A Big Data Scale Algorithm for Optimal Scheduling of Integrated Microgrids
Daniel Felix Ritchie School of Engineering and Computer Science
Microgrids, Optimal scheduling, Generators, Switches, Reliability, Power system reliability
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 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.
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Hung Khanh Nguyen, et al. “A Big Data Scale Algorithm for Optimal Scheduling of Integrated Microgrids.” IEEE Transactions on Smart Grid, vol. 9, no. 1, 2018, pp. 274–282. doi: 10.1109/tsg.2016.2550422.