Contents

pp v-xv

List of Contributors

pp xvi-xx

Preface

pp xxi-xxii

Part I - Statistical Learning

pp 1-2

1 - Learning Power Grid Topologies

pp 3-27

2 - Probabilistic Forecasting of Power System and Market Operations

pp 28-51

3 - Deep Learning in Power Systems

pp 52-73

4 - Estimating the System State and Network Model Errors

pp 74-98

Part II - Data-Driven Anomaly Detection

pp 99-100

5 - Quickest Detection and Isolation of Transmission Line Outages

pp 101-123

6 - Active Sensing for Quickest Anomaly Detection

pp 124-143

7 - Random Matrix Theory for Analyzing Spatio-Temporal Data

pp 144-174

8 - Graph-Theoretic Analysis of Power Grid Robustness

pp 175-194

Part III - Data Quality, Integrity, and Privacy

pp 195-196

9 - Data-Injection Attacks

pp 197-229

10 - Smart Meter Data Privacy

pp 230-260

11 - Data Quality and Privacy Enhancement

pp 261-282

Part IV - Signal Processing

pp 283-284

12 - A Data Analytics Perspective of Fundamental Power Grid Analysis Techniques

pp 285-311

13 - Graph Signal Processing for the Power Grid

pp 312-339

14 - A Sparse Representation Approach for Anomaly Identification

pp 340-360

Part V - Large-Scale Optimization

pp 361-362

15 - Uncertainty-Aware Power Systems Operation

pp 363-399

16 - Distributed Optimization for Power and Energy Systems

pp 400-430

17 - Distributed Load Management

pp 431-454

18 - Analytical Models for Emerging Energy Storage Applications

pp 455-480

Part VI - Game Theory

pp 481-482

19 - Distributed Power Consumption Scheduling

pp 483-503

20 - Electric Vehicles and Mean-Field

pp 504-523