Authors:
Alvaro Gomes
;
C. Henggeler Antunes
;
A. Gomes Martins
and
João Melo
Affiliation:
DEEC – FCT University of Coimbra, Portugal
Keyword(s):
Evolutionary multi-objective optimization, Genetic algorithms, Algorithms assessment, Surface attainment.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Evolutionary Multiobjective Optimization
;
Genetic Algorithms
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Soft Computing
Abstract:
This paper presents a comparative analysis of the results obtained with two different genetic algorithms, NSGA-II and SPEA-II, in the framework of load management activities in electric power systems. The multiobjective problem deals with the identification and the selection of suitable control strategies to be applied to groups of electric loads aimed at reducing maximum power demand at the sub-station level, maximizing profits with selling of electricity and minimizing the discomfort caused to the end-users. The comparative analysis of the algorithms’ performance is done based on the attainment surface approach. Besides, it is shown that this approach can be used as a vehicle to introduce the decision maker’s preferences in the evaluation process.