Feng Liu, Zhaojian Wang, Changhong Zhao

Merging Optimization and Control in Power Systems

Physical and Cyber Restrictions in Distributed Frequency Control and Beyond

• Gebundenes Buch

Produktbeschreibung

Merging Optimization and Control in Power Systems

A novel exploration of distributed control in power systems with insightful discussions of physical and cyber restrictions

In Merging Optimization and Control in Power Systems an accomplished team of engineers deliver a comprehensive introduction to distributed optimal control in power systems. The book re-imagines control design within the framework of cyber-physical systems with restrictions in both the physical and cyber spaces, addressing operational constraints, non-smooth objective functions, rapid power fluctuations caused by renewable generations, partial control coverage, communication delays, and non-identical sampling rates.

This book bridges the gap between optimization and control in two ways. First, optimization-based feedback control is explored. The authors describe feedback controllers which automatically drive system states asymptotically to specific, desired optimal working points. Second, the book discusses feedback-based optimization. Leveraging the philosophy of feedback control, the authors envision the online solving of complicated optimization and control problems of power systems to adapt to time-varying environments.

Readers will also find:
_ A thorough argument against the traditional and centralized hierarchy of power system control in favor of the merged approach described in the book
_ Comprehensive explorations of the fundamental changes gripping the power system today, including the increasing penetration of renewable and distributed generation, the proliferation of electric vehicles, and increases in load demand
_ Data, tables, illustrations, and case studies covering realistic power systems and experiments
_ In-depth examinations of physical and cyber restrictions, as well as the robustness and adaptability of the proposed model

Perfect for postgraduate students and researchers with the prerequisite knowledge of power system analysis, operation, and dynamics, convex optimization theory, and control theory, Merging Optimization and Control in Power Systems is an advanced and timely treatment of distributed optimal controller design.

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Produktdetails

• IEEE Press Series on Control Systems Theory and Applications
• Verlag: Wiley & Sons / Wiley-IEEE Press
• Artikelnr. des Verlages: 1W119827920
• 1. Auflage
• Seitenzahl: 432
• Erscheinungstermin: 23. August 2022
• Englisch
• Abmessung: 235mm x 157mm x 28mm
• Gewicht: 744g
• ISBN-13: 9781119827924
• ISBN-10: 1119827922
• Artikelnr.: 62221925

Autorenporträt

Feng Liu, PhD, is Associate Professor in the Department of Electrical Engineering at Tsinghua University in Beijing, China. Zhaojian Wang, PhD, is Assistant Professor in the Department of Automation at Shanghai Jiao Tong University in Shanghai, China. Changhong Zhao, PhD, is Assistant Professor in the Department of Information Engineering at the Chinese University of Hong Kong, Hong Kong SAR, China. Peng Yang is a PhD Candidate in the Department of Electrical Engineering at Tsinghua University in Beijing, China.

Inhaltsangabe

Foreword xv

Preface xvii

Acknowledgments xix

1 Introduction 1

1.1 Traditional Hierarchical Control Structure 2

1.1.1 Hierarchical Frequency Control 2

1.1.1.1 Primary Frequency Control 4

1.1.1.2 Secondary Frequency Control 5

1.1.1.3 Tertiary Frequency Control 5

1.1.2 Hierarchical Voltage Control 5

1.1.2.1 Primary Voltage Control 6

1.1.2.2 Secondary Voltage Control 7

1.1.2.3 Tertiary Voltage Control 7

1.2 Transitions and Challenges 7

1.3 Removing Central Coordinators: Distributed Coordination 8

1.3.1 Distributed Control 11

1.3.2 Distributed Optimization 12

1.4 Merging Optimization and Control 13

1.4.1 Optimization-Guided Control 14

1.4.2 Feedback-Based Optimization 16

1.5 Overview of the Book 17

Bibliography 19

2 Preliminaries 23

2.1 Norm 23

2.1.1 Vector Norm 23

2.1.2 Matrix Norm 24

2.2 Graph Theory 26

2.2.1 Basic Concepts 26

2.2.2 Laplacian Matrix 26

2.3 Convex Optimization 28

2.3.1 Convex Set 28

2.3.1.1 Basic Concepts 28

2.3.1.2 Cone 30

2.3.2 Convex Function 31

2.3.2.1 Basic Concepts 31

2.3.2.2 Jensen's Inequality 35

2.3.3 Convex Programming 35

2.3.4 Duality 36

2.3.5 Saddle Point 39

2.3.6 KKT Conditions 39

2.4 Projection Operator 41

2.4.1 Basic Concepts 41

2.4.2 Projection Operator 42

2.5 Stability Theory 44

2.5.1 Lyapunov Stability 44

2.5.2 Invariance Principle 46

2.5.3 Input-Output Stability 47

2.6 Passivity and Dissipativity Theory 49

2.6.1 Passivity 49

2.6.2 Dissipativity 51

2.7 Power Flow Model 52

2.7.1 Nonlinear Power Flow 53

2.7.1.1 Bus Injection Model (BIM) 53

2.7.1.2 Branch Flow Model (BFM) 54

2.7.2 Linear Power Flow 55

2.7.2.1 DC Power Flow 55

2.7.2.2 Linearized Branch Flow 56

2.8 Power System Dynamics 56

2.8.1 Synchronous Generator Model 57

2.8.2 Inverter Model 58

Bibliography 60

3 Bridging Control and Optimization in Distributed Optimal Frequency Control 63

3.1 Background 64

3.1.1 Motivation 64

3.1.2 Summary 66

3.1.3 Organization 67

3.2 Power System Model 67

3.2.1 Generator Buses 68

3.2.2 Load Buses 69

3.2.3 Branch Flows 70

3.2.4 Dynamic Network Model 72

3.3 Design and Stability of Primary Frequency Control 74

3.3.1 Optimal Load Control 74

3.3.2 Main Results 75

3.3.3 Implications 79

3.4 Convergence Analysis 79

3.5 Case Studies 88

3.5.1 Test System 88

3.5.2 Simulation Results 89

3.6 Conclusion and Notes 92

Bibliography 93

4 Physical Restrictions: Input Saturation in Secondary Frequency Control 97

4.1 Background 98

4.2 Power System Model 100

4.3 Control Design for Per-Node Power Balance 101

4.3.1 Control Goals 102

4.3.2 Decentralized Optimal Controller 103

4.3.3 Design Rationale 105

4.3.3.1 Primal-Dual Algorithms 105

4.3.3.2 Design of Controller (4.6) 105

4.4 Optimality and Uniqueness of Equilibrium 108

4.5 Stability Analysis 112

4.6 Case Studies 120

4.6.1 Test System 120

4.6.2 Simulation Results 122

4.6.2