Joint application of multi-object beetle antennae search algorithm and BAS-BP fuel cost forecast network on optimal active power dispatch problems

Abstract

The multi-object optimal active power dispatch (MOAPD) problem can realize the optimized operation of electric systems by reducing the fuel cost, power loss and exhaust emission. In this paper, the Pareto front (PF) and the best-compromise scheme (BCS) of MOAPD problems are determined by novel multi-object beetle antennae search algorithm (NMBAS). The proposed NMBAS algorithm, which innovatively integrates the advanced ideas of widely-used NSGA-Ⅱ and multi-goal PSO (MPSO) algorithms, achieves better BCS-quality, PF-uniformity and PF-diversity. More notably, further research indicates that several winning elite solutions (WES) with smaller goal values are distributed around the current BCS of NMBAS. Therefore, a brand new BAS-BP fuel cost forecast network with less prediction-error and time-cost is put forward to seek the potential WES schemes. With BP network as main body and modified by beetle antennae search (BAS) algorithm, the BAS-BP fuel cost forecast model achieves more than six higher-quality WES schemes which are superior to the traditional BCS solution. The superiorities of proposed NMBAS algorithm and BAS-BP fuel cost forecast network are intuitively demonstrated by dual-objective and triple-objective MOAPD experiments. In general, the joint application of NMBAS algorithm and BAS-BP forecast network successfully solves the non-differentiable power grid optimization problems.