Renchang Dai, Guangyi Liu

Graph Database and Graph Computing for Power System Analysis

• Gebundenes Buch

Produktbeschreibung

Graph Database and Graph Computing for Power System Analysis

Understand a new way to model power systems with this comprehensive and practical guide

Graph databases have become one of the essential tools for managing large data systems. Their structure improves over traditional table-based relational databases in that it reconciles more closely to the inherent physics of a power system, enabling it to model the components and the network of a power system in an organic way. The authors' pioneering research has demonstrated the effectiveness and the potential of graph data management and graph computing to transform power system analysis.

Graph Database and Graph Computing for Power System Analysis presents a comprehensive and accessible introduction to this research and its emerging applications. Programs and applications conventionally modeled for traditional relational databases are reconceived here to incorporate graph computing. The result is a detailed guide which demonstrates the utility and flexibility of this cutting-edge technology.

The book's readers will also find:
_ Design configurations for a graph-based program to solve linear equations, differential equations, optimization problems, and more
_ Detailed demonstrations of graph-based topology analysis, state estimation, power flow analysis, security-constrained economic dispatch, automatic generation control, small-signal stability, transient stability, and other concepts, analysis, and applications
_ An authorial team with decades of experience in software design and power systems analysis

Graph Database and Graph Computing for Power System Analysis is essential for researchers and academics in power systems analysis and energy-related fields, as well as for advanced graduate students looking to understand this particular set of technologies.

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Produktdetails

• IEEE Press Series on Power Engineering
• Verlag: Wiley & Sons / Wiley-IEEE Press
• Artikelnr. des Verlages: 1W119903860
• 1. Auflage
• Seitenzahl: 512
• Erscheinungstermin: 17. Oktober 2023
• Englisch
• Abmessung: 260mm x 183mm x 32mm
• Gewicht: 666g
• ISBN-13: 9781119903864
• ISBN-10: 1119903866
• Artikelnr.: 67264807

Autorenporträt

Renchang Dai, PhD, is a Consulting Analyst and Project Manager for Puget Sound Energy, Washington, USA. He is a founding member of GE Energy Consluting Smart Grid CoE and an IEEE Senior Member, and has worked and published extensively on graph based power system analysis software. Guangyi Liu, PhD, is Chief Scientist and Smart Grid CoE at Envision Digital, USA. He is an IEEE Senior member and has extensive experience developing software for graph-based power system analysis across numerous applications.

Inhaltsangabe

About the Authors xiii

Preface xv

Acknowledgments xvii

Part I Theory and Approaches 1

1 Introduction 3

1.1 Power System Analysis 6

1.1.1 Power Flow Calculation 6

1.1.2 State Estimation 6

1.1.3 Contingency Analysis 7

1.1.4 Security-Constrained Automatic Generation Control 7

1.1.5 Security-Constrained ED 8

1.1.6 Electromechanical Transient Simulation 9

1.1.7 Photovoltaic Power Generation Forecast 10

1.2 Mathematical Model 10

1.2.1 Direct Methods of Solving Large-Scale Linear Equations 10

1.2.2 Iterative Methods of Solving Large-Scale Linear Equations 11

1.2.3 High-Dimensional Differential Equations 11

1.2.4 Mixed Integer-Programming Problems 11

1.3 Graph Computing 12

1.3.1 Graph Modeling Basics 13

1.3.2 Graph Parallel Computing 14

References 14

2 Graph Database 17

2.1 Database Management Systems History 17

2.2 Graph Database Theory and Method 18

2.2.1 Graph Database Principle and Concept 18

2.2.1.1 Defining a Graph Schema 19

2.2.1.2 Creating a Loading Job 20

2.2.1.3 Graph Query Language 21

2.2.2 System Architecture 25

2.2.3 Graph Computing Platform 25

2.3 Graph Database Operations and Performance 26

2.3.1 Graph Database Management System 26

2.3.1.1 Parallel Processing by MapReduce 27

2.3.1.2 Graph Partition 29

2.3.2 Graph Database Performance 35

References 38

3 Graph Parallel Computing 41

3.1 Graph Parallel Computing Mechanism 41

3.2 Graph Nodal Parallel Computing 44

3.3 Graph Hierarchical Parallel Computing 46

3.3.1 Symbolic Factorization 47

3.3.2 Elimination Tree 51

3.3.3 Node Partition 56

3.3.4 Numerical Factorization 57

3.3.5 Forward and Backward Substitution 58

References 59

4 Large-Scale Algebraic Equations 61

4.1 Iterative Methods of Solving Nonlinear Equations 61

4.1.1 Gauss-Seidel Method 61

4.1.2 PageRank Algorithm 62

4.1.2.1 PageRank Algorithm Mechanism 63

4.1.2.2 Iterative Method 66

4.1.2.3 Algebraic Method 67

4.1.2.4 Convergence Analysis 69

4.1.3 Newton-Raphson Method 72

4.2 Direct Methods of Solving Linear Equations 75

4.2.1 Introduction 75

4.2.2 Basic Concepts 76

4.2.2.1 Data Structures of Sparse Matrix 76

4.2.2.2 Matrices and Graphs 78

4.2.3 Historical Development 80

4.2.4 Direct Methods 81

4.2.4.1 Solving Triangular Systems 81

4.2.4.2 Symbolic Factorization 82

4.2.4.3 Fill-Reducing Ordering 82

4.3 Indirect Methods of Solving Linear Equations 83

4.3.1 Stationary Methods 83

4.3.1.1 Jacobi Method 83

4.3.1.2 Gauss-Seidel Method 85

4.3.1.3 SOR Method 86

4.3.1.4 SSOR Method 86

4.3.2 Nonstationary Methods 88

4.3.2.1 CG Method 88

4.3.2.2 Gmres 89

4.3.2.3 BCG (bi-CG) 90

References 91

5 High-Dimensional Differential Equations 95

5.1 Integration Methods 95

5.1.1 An Overview of Integration Methods and their Accuracy 95

5.1.1.1 One-Step Methods 96

5.1.1.2 Linear Multistep Methods 99

5.1.2 Integration Methods for Power System Transient Simulations 100

5.1.3 Transient Analysis Accuracy 100

5.1.4 Transient Analysis Stability 101

5.1.4.1 Absolute