Parallel micro genetic algorithm based on merit order loading solutions for constrained dynamic economic dispatch

Abstract

Abstract This paper proposes a parallel micro genetic algorithm based on merit order loading solutions (PMGA-MOL) to solve constrained dynamic economic dispatch (DED) problems for combined cycle (CC) units with linear decreasing and decreasing staircase incremental cost (IC) functions. To minimize the synchronization overheads, the PMGA-MOL employs the load balancing and migration strategies among processors. This PMGA-MOL algorithm is implemented on the eight-processor scalable multicomputer implementation using low-cost equipment ( SMILE ) Beowulf cluster with a fast ethernet switch network on the generating unit system size in the range of 5–80 units over the entire dispatch periods. With different migration strategies, the proposed PMGA-MOL compromises the solution quality and speedup upper bounds for the best performance. PMGA-MOL is shown to be viable to the on-line implementation of constrained DED due to substantial generator fuel cost savings and high speedup upper bounds.