Economic-Emission Load Dispatch by Refined Particle Swarm Optimization and Interactive Bi-Objective Programming

摘要

This paper presents a novel solution approach that combines a refined particle swarm optimization (RPSO) with an interactive bi-objective programming (IBOP) to solve the economic-emission load dispatch (EELD) problem. In this work, two conflicting objective functions, fuel cost and emission, are considered concurrently in the optimization. The RPSO technique is developed to obtain a Pareto-optimal solution. The RPSO technique combines a basic PSO with novel binary encoding/decoding algorithms, which can prevent infeasible solutions, and a mutation operation, which can speed up convergence and escape local optima. The IBOP is then adopted to seek a best compromise solution by following the compromising intention of the decision maker. Moreover, the nonlinear characteristics of generation units and their operation constraints are all considered in this work for practical applications. The attractive properties of the proposed approach in both the solution quality and computational efficiency are demonstrated by three test systems and compared to those of existing methods. Experimental results reveal that the proposed approach can find the most suitable solution and exhibit robust convergence behavior.