Multi-Objective Economic–Environmental Dispatch Using Particle Swarm Optimization and Pareto Analysis

Economic–Environmental Dispatch (EED) is a key optimization problem in modern power systems that aims to minimize fuel cost and emission while satisfying operational constraints. This paper investigates the EED problem for a five-generator thermal power system using Particle Swarm Optimization (PSO). The fuel cost and emission of generating units are modelled using quadratic functions, and generator operating limits and power balance constraints are considered. The performance of the proposed PSO approach is compared with a conventional base optimization method under different dispatch scenarios. Six case studies are analyzed, including economic dispatch, emission dispatch, penalty-based optimization, emission-constrained dispatch, Pareto front generation, and load variation analysis. The results demonstrate that PSO provides slightly improved or comparable solutions in terms of generation cost and emission compared to the base method. Furthermore, the Pareto front analysis illustrates the trade-off between economic and environmental objectives, while load variation analysis confirms the robustness of PSO under different demand conditions. The study shows that PSO is an effective and reliable optimization technique for solving the Economic–Environmental Dispatch problem.