This book provides a thorough treatment of privacy and security issues for researchers in the fields of smart grids, engineering, and computer science. It presents comprehensive insight to understanding the big picture of privacy and security challenges in both physical and information aspects of smart grids. The authors utilize an advanced interdisciplinary approach to address the existing security and privacy issues and propose legitimate countermeasures for each of them in the standpoint of both computing and electrical engineering. The proposed methods are theoretically proofed by mathematical tools and illustrated by real-world examples.…mehr
This book provides a thorough treatment of privacy and security issues for researchers in the fields of smart grids, engineering, and computer science. It presents comprehensive insight to understanding the big picture of privacy and security challenges in both physical and information aspects of smart grids. The authors utilize an advanced interdisciplinary approach to address the existing security and privacy issues and propose legitimate countermeasures for each of them in the standpoint of both computing and electrical engineering. The proposed methods are theoretically proofed by mathematical tools and illustrated by real-world examples.
1 Overview of the Security and Privacy Issues in Smart Grids1.1 Security Issues in Smart Grid1.2 Physical Network Security1.3 Information Network Security1.4 Privacy Issues in Smart Grids1.5 Book Structure and Outlook I Physical Network Security 2 Reliability in Smart Grids2.1 Introduction2.2 Preliminaries on Reliability Quantification2.3 System Adequacy Quantification2.4 Congestion Prevention: An Economic Dispatch Algorithm2.4.1 9-bus Test Network2.4.2 IEEE 30-Bus Test Network2.5 Summary and Conclusion 3 Error Detection of DC Power Flow using State Estimation3.1 Introduction3.2 Preliminaries of the DC Power Flow and State Estimation3.2.1 Introduction to State Estimation3.3 Minimum-Variance Unbiased Estimator (MVUE)3.3.1 Measurement Error Representation in the Linear DC Power Flow Equation3.3.2 Linear Model3.3.3 Generalized Linear Model for State Estimation3.4 Bayesian-based LMMSE Estimator for DC Power Flow Estimation3.4.1 Linear Model3.4.2 Bayesian Linear Model3.4.3 Maximum Likelihood Estimator for DC Power Flow Estimation3.4.4 Bayesian-based Linear Estimator for DC Power Flow3.4.5 Recursive Bayesian-based DC power ow Estimation Approach for DC PowerFlow Estimation3.5 Error Detection Using Sparse Vector Recovery3.5.1 Sparse Vector Recovery3.5.2 Proposed Sparsity-based DC Power Flow Estimation3.5.3 Case Study and Discussion 4 Bad Data Detection4.1 Preliminaries on Falsification Detection Algorithms4.1.1 Related Work4.2 Time-Series Modeling of Load Power4.2.1 Outline of the Proposed Methodology4.2.2 Seasonality4.2.3 Fitting the AR and MA Models4.2.4 Forecast Validation Using Aikaike/Bayesian Information Criteria4.3 Case Study4.3.1 Stabilizing the Variance4.3.2 Fitting the Stationary Signal to a Model with Autoregressive and Moving-Average Elements4.3.3 Model Fine-Tuning and Evaluation4.4 Summary and Conclusion II Information Network Security5 Cloud Network Data Security5.1 Introduction5.2 Data Security Protection in Cloud-connected Smart Grids5.2.1 Simulation Scheme5.2.2 Simulation Results5.3 Summary and Outlook III Privacy Preservation6 End-User Data Privacy6.1 Introduction6.2 Preliminaries to Privacy Preservation Methods6.2.1 k-Anonymity Cloaking6.2.2 Location Obfuscation6.2.3 Preliminary Definitions6.3 Privacy Preservation: Location Obfuscation Methods6.4 Summary and Conclusion 7 Mobile User Data Privacy7.1 Introduction7.2 Preliminaries on Mobile Nodes Trajectory Privacy7.3 Privacy Preservation Quantification: Probabilistic Model7.4 A Vernoi-based Location Obfuscation Method7.4.1 A Stochastic Model of the Node Movement7.4.2 Proposed Scheme for A Mobile Node7.4.3 Computing the Instantaneous Privacy Level7.4.4 Concealing the Movement Path7.5 Summary and Conclusion
1 Overview of the Security and Privacy Issues in Smart Grids1.1 Security Issues in Smart Grid1.2 Physical Network Security1.3 Information Network Security1.4 Privacy Issues in Smart Grids1.5 Book Structure and Outlook I Physical Network Security 2 Reliability in Smart Grids2.1 Introduction2.2 Preliminaries on Reliability Quantification2.3 System Adequacy Quantification2.4 Congestion Prevention: An Economic Dispatch Algorithm2.4.1 9-bus Test Network2.4.2 IEEE 30-Bus Test Network2.5 Summary and Conclusion 3 Error Detection of DC Power Flow using State Estimation3.1 Introduction3.2 Preliminaries of the DC Power Flow and State Estimation3.2.1 Introduction to State Estimation3.3 Minimum-Variance Unbiased Estimator (MVUE)3.3.1 Measurement Error Representation in the Linear DC Power Flow Equation3.3.2 Linear Model3.3.3 Generalized Linear Model for State Estimation3.4 Bayesian-based LMMSE Estimator for DC Power Flow Estimation3.4.1 Linear Model3.4.2 Bayesian Linear Model3.4.3 Maximum Likelihood Estimator for DC Power Flow Estimation3.4.4 Bayesian-based Linear Estimator for DC Power Flow3.4.5 Recursive Bayesian-based DC power ow Estimation Approach for DC PowerFlow Estimation3.5 Error Detection Using Sparse Vector Recovery3.5.1 Sparse Vector Recovery3.5.2 Proposed Sparsity-based DC Power Flow Estimation3.5.3 Case Study and Discussion 4 Bad Data Detection4.1 Preliminaries on Falsification Detection Algorithms4.1.1 Related Work4.2 Time-Series Modeling of Load Power4.2.1 Outline of the Proposed Methodology4.2.2 Seasonality4.2.3 Fitting the AR and MA Models4.2.4 Forecast Validation Using Aikaike/Bayesian Information Criteria4.3 Case Study4.3.1 Stabilizing the Variance4.3.2 Fitting the Stationary Signal to a Model with Autoregressive and Moving-Average Elements4.3.3 Model Fine-Tuning and Evaluation4.4 Summary and Conclusion II Information Network Security5 Cloud Network Data Security5.1 Introduction5.2 Data Security Protection in Cloud-connected Smart Grids5.2.1 Simulation Scheme5.2.2 Simulation Results5.3 Summary and Outlook III Privacy Preservation6 End-User Data Privacy6.1 Introduction6.2 Preliminaries to Privacy Preservation Methods6.2.1 k-Anonymity Cloaking6.2.2 Location Obfuscation6.2.3 Preliminary Definitions6.3 Privacy Preservation: Location Obfuscation Methods6.4 Summary and Conclusion 7 Mobile User Data Privacy7.1 Introduction7.2 Preliminaries on Mobile Nodes Trajectory Privacy7.3 Privacy Preservation Quantification: Probabilistic Model7.4 A Vernoi-based Location Obfuscation Method7.4.1 A Stochastic Model of the Node Movement7.4.2 Proposed Scheme for A Mobile Node7.4.3 Computing the Instantaneous Privacy Level7.4.4 Concealing the Movement Path7.5 Summary and Conclusion
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