A hybrid Bacterial Foraging Optimization Algorithm and sequential quadratic programming method for dynamic economic dispatch considering the valve-point effects

Dynamic economic dispatch deals with the scheduling of online generator outputs with predicted load demands over a certain period of time so that an electric power system operates most economically. This paper proposes a hybrid methodology integrating Bacterial Foraging Optimization Algorithm (BFOA) with sequential quadratic programming (SQP) for solving dynamic economic dispatch problem of generating units considering valve-point effects. This hybrid method incorporates Bacterial Foraging Optimization Algorithm 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.