Abstract:
In this paper, a novel multiobjective genetic algorithm approach for economic
emission load dispatch (EELD) optimization problem is presented. The EELD problem
is formulated as a nonlinear constrained multiobjective optimization problem with both
equality and inequality constraints. A new multiobjective genetic algorithm based
approach employs the concept of co-evolution and repair algorithm for handling
nonlinear constraints. The algorithm maintains a finite-sized archive of non-dominated
solutions which gets iteratively updated in the presence of new solutions based on the
concept of e -dominance. The use of e -dominance also makes the algorithms practical
by allowing a decision maker to control the resolution of the Pareto set approximation.
TOPSIS method is employed to extract the best compromise solution from a finite
set of alternatives based upon simultaneous minimization of distance from an ideal point
(IP) and maximization of distance from a nadir point (NP). Several optimization runs of
the proposed approach are carried out on the standard IEEE 30-bus 6-genrator test
system. Simulation results with the proposed approach have been compared to those
reported in the literature. The comparison demonstrates the superiority of the proposed
approach and confirms its potential to solve the multiobjective EELD problem.