Multi-Objective Emission dispatch using Genetic algorithm

the Multiobjective problem formulation, four important non-commensurable objectives in an electrical thermal power system are considered.

An efficient and reliable evolutionary-based meta-heuristic approach, termed as swarm intelligence, is presented for the solution of optimal economic power dispatch. The generation of electricity from the fossil fuel releases several contaminants, such as sulfur oxides, nitrogen oxides and carbon dioxide, into the atmosphere. Genetic algorithmic approach to power system optimization, as reported here for a case of economic power dispatch, consists essentially of minimizing the objective function while gradually satisfying the constraint relations. The advantages of the genetic algorithmic approach in terms of problem reduction, flexibility and solution methodology are also discussed. In the Multiobjective problem formulation, four important non-commensurable objectives in an electrical thermal power system are considered. These are economy and environmental impacts because of NOx, S02, C02 emissions. The feasibility of the proposed method is compared with a simple genetic algorithm is described to multiobjective optimal economic dispatch of electrical power systems.