Economic Dispatch with Environmental Considerations using Particle Swarm Optimization

This paper presents a particle swarm optimization (PSO) algorithm to solve an economic-emission dispatch problem (EED). This problem has been getting more attention recently due to the deregulation of the power industry and strict environmental regulations. It is formulated as a highly nonlinear constrained multiobjective optimization problem with conflicting objective functions. PSO algorithm is used to solve the formulated problem on two standard test systems, namely the 30-bus and 14-bus systems. Results obtained show that PSO algorithm outperformed most previously proposed algorithms used to solve the same EED problem. These algorithms included evolutionary algorithm, stochastic search technique, linear programming, and adaptive Hopfield neural network. PSO was able to find the Pareto optimal solution set for the multiobjective problem. In addition, PSO results were compared to LINGO software outcomes. Comparison results signify the effectiveness and robustness of PSO as a promising optimization tool for this specific problem