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

• 作者: Dewen WANG Fangfang ZHOU Jiangman LI
• 来源期刊: 现代电力系统与清洁能源学报(英文)
• 学科: 工学
• 关键词: Power flow calculation PARALLEL programming MODEL DISTRIBUTED memory-shared MODEL Resilient DISTRIBUTED datasets(RDDs) Directed ACYCLIC graph(DAG)
• 年，卷（期）: 2019,（1）
• 页码范围: 65-77
• 页数: 13页
• 分类号: TM744

摘要

With the integration of distributed generation and the construction of cross-regional long-distance power grids, power systems become larger and more complex.They require faster computing speed and better scalability for power flow calculations to support unit dispatch.Based 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 model.This paper constructs and simulates a power flow calculation on a large-scale power system based on standard IEEE test data.Experiments are conducted on Spark cluster which is deployed as a cloud computing platform.They 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 calculations.In addition, running time will be reduced when adding cluster nodes.Although not tested under practical conditions, this paper provides a new way of thinking about parallel power flow calculations in large-scale power systems.