Towards sparse matrix operations:graph database approach for power grid computation

作     者：Daoxing Li Kai Xiao Xiaohui Wang Pengtian Guo Yong Chen Daoxing Li;Kai Xiao;Xiaohui Wang;Pengtian Guo;Yong Chen

作者机构：China Electric Power Research Institute Co.Ltd.Beijing 100192P.R.China 

出 版 物：《Global Energy Interconnection》 (全球能源互联网（英文版）)

年 卷 期：2023年第6卷第1期

页      面：50-63页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 080802[工学-电力系统及其自动化] 08[工学] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the National Key R&D Program of China(2020YFB0905900). 

主　　题：Graph database Graph description Matrix Parallel computing Power flow 

摘      要：The construction of new power systems presents higher requirements for the Power Internet of Things(PIoT)technology.The“source-grid-load-storagearchitecture of a new power system requires PIoT to have a stronger multi-source heterogeneous data fusion ability.Native graph databases have great advantages in dealing with multi-source heterogeneous data,which make them suitable for an increasing number of analytical computing tasks.However,only few existing graph database products have native support for matrix operation-related interfaces or functions,resulting in low efficiency when handling matrix calculations that are commonly encountered in power grids.In this paper,the matrix computation process is expressed by a strategy called graph description,which relies on the natural connection between the matrix and structure of the graph.Based on that,we implement matrix operations on graph database,including matrix multiplication,matrix decomposition,etc.Specifically,only the nodes relevant to the computation and their neighbors are concerned in the process,which prunes the influence of zero elements in the matrix and avoids useless iterations compared to the conventional matrix computation.Based on the graph description,a series of power grid computations can be implemented on graph database,which reduces redundant data import and export operations while leveraging the parallel computing capability of graph database.It promotes the efficiency of PIoT when handling multi-source heterogeneous data.An comprehensive experimental study over two different scale power system datasets compares the proposed method with Python and MATLAB baselines.The results reveal the superior performance of our proposed method in both power flow and N-1 contingency computations.