Application of modified beetle antennae search algorithm and BP power flow prediction model on multi-objective optimal active power dispatch

Abstract

The multi-objective optimal active power dispatch (MOAPD), an important part to realize the high-efficient and economic operation of power systems, widely considers the reduction of power loss, fuel cost and emission. To successfully solve the nonlinear MOAPD problems with contradictory objectives, the modified hybrid beetle antennae search (MHBAS) algorithm with adaptively-adjusted step factor is proposed in this paper. The suggested MHBAS algorithm integrates the preliminary optimization and mutation-crossover mechanisms of multi-objective differential evolution (MDE) algorithm, and has better population diversity. Furthermore, the novel fuzzy-object sorting method (FOSM) is put forward to determine the feasible Pareto fronts (PFs) of MOAPD. Seven testing cases on IEEE 30, 57, and 118-bus systems certify that, the best compromise solution (BCS) achieved by the MHBAS algorithm with FOSM is more desirable than traditional MDE and NSGA-II algorithms. Besides, the great superiority of MHBAS algorithm on PF diversity and PF uniformity are quantitatively validated by the hypervolume (HV) and inverted generational distance (IGD) indexes. Even more importantly, a feasible BP power flow prediction model which takes the basic fuel cost as the fulcrum is put forward for the first time. The innovative MHBAS-BP method, the combination of BP prediction model and MHBAS algorithm, provides great convenience for exploring the higher quality dispatching schemes near the current BCS. These multiple alternative power flow solutions obtained by NMBAS-BP method can meet the various requirements of decision makers with less time cost. In general, the proposed MHBAS-BP provides a representative application of computer technologies in solving complex MOAPD problems.