Hybridization of bee colony optimization and sequential quadratic programming for dynamic economic dispatch

Dynamic economic dispatch deals with the scheduling of online generator outputs with predicted load demands over a certain period of time so as to operate an electric power system most economically. This paper proposes a hybrid methodology integrating bee colony optimization with sequential quadratic programming for solving dynamic economic dispatch problem of generating units considering valve-point effects. This hybrid method incorporates bee colony optimization as a base level search which can give a good direction to the optimal region and sequential quadratic programming as a local search procedure which is used to fine tune that region for achieving the final solution. Numerical results of a ten-unit system have been presented to demonstrate the performance and applicability of the proposed method. The results obtained from the proposed method are compared with those obtained from hybrid of particle swarm optimization and sequential quadratic programming and hybrid of evolutionary programming and sequential quadratic programming.

► Dynamic economic dispatch deals with the scheduling of online generator outputs with predicted load demands. ► This paper proposes a hybrid methodology integrating BCO with SQP for solving dynamic economic dispatch. ► This hybrid method incorporates BCO as a base level search and SQP as a local search procedure.