Quantum-behaved Particle Swarm Optimization for Power Economic Dispatch Problem of Units with Multiple Fuel Option

Abstract

This paper presents a quantum-behaved particle swarm optimization (QPSO) with a multiple updating (MU) for solving the power economic dispatch problem (PEDP) of generators with multiple fuel options (MFOs). The QPSO assists the proposed method efficaciously find and precisely search. The MU helps the proposed method prevent deforming the augmented Lagrange function (ALF) and caused difficultly in searching optimal solution. The proposed approach combines the QPSO and the MU that has benefits of adopting a widespread area of punishment parameters and a small-size population. The proposed algorithm has been demonstrated on a practical ten generating units system; every one unit is composed of two or three fuel changes. The entire fuel price got by the proposed QPSO-MU has been competed with former studies for validating its efficacy. Compared achievements clearly express that the presented method is an effective alternative for resolving PEDP of units with MFOs in the realistic operations of power system.

A Brief Review on Mathematical Tools Applicable to Quantum Computing for Modelling and Optimization Problems in Engineering

“…In Chiang (2017) [95], the power economic dispatch problem of generators with multiple fuel options was using a method based on quantum-behaved particle swarm optimization with multiple updating, described as highly effective. This involves conducting a refined search and avoiding the deformations associated with the augmented Lagrange function (thanks to multiple updating) and thus ensuring convergence towards the optimal solution.…”