Parallel tabu search algorithm for constrained economic dispatch

A parallel tabu search (PTS) algorithm for solving ramp rate constrained economic dispatch (CED) problems for generating units with non-monotonically and monotonically increasing incremental cost (IC) functions is proposed. To parallelise tabu search (TS) algorithms efficiently, the neighbourhood decomposition is used to balance the computing load, whereas competitive selection is used to update the best solution reached among subneighbourhoods. The proposed PTS is implemented on a 32-processor Beowulf cluster with an Ethernet switching network on a generating unit system size in the range 10-80 units over the entire dispatch periods. With different subneighbourhood sizes, the proposed PTS compromises the experimental speedup and solution quality for the best performance. PTS is potentially viable for the online implementation of CED because of the substantial generator fuel cost savings and high speedup upper bounds.