DOI:https://doi.org/10.1007/s40565-018-0406-4 Cloud-based parallel power flow calculation using resilient distributed datasets and directed acyclic graph Page view: 0        Net amount: 104 Author: Dewen WANG1, Fangfang ZHOU1, Jiangman LI1 Author Affiliation: School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China Foundation: This work was supported by National Natural Science Foundation of China (No. 51677072). Abstract: 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. Keywords: Power flow calculation, Parallel programming model, Distributed memory-shared model, Resilient distributed datasets (RDDs), Directed acyclic graph (DAG)               Online Time:2019/01/28 View Full Text       Download reader