Emission-Economic Dispatch using a Novel Constraint Handling Particle Swarm Optimization Strategy

Abstract

This paper presents a particle swarm optimization (PSO) algorithm to solve an economic-emission dispatch problem (EED) which gained recent attention due to the deregulation of the power industry and strict environmental regulations. The problem treats four objective functions, namely economic cost, CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> , SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> , and NO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> emission functions. It is formulated as a highly nonlinear constrained multiobjective optimization problem with conflicting objectives. The inequality constraints considered are the generating unit capacity limits while the equality constraint is generation-demand balance with network losses included. A novel equality constraint handling mechanism is proposed in this paper. PSO algorithm is tested on a 30-bus standard test system. Results obtained show that PSO algorithm has a great potential in handling multiobjective optimization problems. PSO was able to find the Pareto optimal solution set for each pair of objective functions. The overall problem is solved based on the decision maker utility function that assigns proper weights for each objective based on its importance. Comparison results signify the effectiveness and robustness of PSO as a promising optimization tool