1. Applications in Germany
1.1. Role and applications of the transmission system operator
1.2. Role and applications of the distribution system operator
1.3. Role and applications of consumers and decentralized producers in electrical grids
1.4. Distribution grid digitalization as enabler for energy transition
2.1. Basic concepts, roles, and components of German AMI
2.2. AMI-based use cases for the German Energiewende
2.2.1. Use case ``providing data for state estimation´´
2.2.2. Use case ``providing data for invoicing´´
2.2.3. Use case ``tariff updating´´
2.2.4. Use case ``energy usage visualization´´
2.2.5. Use case ``invoice verification´´
2.2.6. Use case ``feed-in management´´
2.2.7. Use case ``virtual power plant´´
2.2.8. Use case ``home health monitoring´´
3.1. System and security architecture of the German AMI
3.2. Data privacy compliant metered data delivery
8. Case studies and field trials
9. Summary and conclusion
Chapter 11: Analysis of the future power systems's ability to enable sustainable energy—Using the case system of Smart G
1.2. Smart grid demonstration project at the Swedish island Gotland
1.3. Initial data analyses used as input when developing the method
2.2. Electric energy consumption model
2.3. Wind power generation model
2.4. Solar power generation model
2.5. Dynamic rating introduction, assumptions, and model used
2.6. Energy storage algorithm
3.1. Dynamic rating and utilizing correlations
3.3. Discussion and analyses of further energy storage utilization
Chapter 12: Application of cluster analysis for enhancing power consumption awareness in smart grids
2. Mathematical preliminaries
2.1. Elements of cluster analysis
2.1.1. Clustering techniques
2.1.2. Cluster classification
3. Detecting load outliers by clustering analysis
Chapter 13: Smart grids and the role of the electric vehicle to support the electricity grid during peak demand
2.1. What is a smart grid?
2.2. Innovations in the power industry to make grids smarter
2.3. Electricity networks worldwide face a number of challenges
2.4. Some customers demand individualized services
2.5. What factors make an electricity grid smart?
3.2. EV historic timeline
3.4. EVs and the smart grid
3.5. EV integration within the smart grid and its impact on electricity networks
3.6. Feature requirements for in-vehicle display unit
3.7. In-vehicle communication board
Chapter 14: Measurement-based voltage stability monitoring for load areas
2. N+1 buses equivalent system
2.1. Identification of external system parameters
2.2. Identification of load area parameters
2.3. Solving the power transfer limit of each tie line
3. Online scheme for implementation
4. Demonstration on a four-bus power system
5. Case studies on the NPCC test system
5.1. Scenario A: Generator trip followed by load increase leading to voltage instability
5.2. Scenario B: Generator trip followed by a tie line tip causing voltage instability
5.3. Scenario C: Two successive tie line trips causing voltage instability
5.4. Scenario D: Shunt switching to postpone voltage instability
6. Discussion and conclusions
Application of Smart Grid Technologies
：Case Studies in Saving Electricity in Different Parts of the World
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