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

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 bi-level PageRank algorithm is developed from PageRank algorithm and Gauss-Seidel methodology realize high performance parallel computation. MP 10790 case, and its extensions, MP 10790*10 and MP 10790*100, 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's effectiveness in real world.

[1]  Yiting Zhao,et al.  Graph-based Preconditioning Conjugate Gradient Algorithm for "N-1" Contingency Analysis , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[2]  Vipin Kumar,et al.  A Parallel Algorithm for Multilevel Graph Partitioning and Sparse Matrix Ordering , 1998, J. Parallel Distributed Comput..

[3]  Marcin Zawada,et al.  An Application of GPU Parallel Computing to Power Flow Calculation in HVDC Networks , 2015, 2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing.

[4]  Xi Chen,et al.  Evolving Graph Based Power System EMS Real Time Analysis Framework , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[5]  Rupak Biswas,et al.  Graph partitioning and parallel computing , 2000, Parallel Computing.

[6]  W. F. Tinney,et al.  Sparse Vector Methods , 1985, IEEE Transactions on Power Apparatus and Systems.

[7]  Chen Yuan,et al.  Economic sizing of distributed energy resources for reliable community microgrids , 2017, 2017 IEEE Power & Energy Society General Meeting.

[8]  Garng M. Huang,et al.  CIM/E Oriented Graph Database Model Architecture and Parallel Network Topology Processing , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[9]  Xi Chen,et al.  Economic Power Capacity Design of Distributed Energy Resources for Reliable Community Microgrids , 2017 .

[10]  Xi Chen,et al.  Exploration of Graph Computing in Power System State Estimation , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[11]  Xue Li,et al.  GPU-based fast decoupled power flow with preconditioned iterative solver and inexact newton method , 2017 .

[12]  Chen Yuan,et al.  Co-Optimization Scheme for Distributed Energy Resource Planning in Community Microgrids , 2017, IEEE Transactions on Sustainable Energy.

[13]  Yixin Chen,et al.  A comparison of a graph database and a relational database: a data provenance perspective , 2010, ACM SE '10.

[14]  Daniel S. Kirschen,et al.  Look-Ahead Bidding Strategy for Energy Storage , 2017, IEEE Transactions on Sustainable Energy.

[15]  Tao Chen,et al.  Graph based Platform for Electricity Market Study, Education and Training , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[16]  Kevin Tomsovic,et al.  Security Constrained Multi-Stage Transmission Expansion Planning Considering a Continuously Variable Series Reactor , 2017, IEEE Transactions on Power Systems.

[17]  Zhiwei Wang,et al.  Graph Based Power Flow Calculation for Energy Management System , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[18]  A. T. Holen,et al.  Power system modelling and sparse matrix operations using object-oriented programming , 1994 .

[19]  Raed Alqadi,et al.  An Efficient Parallel Gauss-Seidel Algorithm for the Solution of Load Flow Problems , 2007, Int. Arab J. Inf. Technol..

[20]  Lingfeng Wang,et al.  Integration of Plug-in Hybrid Electric Vehicles into Residential Distribution Grid Based on Two-Layer Intelligent Optimization , 2014, IEEE Transactions on Smart Grid.