Optimal real power rescheduling of generators for congestion management using a novel ant lion optimiser

Abstract

Considering system uncertainties prevailing in the restructured power system, congestion management (CM) is a vital issue in power system operation and control. The objective of CM is to remove congestion in the lines while satisfying all the constraints with minimum congestion cost. This study proposes a generation rescheduling-based approach for CM in electricity market using a novel ant lion optimiser (ALO) algorithm. ALO is a recently developed the nature-inspired algorithm based on the hunting mechanism of antlions. The effectiveness of the proposed approach is tested on modified IEEE 30-bus, modified IEEE 57-bus and IEEE 118-bus test systems. The security constraints like load bus voltage and line loading impact are incorporated in this study. To prove the validity of the proposed technique, the obtained results are compared to the results offered by various recent optimisation algorithms. The results show that the proposed ALO algorithm outperforms the other comparative algorithms. The proposed approach uses less number of fitness function evaluations, not traps into local minima and offers promising convergence characteristic. The proposed approach will ease the system operator to remove the contingency rapidly for secured and reliable operation of the power system under deregulated environment.