Exploration of Bi-Level PageRank Algorithm for Power Flow Analysis Using Graph Database

TL;DR

This paper investigates modeling power systems with graph databases and develops a bi-level PageRank algorithm combined with Gauss-Seidel method for efficient parallel power flow analysis, validated on multiple case studies.

Contribution

It introduces a novel GDB-based bi-level PageRank algorithm for power flow analysis, integrating parallel computation techniques and demonstrating effectiveness on large-scale systems.

Findings

Successful modeling of power systems using GDB.

Enhanced parallel computation performance.

Effective analysis on large real-world systems.

Abstract

Compared with traditional relational database, graph database, GDB, is a natural expression of most real-world systems. Each node in the GDB is not only a storage unit, but also a logic operation unit to implement local computation in parallel. This paper firstly explores the feasibility of power system modeling using GDB. Then a brief introduction of the PageRank algorithm and the feasibility analysis of its application in GDB are presented. Then the proposed GDB based bilevel PageRank algorithm is developed from PageRank algorithm and Gauss Seidel methodology realize high performance parallel computation. MP 10790 case, and its extensions, MP 107900 and MP 1079000, are tested to verify the proposed method and investigate its parallelism in GDB. Besides, a provincial system, FJ case which include 1425 buses and 1922 branches, is also included in the case study to further prove the proposed algorithm effectiveness in real world.