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.

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.

Preface

Acknowledgements

Section I: 

Chapter 1: Introduction

Chapter 2: Graph Database

Chapter 3:  Graph Parallel Computing

Chapter 4:  Large-Scale Algebraic Equations

Chapter 5: High Dimensional Differential Equations

Chapter 6:  Optimization Problems

Chapter 7:  Graph-based Machine Learning

Section II: 

Chapter 8:  Power Systems Modeling

Chapter 9: State Estimation Graph Computing

Chapter 10:  Power Flow Graph Computing

Chapter 11:  Contingency Analysis Graph Computing

Chapter 12:  Economic Dispatch and Unit Commitment

Chapter 13:  Automatic Generation Control

Chapter 14:  Small-signal Stability

Chapter 15:  Transient Stability

Chapter 16: Graph-based Deep Reinforcement Learning on Overload Control

Chapter 17:  Conclusions

Appendix

Index

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