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.

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Bibliographic Details
Other Authors: Sun, Hongjian, (Editor), Hatziargyriou, Nikos, (Editor), Poor, H. Vincent, (Editor), Carpanini, Laurence, (Editor), Sanchez-Fornie, Miguel A., (Editor)
Format: eBook
Language: English
Published: London : The Institution of Engineering and Technology, 2016.
Series: IET power and energy series ; 88.
Subjects:
Smart power grids.
elektronické knihy
electronic books
ISBN: 9781523105793
1523105798
9781785611056
1785611054
9781785611049
1785611046
Physical Description: 1 online resource : illustrations

Cover

Table of contents

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245 0 0 |a Smarter energy :  |b from smart metering to the smart grid /  |c edited by Hongjian Sun, Nikos Hatziargyriou, H. Vincent Poor, Laurence Carpanini and Miguel Angel Sánchez Fornié. 
264 1 |a London :  |b The Institution of Engineering and Technology,  |c 2016. 
300 |a 1 online resource :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a IET power and energy series ;  |v 88 
504 |a Includes bibliographical references and index. 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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. 
505 0 0 |g Machine generated contents note:  |g 1.  |t Smart energy -- smart grid research and projects overview /  |r Hongjian Sun --  |g 1.1.  |t Smart Grid --  |g 1.1.1.  |t Introduction --  |g 1.1.2.  |t Smart metering and data privacy --  |g 1.1.3.  |t Smart grid communications, networking and security --  |g 1.1.4.  |t Smart grid modelling, control and optimization --  |g 1.2.  |t Smart grid research: mapping of ongoing activities --  |g 1.2.1.  |t Europe --  |g 1.2.2.  |t United States of America --  |g 1.2.3.  |t Asia-Pacific --  |g 1.3.  |t Smart grid research in Europe: what comes next? --  |g 1.4.  |t SmarterEMC2 project --  |g 1.4.1.  |t Stakeholders involved in SmarterEMC2 --  |g 1.4.2.  |t Conceptual architecture of the SmarterEMC2 ICT ecosystem --  |t Acknowledgements --  |t Bibliography --  |g pt. I  |t Smart metering --  |g 2.  |t Privacy-preserving data aggregation in smart metering systems /  |r Fabio Borges --  |g 2.1.  |t Introduction --  |g 2.2.  |t Definitions --  |g 2.2.1.  |t List of acronyms --  |g 2.2.2.  |t List of symbols --  |g 2.3.  |t Background --  |g 2.4.  |t State-of-the-art protocols --  |g 2.4.1.  |t Homomorphic encryption --  |g 2.4.2.  |t Commitments --  |g 2.4.3.  |t Symmetric DC-Net (SDC-Net) --  |g 2.4.4.  |t Asymmetric DC-Net (ADC-Net) --  |g 2.5.  |t improved ADC-Net --  |g 2.6.  |t Comparison with related work --  |g 2.6.1.  |t Privacy --  |g 2.6.2.  |t Communication --  |g 2.6.3.  |t Processing time --  |g 2.6.4.  |t Techniques --  |g 2.7.  |t Simulations --  |g 2.7.1.  |t Real-world data set --  |g 2.7.2.  |t Software and hardware --  |g 2.7.3.  |t Simulation parameters --  |g 2.7.4.  |t Simulation results --  |g 2.8.  |t Conclusions --  |t Acknowledgements --  |t Bibliography --  |g 3.  |t Smart price-based scheduling of flexible residential appliances /  |r Goran Strbac --  |t Nomenclature --  |g 3.1.  |t Introduction --  |g 3.1.1.  |t Context -- emerging challenges for low-carbon electrical power systems --  |g 3.1.2.  |t Role of residential demand in addressing emerging challenges --  |g 3.1.3.  |t Challenges in scheduling residential appliances --  |g 3.1.4.  |t Overview of alternative approaches for smart scheduling of residential appliances --  |g 3.2.  |t Modelling operation and price response of flexible residential appliances --  |g 3.2.1.  |t Appliances with continuously adjustable power levels -- EV with smart charging capability --  |g 3.2.2.  |t Appliances with shiftable cycles -- WA with delay functionality --  |g 3.3.  |t Measures against demand response concentration --  |g 3.3.1.  |t Flexibility restriction --  |g 3.3.2.  |t Non-linear pricing --  |g 3.3.3.  |t Randomised pricing --  |g 3.3.4.  |t Tuning the parameters of smart measures --  |g 3.4.  |t Case studies --  |g 3.4.1.  |t Scheduling of flexible residential appliances in electricity markets --  |g 3.4.2.  |t Scheduling of flexible residential appliances for management of local distribution networks --  |g 3.5.  |t Conclusions and future work --  |t Bibliography --  |g 4.  |t Smart tariffs for demand response from smart metering platform /  |r Furong Li --  |g 4.1.  |t Introduction --  |g 4.2.  |t Electricity tariff review --  |g 4.2.1.  |t Current energy tariff products --  |g 4.2.2.  |t Variable electricity tariffs --  |g 4.3.  |t Variable ToU tariff design --  |g 4.3.1.  |t Introduction --  |g 4.3.2.  |t Rationale of proposed tariff design --  |g 4.3.3.  |t ToU tariff design by equal interval grouping --  |g 4.3.4.  |t ToU tariff development by hierarchical clustering --  |g 4.4.  |t Results and discussion --  |g 4.4.1.  |t Results of RTP tariffs --  |g 4.4.2.  |t ToU tariffs by equal interval grouping --  |g 4.4.3.  |t ToU tariffs by hierarchical clustering --  |g 4.5.  |t Impact analysis of ToU tariffs --  |g 4.5.1.  |t Flexible load modelling --  |g 4.5.2.  |t Impact analysis of designed ToU tariffs --  |g 4.5.3.  |t Benefit quantification --  |g 4.5.4.  |t Cooperation with energy storage --  |g 4.5.5.  |t Case study --  |g 4.6.  |t Impact of networks on tariff design --  |g 4.6.1.  |t Quantification of DSR on network investment --  |g 4.6.2.  |t Tariff design in response to network conditions --  |g 4.7.  |t Discussion and conclusion --  |g 4.7.1.  |t Discussion --  |g 4.7.2.  |t Conclusion --  |t Bibliography --  |g pt. II  |t Smart grid modeling, control and optimization --  |g 5.  |t Decentralized models for real-time renewable integration in future grid /  |r Kiyoshi Nakayama --  |g 5.1.  |t Introduction to future smart grid --  |g 5.2.  |t Hybrid model of centralized resource management and decentralized grid control --  |g 5.2.1.  |t Centralized resource management --  |g 5.2.2.  |t Decentralized grid control --  |g 5.3.  |t Graph modeling --  |g 5.4.  |t Maximizing real-time renewable integration --  |g 5.5.  |t General decentralized approaches --  |g 5.6.  |t Distributed nodal approach --  |g 5.6.1.  |t Initialize --  |g 5.6.2.  |t Send --  |g 5.6.3.  |t Receive --  |g 5.6.4.  |t Compare --  |g 5.6.5.  |t Optimize --  |g 5.6.6.  |t Notify --  |g 5.6.7.  |t Confirm --  |g 5.6.8.  |t StandBy --  |g 5.7.  |t Distributed clustering approach --  |g 5.7.1.  |t Tie-set graph theory and its application to distributed systems --  |g 5.7.2.  |t Tie-set Based Optimization Algorithm --  |g 5.8.  |t Case study of decentralized grid control --  |g 5.9.  |t Simulation and experiments --  |g 5.9.1.  |t Energy stimulus response --  |g 5.9.2.  |t Convergence with different renewable penetration rates --  |g 5.9.3.  |t Comparison of TBO and DLP --  |g 5.10.  |t Summary --  |t Bibliography --  |g 6.  |t Distributed and decentralized control in future power systems /  |r Chris Dent --  |g 6.1.  |t Introduction --  |g 6.2.  |t look into current power systems control --  |g 6.3.  |t Identifying the role of distributed methods --  |g 6.4.  |t Distributed optimization definitions and scope --  |g 6.4.1.  |t Distributed optimization fundamentals --  |g 6.4.2.  |t Simple price-based decomposition --  |g 6.4.3.  |t From optimization to control using prices --  |g 6.4.4.  |t Making prices work --  |g 6.5.  |t Decomposition methods --  |g 6.5.1.  |t Improving price-updates --  |g 6.5.2.  |t Decomposing an augmented Lagrangian --  |g 6.5.3.  |t Proximal decomposition methods --  |g 6.5.4.  |t Optimality Condition Decomposition --  |g 6.5.5.  |t On other distributed methods --  |g 6.6.  |t OPF insights --  |g 6.6.1.  |t Decomposition structure considerations --  |g 6.6.2.  |t Practical application considerations --  |g 6.7.  |t UC time frame --  |g 6.8.  |t ED time frame --  |g 6.9.  |t Closer to real time --  |g 6.10.  |t Conclusions --  |t Bibliography --  |g 7.  |t Multiobjective optimization for smart grid system design /  |r Wei-Yu Chiu --  |g 7.1.  |t Introduction --  |g 7.2.  |t Problem formulation --  |g 7.2.1.  |t Model of MOP --  |g 7.2.2.  |t Design examples --  |g 7.3.  |t Solution methods --  |g 7.4.  |t Numerical results --  |g 7.5.  |t Conclusion --  |t Acknowledgments --  |t Bibliography --  |g 8.  |t Frequency regulation of smart grid via dynamic demand control and battery energy storage system /  |r Lin Jiang --  |g 8.1.  |t Introduction --  |g 8.2.  |t Dynamic model of smart grid for frequency regulation --  |g 8.2.1.  |t Structure of frequency regulation --  |g 8.2.2.  |t Wind farm with variable-speed wind turbines --  |g 8.2.3.  |t Battery energy storage system --  |g 8.2.4.  |t Plug-in electric vehicles --  |g 8.2.5.  |t Controllable air conditioner based DDC --  |g 8.2.6.  |t State-space model of closed-loop LFC scheme --  |g 8.3.  |t Delay-dependent stability analysis --  |g 8.3.1.  |t Delay-dependent stability criterion --  |g 8.3.2.  |t Delay margin calculation --  |g 8.4.  |t Delay-dependent robust controller design --  |g 8.4.1.  |t Delay-dependent performance analysis --  |g 8.4.2.  |t Controller gain tuning based on the PSO algorithm --  |g 8.5.  |t Case studies --  |g 8.5.1.  |t Robust controller design --  |g 8.5.2.  |t Contribution of the DDC, BESS, and PEV to frequency regulation --  |g 8.5.3.  |t Robustness against to load disturbances --  |g 8.5.4.  |t Robustness against to parameters uncertainties --  |g 8.5.5.  |t Robustness against to time delays --  |g 8.6.  |t Conclusion --  |t Bibliography --  |g 9.  |t Distributed frequency control and demand-side management /  |r I. 
505 0 0 |g Lestas --  |g 9.1.  |t Introduction --  |g 9.1.1.  |t Frequency control in the power grid --  |g 9.1.2.  |t Optimality in frequency control --  |g 9.1.3.  |t Demand-side management --  |g 9.2.  |t Swing equation dynamics --  |g 9.3.  |t Primary frequency control --  |g 9.3.1.  |t Historical development --  |g 9.3.2.  |t Passivity conditions for stability analysis --  |g 9.3.3.  |t Economic optimality and fairness in primary control --  |g 9.3.4.  |t Supply passivity framework for demand-side integration --  |g 9.4.  |t Secondary frequency control --  |g 9.4.1.  |t Historical development --  |g 9.4.2.  |t Economic optimality and fairness in secondary control --  |g 9.4.3.  |t Stability guarantees via a dissipativity framework --  |g 9.5.  |t Future challenges --  |t Bibliography --  |g 10.  |t Game theory approaches for demand side management in the smart grid /  |r Nikos Hatziargyriou --  |g 10.1.  |t Introduction --  |g 10.1.1.  |t Related bibliography --  |g 10.1.2.  |t Overview --  |g 10.2.  |t Bilevel decision framework for optimal energy procurement of DERs --  |g 10.2.1.  |t Nomenclature --  |g 10.2.2.  |t Model --  |g 10.2.3.  |t Solution methodology --  |g 10.2.4.  |t Implementation --  |g 10.2.5.  |t Results --  |g 10.3.  |t Bilevel decision framework for optimal energy management of DERs --  |g 10.3.1.  |t Nomenclature --  |g 10.3.2.  |t Model --  |g 10.3.3.  |t Solution methodology --  |g 10.3.4.  |t Implementation --  |g 10.3.5.  |t Results --  |g 10.4.  |t Conclusions --  |t Bibliography --  |g pt. III  |t Smart grid communications and networking --  |g 11.  |t Cyber security of smart grid state estimation: attacks and defense mechanisms /  |r Zhong Fan --  |g 11.1.  |t Power system state estimation and FDIAs --  |g 11.1.1.  |t State estimation --  |g 11.1.2.  |t Malicious FDIAs --  |g 11.2.  |t Stealth attack strategies --  |g 11.2.1.  |t Random attacks --  |g 11.2.2.  |t Numerical results --  |g 11.2.3.  |t Target attacks --  |g 11.2.4.  |t Numerical results --  |g 11.3.  |t Defense mechanisms --  |g 11.3.1.  |t Strategic protection --  |g 11.3.2.  |t Numerical results --  |g 11.3.3.  |t Robust detection --  |g 11.3.4.  |t Numerical results --  |g 11.4.  |t Conclusions --  |t Bibliography --  |g 12.  |t Overview of research in the ADVANTAGE project /  |r Dejan Vukobratovic --  |g 12.1.  |t Introduction --  |g 12.2.  |t Cellular-enabled D2D communication for smart grid neighbourhood area networks --  |g 12.2.1.  |t Limitations of LTE technology --  |g 12.2.2.  |t promising approach: LTE-D2D communication --  |g 12.2.3.  |t State of the art -- open challenges --  |g 12.2.4.  |t Conclusions and outlook --  |g 12.3.  |t Power talk in DC MicroGrids: merging primary control with communication. 
505 0 0 |g Note continued:  |g 12.3.1.  |t Why power talk? --  |g 12.3.2.  |t Embedding information in primary control loops --  |g 12.3.3.  |t One-way power talk communication --  |g 12.3.4.  |t Conclusions and outlook --  |g 12.4.  |t Compression techniques for smart meter data --  |g 12.4.1.  |t Introduction --  |g 12.4.2.  |t Basic concepts of data compression --  |g 12.4.3.  |t Smart meter data and communication scenario --  |g 12.5.  |t State estimation in electric power distribution system with belief propagation algorithm --  |g 12.5.1.  |t Introduction --  |g 12.5.2.  |t Conventional state estimation --  |g 12.5.3.  |t Belief propagation algorithm in electric power distribution system --  |g 12.6.  |t Research and design of novel control algorithms needed for the effective integration of distributed generators --  |g 12.6.1.  |t Overview --  |g 12.6.2.  |t Hierarchical control of a microgrid --  |g 12.6.3.  |t Conclusions and outlook --  |g 12.7.  |t Chapter conclusions --  |t Acknowledgements --  |t Bibliography --  |g 13.  |t Big data analysis of power grid from random matrix theory /  |r Qian Ai --  |g 13.1.  |t Background for conduct SA in power grid with big data analytics --  |g 13.1.1.  |t Smart grid -- an essential big data system with 4Vs data --  |g 13.1.2.  |t Smart grid and its stability, control, and SA --  |g 13.1.3.  |t Approach to SA -- big data analytics and unsupervised learning mechanism --  |g 13.1.4.  |t RMM and probability in high dimension --  |g 13.2.  |t Three general principles related to big data analytics --  |g 13.2.1.  |t Concentration --  |g 13.2.2.  |t Suprema --  |g 13.2.3.  |t Universality --  |g 13.3.  |t Fundamentals of random matrices --  |g 13.3.1.  |t Types of matrices --  |g 13.3.2.  |t Central limiting theorem --  |g 13.3.3.  |t Limit results of GUE and LUE --  |g 13.3.4.  |t Asymptotic expansion for the Stieltjes transform of GUE --  |g 13.3.5.  |t rate of convergence for spectra of GUE and LUE --  |g 13.4.  |t From power grid to RMM --  |g 13.5.  |t LES and related research --  |g 13.5.1.  |t Definition of LES --  |g 13.5.2.  |t Law of Large Numbers --  |g 13.5.3.  |t CLTs of LES --  |g 13.5.4.  |t CLT for covariance matrices --  |g 13.5.5.  |t LES for Ring law --  |g 13.5.6.  |t LES for covariance matrices --  |g 13.6.  |t Data preprocessing -- data fusion --  |g 13.6.1.  |t Augmented matrix method for power systems --  |g 13.6.2.  |t Another kind of data fusion --  |g 13.7.  |t new methodology and epistemology for power systems --  |g 13.7.1.  |t evolution of power systems and group-work mode --  |g 13.7.2.  |t methodology of SA for smart grids --  |g 13.7.3.  |t Novel indicator system and its advantages --  |g 13.8.  |t Case studies --  |g 13.8.1.  |t Case 1: anomaly detection and statistical indicators designing using simulated 118-bus system --  |g 13.8.2.  |t Case 2: correlation analysis for single factor using simulated 118-bus system --  |g 13.8.3.  |t Case 3: advantages of LES and visualization using 3D power-map --  |g 13.8.4.  |t Case 4: SA using real data --  |t Bibliography --  |g 14.  |t model-driven evaluation of demand response communication protocols for smart grid /  |r Rune Hylsberg Jacobsen --  |g 14.1.  |t Introduction --  |g 14.2.  |t State of the art --  |g 14.3.  |t Background --  |g 14.3.1.  |t Demand response reference architecture --  |g 14.3.2.  |t Demand response programs --  |g 14.3.3.  |t Demand response protocols --  |g 14.3.4.  |t Modeling languages and tools --  |g 14.3.5.  |t Evaluation metrics --  |g 14.4.  |t methodology --  |g 14.4.1.  |t Describing household scenarios, demand response strategy, and protocol --  |g 14.4.2.  |t Platform-independent and executable descriptions --  |g 14.4.3.  |t Evaluating demand response strategy and protocol --  |g 14.5.  |t Proof of concept --  |g 14.6.  |t Experimental results --  |g 14.6.1.  |t Case 1: individual household --  |g 14.6.2.  |t Case 2: load aggregation --  |g 14.7.  |t Conclusion --  |t Acknowledgments --  |t Bibliography --  |g 15.  |t Energy-efficient smart grid communications /  |r F. Richard Yu --  |g 15.1.  |t Introduction --  |g 15.2.  |t Energy-efficient wireless smart grid communications --  |g 15.3.  |t System model --  |g 15.4.  |t Problem transformation --  |g 15.5.  |t Non-cooperative game formulation --  |g 15.5.1.  |t Utility function of each DAU in the multicell OFDMA cellular network --  |g 15.5.2.  |t Game formulation within each time slot --  |g 15.6.  |t Analysis of the proposed EE resource allocation game with fairness --  |g 15.6.1.  |t Subchannel assignment algorithm --  |g 15.6.2.  |t Non-cooperative EE power allocation game --  |g 15.6.3.  |t Properties of the interference pricing function factors --  |g 15.6.4.  |t Existence of the NE in the proposed game --  |g 15.6.5.  |t Proposed parallel iterative algorithm --  |g 15.7.  |t EE resource allocation iterative algorithm --  |g 15.8.  |t Simulation results and discussions --  |g 15.9.  |t Conclusions --  |t Appendix --  |g A.  |t Proof of Theorem 15.1 --  |g B.  |t Proof of Proposition 15.5 --  |g C.  |t Proof of Proposition 15.3. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a Smart power grids. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Sun, Hongjian,  |e editor. 
700 1 |a Hatziargyriou, Nikos,  |e editor. 
700 1 |a Poor, H. Vincent,  |e editor. 
700 1 |a Carpanini, Laurence,  |e editor. 
700 1 |a Sanchez-Fornie, Miguel A.,  |e editor. 
776 0 8 |i Print version:  |t Smarter energy.  |d London : The Institution of Engineering and Technology, 2016  |z 9781785611049  |w (DLC) 2016499838  |w (OCoLC)966602733 
830 0 |a IET power and energy series ;  |v 88. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpSEFSMSG1/smarter-energy-from?kpromoter=marc  |y Full text 

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