Economic & Emission Constrained Load Dispatch Using Genetic Algorithm

A global optimization technique known as Genetic Algorithm (GA) has emerged as a candidate due to its flexibility and efficiency for many optimization applications. Genetic algorithm is a stochastic searching algorithm. It combines an artificial, i.e. the Darwinian survival of the fittest principle with genetic operation, abstracted from nature to form a robust mechanism that is very effective at finding optimal solutions to complex-real world problems. This dissertation presents a GA to solve the generation scheduling for Economic load Dispatch which gained recent attention due to the deregulation of the power industry and strict environmental regulations. The problem is formulated for three generators subjected to different constraints. The inequality constraints considered are the generating unit capacity limits while the equality constraint is generation-demand balance. A novel equality constraint handling mechanism is proposed in this dissertation. Results obtained show that genetic algorithm has a optimal solution set under different loading conditions.