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
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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