Application of smart grid technologies : case studies in saving electricity in different parts of the world

Application of smart grid technologies case studies in saving electricity in different parts of the world

Application of Smart Grid Technologies: Case Studies in Saving Electricity in Different Parts of the World provides a wide international view of smart grid technologies and their implementation in all regions of the globe. A brief overview of smart grid concepts and state-of-the art technologies is...

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Detalles Bibliográficos
Otros Autores: Lamont, Lisa, author (author), Lamont, Lisa, editor (editor), Ṣāʼigh, ʻAlī, editor
Formato: Libro electrónico
Idioma:Inglés
Publicado: London : Academic Press, an imprint of Elsevier [2018]
Edición:First edition
Materias:
Electric power > Conservation.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630468906719
Tabla de Contenidos:
  • Front Cover
  • Application of Smart Grid Technologies: Case Studies in Saving Electricity in Different Parts of the World
  • Copyright
  • Contents
  • List of contributors
  • Preface
  • Chapter 1: Smart grids-Overview and background information
  • 1. Introduction
  • 2. Definition
  • 3. Components
  • 4. Renewable energy resources
  • 5. Load management
  • 6. Energy storage
  • 7. Self-healing
  • 8. Customer active participation
  • 9. Security
  • 10. Power quality
  • 11. DG and storage
  • 12. Efficient operation
  • 13. Summary
  • References
  • Part One: Asia
  • Chapter 2: Iranian smart grid: road map and metering program
  • 1. Smart grid technology roadmap in Iran
  • 1.1. Introduction
  • 1.2. Economic, social, and environmental requirements of smart grid development
  • 1.3. Values
  • 1.4. Vision of Iran smart grid
  • 1.5. Grand policies
  • 1.6. Grand goals
  • 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
  • 2.1. Pilot project
  • 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)
  • 2.5. Layer model of AMI
  • 2.6. ICT architecture and CAS communications
  • 2.7. Interoperability
  • 2.8. Security
  • 2.8.1. Security assumptions
  • 2.8.2. Foundational security requirements
  • 2.9. Use cases
  • 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
  • 3. Conclusions
  • References
  • Further reading
  • Chapter 3: Intelligent control and protection in the Russian electric power system
  • 1. Summary
  • 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
  • 3.1. SCADA and WAMS
  • 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
  • 3.3.1.3. Decomposition
  • 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
  • 3.3.4.4. Case study
  • 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
  • 4.3.1. General structure
  • 4.3.2. Forecasting
  • 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
  • 6. Conclusion
  • References
  • Part Two: North America
  • Chapter 4: Demand response: An enabling technology to achieve energy efficiency in a smart grid
  • 1. Introduction
  • 2. Demand response development in the United States
  • 2.1. Demand response at consumer-premise level
  • 2.2. Demand response at utilities level
  • 2.2.1. PG&E
  • 2.2.2. SCE
  • 2.2.3. ComEd
  • 2.2.4. WPS
  • 2.2.5. Con Edison.
  • 2.2.6. Gulf Power
  • 2.3. Demand response at ISO/RTO level
  • 2.3.1. NYISO
  • 2.3.2. ERCOT
  • 2.3.3. PJM interconnection
  • 2.3.4. California ISO
  • 2.3.5. ISO New England
  • 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. Numerical results
  • 4.1. Scheduling results
  • 4.2. Effects of EMC network size
  • 4.3. Effects of DR resources
  • 5. Summary
  • References
  • Chapter 5: Development of a residential microgrid using home energy management systems
  • 1. Introduction
  • 2. Home energy system overview
  • 2.1. Communication protocol
  • 2.2. System hardware configuration
  • 2.3. System software configuration
  • 2.3.1. Monitoring
  • 2.3.2. Scheduling
  • 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
  • 4. Conclusion
  • References
  • 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
  • 3.2. Eletropaulo Digital.
  • 3.3. Smart Grid Light
  • 3.4. Parintins Project
  • 3.5. Búzios Intelligent City
  • 3.6. Fernando de Noronha Archipelago Smart Grid project
  • 3.7. InovCity project
  • 3.8. CPFL Smart Grid
  • 3.9. Aquiraz Smart City
  • 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
  • 7. Conclusions
  • References
  • Further reading
  • Part Four: Europe
  • Chapter 7: Automation for smart grids in Europe
  • 1. Introduction
  • 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
  • 1.3. IDE4L at a glance
  • 2. Architecture
  • 2.1. DSO control hierarchy
  • 2.2. Commercial aggregator control hierarchy and interaction with DSO
  • 3. IDE4L demo
  • 3.1. Unareti field demonstrator
  • 3.1.1. MV demo
  • 3.1.2. LV demo
  • 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
  • 7. Conclusions
  • References
  • 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
  • 1.2.2. Actors
  • 1.3. Distribution networks
  • 1.4. The consumption side.
  • 2. Smart grid features.

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