A T-cell algorithm for solving dynamic economic power dispatch problems

espanolEste articulo presenta el sistema inmune artificial IA_DED (Immune Algorithm Dynamic Economic Dispatch) para resolver el problema de despacho de energaa economico dinamico (DED) y el problema de despacho de energia economico dinamico que tiene en cuenta la emision de gases (DEED). Nuestro enfoque considera estos problemas como problemas dinamicos cuyas restricciones cambian con el tiempo. IA\DED esta inspirado en el proceso de activacion que sufren las celulas T del sistema inmune para encontrar soluciones parciales. El enfoque propuesto se valida utilizando varios problemas de DED tomados de literatura especializada y un problema DEED. El ultimo se aborda transformando un problema multi-objetivo en un problema de un solo objetivo mediante el uso de una funcion agregativa lineal que combina los valores ponderados de dos objetivos en un solo valor escalar. Nuestros resultados se comparan con respecto a los obtenidos por otros enfoques tomados de la literatura especializada. Tambien proporcionamos un analisis estadistico para determinar la sensibilidad del desempeno de nuestro enfoque a sus parametros. Parte de este trabajo fue presentado en el XXV Congreso Argentino de Inform\'atica (CACIC), 2019. EnglishThis paper presents the artificial immune system IA_DED (Immune Algorithm Dynamic Economic Dispatch) to solve the Dynamic Economic Dispatch (DED) problem and the Dynamic Economic Emission Dispatch (DEED) problem. Our approach considers these as dynamic problems whose constraints change over time. IA\DED is inspired on the activation process that T cells suffer in order to find partial solutions. The proposed approach is validated using several DED problems taken from specialized literature and one DEED problem. The latter is addressed by transforming a multi-objective problem into a single-objective problem by using a linear aggregating function that combines the (weighted) values of the objectives into a single scalar value. Our results are compared with respect to those obtained by other approaches taken from the specialized literature. We also provide some statistical analysis in order to determine the sensitivity of the performance of our proposed approach to its parameters. Part of this work was presented at the XXV Argentine Congress of Computer Science (CACIC), 2019.