Smarter energy : from smart metering to the smart grid /

This book presents cutting-edge perspectives and research results in smart energy spanning multiple disciplines across four main topics: smart metering, smart grid modeling, control and optimisation, and smart grid communications and networking.

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Sun, Hongjian (Editor ), Hatziargyriou, Nikos (Editor ), Poor, H. Vincent (Editor ), Carpanini, Laurence (Editor ), Sanchez-Fornie, Miguel A. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : The Institution of Engineering and Technology, 2016.
Colección:IET power and energy series ; 88.
Temas:
Smart power grids.
Réseaux électriques intelligents.
BUSINESS & ECONOMICS > Real Estate > General.
Smart power grids
security of data.
smart meters.
smart power grids.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Machine generated contents note: 1. Smart energy
  • smart grid research and projects overview / Hongjian Sun
  • 1.1. Smart Grid
  • 1.1.1. Introduction
  • 1.1.2. Smart metering and data privacy
  • 1.1.3. Smart grid communications, networking and security
  • 1.1.4. Smart grid modelling, control and optimization
  • 1.2. Smart grid research: mapping of ongoing activities
  • 1.2.1. Europe
  • 1.2.2. United States of America
  • 1.2.3. Asia-Pacific
  • 1.3. Smart grid research in Europe: what comes next?
  • 1.4. SmarterEMC2 project
  • 1.4.1. Stakeholders involved in SmarterEMC2
  • 1.4.2. Conceptual architecture of the SmarterEMC2 ICT ecosystem
  • Acknowledgements
  • Bibliography
  • pt. I Smart metering
  • 2. Privacy-preserving data aggregation in smart metering systems / Fabio Borges
  • 2.1. Introduction
  • 2.2. Definitions
  • 2.2.1. List of acronyms
  • 2.2.2. List of symbols
  • 2.3. Background
  • 2.4. State-of-the-art protocols
  • 2.4.1. Homomorphic encryption
  • 2.4.2. Commitments
  • 2.4.3. Symmetric DC-Net (SDC-Net)
  • 2.4.4. Asymmetric DC-Net (ADC-Net)
  • 2.5. improved ADC-Net
  • 2.6. Comparison with related work
  • 2.6.1. Privacy
  • 2.6.2. Communication
  • 2.6.3. Processing time
  • 2.6.4. Techniques
  • 2.7. Simulations
  • 2.7.1. Real-world data set
  • 2.7.2. Software and hardware
  • 2.7.3. Simulation parameters
  • 2.7.4. Simulation results
  • 2.8. Conclusions
  • Acknowledgements
  • Bibliography
  • 3. Smart price-based scheduling of flexible residential appliances / Goran Strbac
  • Nomenclature
  • 3.1. Introduction
  • 3.1.1. Context
  • emerging challenges for low-carbon electrical power systems
  • 3.1.2. Role of residential demand in addressing emerging challenges
  • 3.1.3. Challenges in scheduling residential appliances
  • 3.1.4. Overview of alternative approaches for smart scheduling of residential appliances
  • 3.2. Modelling operation and price response of flexible residential appliances
  • 3.2.1. Appliances with continuously adjustable power levels
  • EV with smart charging capability
  • 3.2.2. Appliances with shiftable cycles
  • WA with delay functionality
  • 3.3. Measures against demand response concentration
  • 3.3.1. Flexibility restriction
  • 3.3.2. Non-linear pricing
  • 3.3.3. Randomised pricing
  • 3.3.4. Tuning the parameters of smart measures
  • 3.4. Case studies
  • 3.4.1. Scheduling of flexible residential appliances in electricity markets
  • 3.4.2. Scheduling of flexible residential appliances for management of local distribution networks
  • 3.5. Conclusions and future work
  • Bibliography
  • 4. Smart tariffs for demand response from smart metering platform / Furong Li
  • 4.1. Introduction
  • 4.2. Electricity tariff review
  • 4.2.1. Current energy tariff products
  • 4.2.2. Variable electricity tariffs
  • 4.3. Variable ToU tariff design
  • 4.3.1. Introduction
  • 4.3.2. Rationale of proposed tariff design
  • 4.3.3. ToU tariff design by equal interval grouping
  • 4.3.4. ToU tariff development by hierarchical clustering
  • 4.4. Results and discussion
  • 4.4.1. Results of RTP tariffs
  • 4.4.2. ToU tariffs by equal interval grouping
  • 4.4.3. ToU tariffs by hierarchical clustering
  • 4.5. Impact analysis of ToU tariffs
  • 4.5.1. Flexible load modelling
  • 4.5.2. Impact analysis of designed ToU tariffs
  • 4.5.3. Benefit quantification
  • 4.5.4. Cooperation with energy storage
  • 4.5.5. Case study
  • 4.6. Impact of networks on tariff design
  • 4.6.1. Quantification of DSR on network investment
  • 4.6.2. Tariff design in response to network conditions
  • 4.7. Discussion and conclusion
  • 4.7.1. Discussion
  • 4.7.2. Conclusion
  • Bibliography
  • pt. II Smart grid modeling, control and optimization
  • 5. Decentralized models for real-time renewable integration in future grid / Kiyoshi Nakayama
  • 5.1. Introduction to future smart grid
  • 5.2. Hybrid model of centralized resource management and decentralized grid control
  • 5.2.1. Centralized resource management
  • 5.2.2. Decentralized grid control
  • 5.3. Graph modeling
  • 5.4. Maximizing real-time renewable integration
  • 5.5. General decentralized approaches
  • 5.6. Distributed nodal approach
  • 5.6.1. Initialize
  • 5.6.2. Send
  • 5.6.3. Receive
  • 5.6.4. Compare
  • 5.6.5. Optimize
  • 5.6.6. Notify
  • 5.6.7. Confirm
  • 5.6.8. StandBy
  • 5.7. Distributed clustering approach
  • 5.7.1. Tie-set graph theory and its application to distributed systems
  • 5.7.2. Tie-set Based Optimization Algorithm
  • 5.8. Case study of decentralized grid control
  • 5.9. Simulation and experiments
  • 5.9.1. Energy stimulus response
  • 5.9.2. Convergence with different renewable penetration rates
  • 5.9.3. Comparison of TBO and DLP
  • 5.10. Summary
  • Bibliography
  • 6. Distributed and decentralized control in future power systems / Chris Dent
  • 6.1. Introduction
  • 6.2. look into current power systems control
  • 6.3. Identifying the role of distributed methods
  • 6.4. Distributed optimization definitions and scope
  • 6.4.1. Distributed optimization fundamentals
  • 6.4.2. Simple price-based decomposition
  • 6.4.3. From optimization to control using prices
  • 6.4.4. Making prices work
  • 6.5. Decomposition methods
  • 6.5.1. Improving price-updates
  • 6.5.2. Decomposing an augmented Lagrangian
  • 6.5.3. Proximal decomposition methods
  • 6.5.4. Optimality Condition Decomposition
  • 6.5.5. On other distributed methods
  • 6.6. OPF insights
  • 6.6.1. Decomposition structure considerations
  • 6.6.2. Practical application considerations
  • 6.7. UC time frame
  • 6.8. ED time frame
  • 6.9. Closer to real time
  • 6.10. Conclusions
  • Bibliography
  • 7. Multiobjective optimization for smart grid system design / Wei-Yu Chiu
  • 7.1. Introduction
  • 7.2. Problem formulation
  • 7.2.1. Model of MOP
  • 7.2.2. Design examples
  • 7.3. Solution methods
  • 7.4. Numerical results
  • 7.5. Conclusion
  • Acknowledgments
  • Bibliography
  • 8. Frequency regulation of smart grid via dynamic demand control and battery energy storage system / Lin Jiang
  • 8.1. Introduction
  • 8.2. Dynamic model of smart grid for frequency regulation
  • 8.2.1. Structure of frequency regulation
  • 8.2.2. Wind farm with variable-speed wind turbines
  • 8.2.3. Battery energy storage system
  • 8.2.4. Plug-in electric vehicles
  • 8.2.5. Controllable air conditioner based DDC
  • 8.2.6. State-space model of closed-loop LFC scheme
  • 8.3. Delay-dependent stability analysis
  • 8.3.1. Delay-dependent stability criterion
  • 8.3.2. Delay margin calculation
  • 8.4. Delay-dependent robust controller design
  • 8.4.1. Delay-dependent performance analysis
  • 8.4.2. Controller gain tuning based on the PSO algorithm
  • 8.5. Case studies
  • 8.5.1. Robust controller design
  • 8.5.2. Contribution of the DDC, BESS, and PEV to frequency regulation
  • 8.5.3. Robustness against to load disturbances
  • 8.5.4. Robustness against to parameters uncertainties
  • 8.5.5. Robustness against to time delays
  • 8.6. Conclusion
  • Bibliography
  • 9. Distributed frequency control and demand-side management / I.
  • Lestas
  • 9.1. Introduction
  • 9.1.1. Frequency control in the power grid
  • 9.1.2. Optimality in frequency control
  • 9.1.3. Demand-side management
  • 9.2. Swing equation dynamics
  • 9.3. Primary frequency control
  • 9.3.1. Historical development
  • 9.3.2. Passivity conditions for stability analysis
  • 9.3.3. Economic optimality and fairness in primary control
  • 9.3.4. Supply passivity framework for demand-side integration
  • 9.4. Secondary frequency control
  • 9.4.1. Historical development
  • 9.4.2. Economic optimality and fairness in secondary control
  • 9.4.3. Stability guarantees via a dissipativity framework
  • 9.5. Future challenges
  • Bibliography
  • 10. Game theory approaches for demand side management in the smart grid / Nikos Hatziargyriou
  • 10.1. Introduction
  • 10.1.1. Related bibliography
  • 10.1.2. Overview
  • 10.2. Bilevel decision framework for optimal energy procurement of DERs
  • 10.2.1. Nomenclature
  • 10.2.2. Model
  • 10.2.3. Solution methodology
  • 10.2.4. Implementation
  • 10.2.5. Results
  • 10.3. Bilevel decision framework for optimal energy management of DERs
  • 10.3.1. Nomenclature
  • 10.3.2. Model
  • 10.3.3. Solution methodology
  • 10.3.4. Implementation
  • 10.3.5. Results
  • 10.4. Conclusions
  • Bibliography
  • pt. III Smart grid communications and networking
  • 11. Cyber security of smart grid state estimation: attacks and defense mechanisms / Zhong Fan
  • 11.1. Power system state estimation and FDIAs
  • 11.1.1. State estimation
  • 11.1.2. Malicious FDIAs
  • 11.2. Stealth attack strategies
  • 11.2.1. Random attacks
  • 11.2.2. Numerical results
  • 11.2.3. Target attacks
  • 11.2.4. Numerical results
  • 11.3. Defense mechanisms
  • 11.3.1. Strategic protection
  • 11.3.2. Numerical results
  • 11.3.3. Robust detection
  • 11.3.4. Numerical results
  • 11.4. Conclusions
  • Bibliography
  • 12. Overview of research in the ADVANTAGE project / Dejan Vukobratovic
  • 12.1. Introduction
  • 12.2. Cellular-enabled D2D communication for smart grid neighbourhood area networks
  • 12.2.1. Limitations of LTE technology
  • 12.2.2. promising approach: LTE-D2D communication
  • 12.2.3. State of the art
  • open challenges
  • 12.2.4. Conclusions and outlook
  • 12.3. Power talk in DC MicroGrids: merging primary control with communication.
  • Note continued: 12.3.1. Why power talk?
  • 12.3.2. Embedding information in primary control loops
  • 12.3.3. One-way power talk communication
  • 12.3.4. Conclusions and outlook
  • 12.4. Compression techniques for smart meter data
  • 12.4.1. Introduction
  • 12.4.2. Basic concepts of data compression
  • 12.4.3. Smart meter data and communication scenario
  • 12.5. State estimation in electric power distribution system with belief propagation algorithm
  • 12.5.1. Introduction
  • 12.5.2. Conventional state estimation
  • 12.5.3. Belief propagation algorithm in electric power distribution system
  • 12.6. Research and design of novel control algorithms needed for the effective integration of distributed generators
  • 12.6.1. Overview
  • 12.6.2. Hierarchical control of a microgrid
  • 12.6.3. Conclusions and outlook
  • 12.7. Chapter conclusions
  • Acknowledgements
  • Bibliography
  • 13. Big data analysis of power grid from random matrix theory / Qian Ai
  • 13.1. Background for conduct SA in power grid with big data analytics
  • 13.1.1. Smart grid
  • an essential big data system with 4Vs data
  • 13.1.2. Smart grid and its stability, control, and SA
  • 13.1.3. Approach to SA
  • big data analytics and unsupervised learning mechanism
  • 13.1.4. RMM and probability in high dimension
  • 13.2. Three general principles related to big data analytics
  • 13.2.1. Concentration
  • 13.2.2. Suprema
  • 13.2.3. Universality
  • 13.3. Fundamentals of random matrices
  • 13.3.1. Types of matrices
  • 13.3.2. Central limiting theorem
  • 13.3.3. Limit results of GUE and LUE
  • 13.3.4. Asymptotic expansion for the Stieltjes transform of GUE
  • 13.3.5. rate of convergence for spectra of GUE and LUE
  • 13.4. From power grid to RMM
  • 13.5. LES and related research
  • 13.5.1. Definition of LES
  • 13.5.2. Law of Large Numbers
  • 13.5.3. CLTs of LES
  • 13.5.4. CLT for covariance matrices
  • 13.5.5. LES for Ring law
  • 13.5.6. LES for covariance matrices
  • 13.6. Data preprocessing
  • data fusion
  • 13.6.1. Augmented matrix method for power systems
  • 13.6.2. Another kind of data fusion
  • 13.7. new methodology and epistemology for power systems
  • 13.7.1. evolution of power systems and group-work mode
  • 13.7.2. methodology of SA for smart grids
  • 13.7.3. Novel indicator system and its advantages
  • 13.8. Case studies
  • 13.8.1. Case 1: anomaly detection and statistical indicators designing using simulated 118-bus system
  • 13.8.2. Case 2: correlation analysis for single factor using simulated 118-bus system
  • 13.8.3. Case 3: advantages of LES and visualization using 3D power-map
  • 13.8.4. Case 4: SA using real data
  • Bibliography
  • 14. model-driven evaluation of demand response communication protocols for smart grid / Rune Hylsberg Jacobsen
  • 14.1. Introduction
  • 14.2. State of the art
  • 14.3. Background
  • 14.3.1. Demand response reference architecture
  • 14.3.2. Demand response programs
  • 14.3.3. Demand response protocols
  • 14.3.4. Modeling languages and tools
  • 14.3.5. Evaluation metrics
  • 14.4. methodology
  • 14.4.1. Describing household scenarios, demand response strategy, and protocol
  • 14.4.2. Platform-independent and executable descriptions
  • 14.4.3. Evaluating demand response strategy and protocol
  • 14.5. Proof of concept
  • 14.6. Experimental results
  • 14.6.1. Case 1: individual household
  • 14.6.2. Case 2: load aggregation
  • 14.7. Conclusion
  • Acknowledgments
  • Bibliography
  • 15. Energy-efficient smart grid communications / F. Richard Yu
  • 15.1. Introduction
  • 15.2. Energy-efficient wireless smart grid communications
  • 15.3. System model
  • 15.4. Problem transformation
  • 15.5. Non-cooperative game formulation
  • 15.5.1. Utility function of each DAU in the multicell OFDMA cellular network
  • 15.5.2. Game formulation within each time slot
  • 15.6. Analysis of the proposed EE resource allocation game with fairness
  • 15.6.1. Subchannel assignment algorithm
  • 15.6.2. Non-cooperative EE power allocation game
  • 15.6.3. Properties of the interference pricing function factors
  • 15.6.4. Existence of the NE in the proposed game
  • 15.6.5. Proposed parallel iterative algorithm
  • 15.7. EE resource allocation iterative algorithm
  • 15.8. Simulation results and discussions
  • 15.9. Conclusions
  • Appendix
  • A. Proof of Theorem 15.1
  • B. Proof of Proposition 15.5
  • C. Proof of Proposition 15.3.

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