DOI:https://doi.org/10.1007/s40565-018-0406-4 |
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Cloud-based parallel power flow calculation using resilient distributed datasets and directed acyclic graph |
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Net amount: 770 |
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Author:
Dewen WANG1, Fangfang ZHOU1, Jiangman LI1
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Author Affiliation:
School of Control and Computer Engineering, North China Electric Power University, Baoding 071003, China
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Foundation: |
This work was supported by National Natural Science Foundation of China (No. 51677072). |
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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) |
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Online Time:2019/01/28 |
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