Genetic Algorithm and Rough Sets Based Hybrid Approach for Economic Environmental Dispatch of Power Systems

In this paper we present a new optimization algorithm for Economic environmental dispatch EED of power systems. The purpose of EED problem is to compute the optimal generation for individual units of the power system by minimizing the fuel cost and emission levels simultaneously, subject to various equality and inequality constraints. The proposed algorithm is population based an evolutionary algorithm which operates in two phases: in the first one, genetic algorithm is implemented as search engine in order to generate approximate true Pareto front. This algorithm based on concept of co-evolution and repair algorithm for handling nonlinear constraints. Also it maintains a finite-sized archive of non-dominated solutions which gets iteratively updated in the presence of new solutions based on the concept of ε -dominance. Then, in the second phase, rough sets theory is adopted as local search engine in order to improve the spread of the solutions found so far. Optimization using multiobjective evolutionary algorithms yields not a single optimal solution. However, for practical applications, we need to select one solution which will satisfy the different goals to some extent. TOPSIS method has the ability to identify the best alternative from a finite set of alternatives. The proposed approach is carried out on the standard IEEE 30-bus 6-generator test system. The results demonstrate the capabilities of the proposed approach to generate true and well-distributed Pareto-optimal nondominated solutions of the multiobjective EED problem. Also the comparison with the exiting well-known algorithms demonstrates the superiority of the proposed approach and confirms its potential to solve the multiobjective EED problem.