Cloud-based parallel power flow calculation using resilient distributed datasets and directed acyclic graph

作     者：Dewen WANG Fangfang ZHOU Jiangman LI 

作者机构：School of Control and Computer Engineering North China Electric Power University 

出 版 物：《Journal of Modern Power Systems and Clean Energy》 (现代电力系统与清洁能源学报（英文）)

年 卷 期：2019年第7卷第1期

页      面：65-77页

核心收录：

学科分类：0830[工学-环境科学与工程（可授工学、理学、农学学位）] 0808[工学-电气工程] 080802[工学-电力系统及其自动化] 08[工学] 0807[工学-动力工程及工程热物理] 

基　　金：supported by National Natural Science Foundation of China (No.51677072) 

主　　题：Power flow calculation Parallel programming model Distributed memory-shared model Resilient distributed datasets(RDDs) Directed acyclic graph(DAG) 

摘      要：With the integration of distributed generation and the construction of cross-regional long-distance power grids, power systems become larger and more *** require faster computing speed and better scalability for power flow calculations to support unit *** on the analysis of a variety of parallelization methods, this paper deploys the large-scale power flow calculation task on a cloud computing platform using resilient distributed datasets(RDDs).It optimizes a directed acyclic graph that is stored in the RDDs to solve the low performance problem of the MapReduce *** paper constructs and simulates a power flow calculation on a large-scale power system based on standard IEEE test *** are conducted on Spark cluster which is deployed as a cloud computing *** show that the advantages of this method are not obvious at small scale, but the performance is superior to the stand-alone model and the MapReduce model for large-scale *** addition, running time will be reduced when adding cluster *** not tested under practical conditions, this paper provides a new way of thinking about parallel power flow calculations in large-scale power systems.