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A Big Data Scale Algorithm for Optimal Scheduling of Integrated Microgrids
یک الگوریتم مقیاس داده های بزرگ برای زمانبندی بهینه ریز شبکه های یکپارچه-2016 The capability of switching into the islanded operation mode of microgrids has been advocated as a viable solution
to achieve high system reliability. This paper proposes a new
model for the microgrids optimal scheduling and load curtailment
problem. The proposed problem determines the optimal schedule
for local generators of microgrids to minimize the generation cost
of the associated distribution system in the normal operation.
Moreover, when microgrids have to switch into the islanded
operation mode due to reliability considerations, the optimal
generation solution still guarantees for the minimal amount of
load curtailment. Due to the large number of constraints in both
normal and islanded operations, the formulated problem becomes
a large-scale optimization problem and is very challenging to
solve using the centralized computational method. Therefore,
we propose a decomposition algorithm using the alternating
direction method of multipliers (ADMM) that provides a parallel
computational framework. The simulation results demonstrate
the efficiency of our proposed model in reducing generation cost
as well as guaranteeing the reliable operation of microgrids in the
islanded mode. We finally describe the detailed implementation
of parallel computation for our proposed algorithm to run
on a computer cluster using the Hadoop MapReduce software
framework.
Index Terms: ADMM | big data | Hadoop | integrated microgrid | islanded operation | load curtailment | MapReduce |
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