Application of multi-objective teaching-learning-based algorithm to an economic load dispatch problem with incommensurable objectives

Authors:
Bijaya Ketan Panigrahi
Affiliations:
Department of Electrical Engineering, Indian Institute of Technology, Delhi, India
Venue:
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Year:
2011

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Abstract

In this paper, a multiobjective teaching-learning-based optimization algorithm with non-domination based sorting is applied to solve the environmental/economic dispatch (EED) problem containing the incommensurable objectives of best economic dispatch and least emission dispatch. The address of the environmental concerns that arise in the present day due to the operation of fossil fuel fired electric generators and global warming requires the transformation of the classical single objective economic load dispatch problem into multiobjective environmental/economic dispatch problem. In the work presented a test system of forty units is taken with fuel cost and emission as two conflicting objectives to be optimized simultaneously. The mathematical model used considers practical upper and lower bounds applicable to the generators. The valve point loading of the generator is mimicked in the modeling to accommodate a more realistic system. The simulation result reveals that the proposed approach is a competitive one to the current existing methods for finding the best optimal pareto front of two conflicting objectives and has the better robustness.