Algoritmos culturais para o problema do despacho de energia elétrica

Abstract

In this thesis, Artificial Immune Systems are applied to solve different instances of the economic and environmental/economic load dispatch problems. The immune systems considered here are based on the clonal selection principle and use a real coded representation with pure aging operator and hypermutation operators utilizing Gaussian and Cauchy distributions. Cultural Algorithms using normative, situational, historical and topographical knowledge sources are incorporated to improve the global optimization capability of immune systems. All the proposed approaches have several points of self-adaptation and most of them use a local search operator that is based on a quasi-simplex technique. A chaotic sequence is also considered as a potential source of improvement to the cultural variation. Repair procedures represent another contribution of this work and are applied to avoid dealing with infeasible solutions in all the considered problems. In the first part of the experiments, four instances of the economic load dispatch problem are considered. In all the cases, a non-smooth fuel cost function which takes into account the valve-point loading effects is utilized. One of instances also considers energy transmission losses. In the experiments conducted to compare the proposed approaches, the immune-cultural based approaches outperformed the pure immune version. The proposed cultural method which presents the best performance is chosen to be compared with other modern optimization techniques reported in the recent literature. In all the mono-objective cases considered, the proposed approach is capable of finding the minimum fuel cost value. The second part of the experiments deals with the environmental/economic load dispatch problem. This is a multi-objective version of the economic load dispatch where pollution emission is added as an objective, it is formulated as a non-linear constrained multi-objective optimization problem. Cultural immune algorithms based on scalarizing factors and Pareto-dominance are proposed for this case. Several instances of the problem are considered, some dealing with energy transmission losses. The proposed algorithms are favorably compared with a state-of-art algorithm for multi-objective optimization (the Non-dominated Sorting Genetic Algorithm II - NSGA - II). The best proposed algorithm is also compared with methods reported in recent literature. The comparisons demonstrate the good performance of the best proposed approach and confirm its potential to solve the environmental/economic load dispatch problem.