Smarter energy : from smart metering to the smart grid /
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| Imprint: | London : The Institution of Engineering and Technology, 2016. |
|---|---|
| Description: | 1 online resource : illustrations |
| Language: | English |
| Series: | IET power and energy series ; 88 IET power and energy series ; 88. |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11408157 |
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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 | ||
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| 490 | 1 | |a IET power and energy series ; |v 88 | |
| 588 | 0 | |a Print version record. | |
| 504 | |a Includes bibliographical references and index. | ||
| 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. |
| 650 | 0 | |a Smart power grids. |0 http://id.loc.gov/authorities/subjects/sh2011003732 | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Real Estate |x General. |2 bisacsh | |
| 650 | 7 | |a Smart power grids. |2 fast |0 (OCoLC)fst01792824 | |
| 650 | 7 | |a security of data. |2 inspect | |
| 650 | 7 | |a smart meters. |2 inspect | |
| 650 | 7 | |a smart power grids. |2 inspect | |
| 655 | 0 | |a Electronic book. | |
| 655 | 4 | |a Electronic books. | |
| 700 | 1 | |a Sun, Hongjian, |e editor. | |
| 700 | 1 | |a Hatziargyriou, Nikos, |e editor. |0 http://id.loc.gov/authorities/names/nb2011011882 | |
| 700 | 1 | |a Poor, H. Vincent, |e editor. |0 http://id.loc.gov/authorities/names/n85055471 | |
| 700 | 1 | |a Carpanini, Laurence, |e editor. | |
| 700 | 1 | |a Sanchez-Fornie, Miguel A., |e editor. |0 http://id.loc.gov/authorities/names/no2016124607 | |
| 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. |0 http://id.loc.gov/authorities/names/no2007033401 | |
| 903 | |a HeVa | ||
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| 928 | |t Library of Congress classification |a TK3105.S637 2016eb |l Online |c UC-FullText |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=e000xna&AN=1359621 |z eBooks on EBSCOhost |g ebooks |i 12444752 | ||