The application of stochastic methods in engineering research and optimization has been increasing over the past few decades. Ant Colony Optimization, in particular, has been attracting growing attention as a promising approach both in discrete and continuous domains. The present work proposes a multi-objective Ant Colony Optimization for continuous domains showing good convergence properties and uniform coverage of the non-dominated front. These properties have been proved both with mathematical test functions and with a complex real world problem. Besides the second part of the chapter presents the application of the new algorithm to the problem of optimal dispatch of dispersed power generation units in modern electrical distribution networks. The issue is intrinsically multi-objective and the objectives are calculated based on the solution of the power load flow problem. The performances of the algorithm have been compared to those of the Non-dominated Sorting Genetic Algorithm II on all applications. The chapter is organized as follows, in the introductory part, the relevance of multi-objective optimization problems to modern power distribution operation is outlined. Then the Non-dominated Sorting Genetic Algorithm II is described as well as the proposed Multi-objective Ant Colony Optimization algorithm in details. Both approaches are compared on a test suite of mathematical test functions. Finally, an interesting case study in the field of modern electrical distribution systems management is proposed.

RIVA SANSEVERINO, E., FILECCIA SCIMEMI, G., & Zizzo, G. (2010). A new meta-heuristic multi-objective approach for optimal dispatch of dispersed and renewable generating units in power distribution systems. In Prof. J. Jozefczyk, & Dr. D. Orski (a cura di), Knowledge-Based Intelligent System Advancements: Systemic and Cybernetic Approaches (pp. 162-181). IGI global.

A new meta-heuristic multi-objective approach for optimal dispatch of dispersed and renewable generating units in power distribution systems

RIVA SANSEVERINO, Eleonora;FILECCIA SCIMEMI, Giuseppe;ZIZZO, Gaetano
2010

Abstract

The application of stochastic methods in engineering research and optimization has been increasing over the past few decades. Ant Colony Optimization, in particular, has been attracting growing attention as a promising approach both in discrete and continuous domains. The present work proposes a multi-objective Ant Colony Optimization for continuous domains showing good convergence properties and uniform coverage of the non-dominated front. These properties have been proved both with mathematical test functions and with a complex real world problem. Besides the second part of the chapter presents the application of the new algorithm to the problem of optimal dispatch of dispersed power generation units in modern electrical distribution networks. The issue is intrinsically multi-objective and the objectives are calculated based on the solution of the power load flow problem. The performances of the algorithm have been compared to those of the Non-dominated Sorting Genetic Algorithm II on all applications. The chapter is organized as follows, in the introductory part, the relevance of multi-objective optimization problems to modern power distribution operation is outlined. Then the Non-dominated Sorting Genetic Algorithm II is described as well as the proposed Multi-objective Ant Colony Optimization algorithm in details. Both approaches are compared on a test suite of mathematical test functions. Finally, an interesting case study in the field of modern electrical distribution systems management is proposed.
Settore ING-IND/33 - Sistemi Elettrici Per L'Energia
RIVA SANSEVERINO, E., FILECCIA SCIMEMI, G., & Zizzo, G. (2010). A new meta-heuristic multi-objective approach for optimal dispatch of dispersed and renewable generating units in power distribution systems. In Prof. J. Jozefczyk, & Dr. D. Orski (a cura di), Knowledge-Based Intelligent System Advancements: Systemic and Cybernetic Approaches (pp. 162-181). IGI global.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/10447/51754
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