| Description |
1 online resource : illustrations |
| Series |
IET power and energy series ; 88 |
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IET power and energy series ; 88.
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| Contents |
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 |
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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 |
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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 |
| Summary |
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 |
| Analysis |
smart metering |
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cyber-security |
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smart grid communication |
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smart grid modelling |
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smart energy system |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Print version record |
| Subject |
Smart power grids.
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BUSINESS & ECONOMICS -- Real Estate -- General.
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Smart power grids
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security of data.
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smart meters.
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smart power grids.
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| Form |
Electronic book
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| Author |
Sun, Hongjian, editor
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Hatziargyriou, Nikos, editor.
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Poor, H. Vincent, editor.
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Carpanini, Laurence, editor
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Sanchez-Fornie, Miguel A., editor.
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| ISBN |
9781523105793 |
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1523105798 |
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9781785611056 |
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1785611054 |
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