Robust Design of Power Distribution Systems Using an Enhanced Multi-Objective Genetic Algorithm

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Author(s): Cristiane G. Taroco (Universidade Federal de Minas Gerais, Brazil), Eduardo G. Carrano (Centro Federal de Educação Tecnológica de Minas Gerais, Brazil)and Oriane M. Neto (Universidade Federal de Minas Gerais, Brazil)
Copyright: 2012
Pages: 22
Source title: Nature-Inspired Computing Design, Development, and Applications
Source Author(s)/Editor(s): Leandro Nunes de Castro (Mackenzie University, Brazil)
DOI: 10.4018/978-1-4666-1574-8.ch010

Keywords: Bioinformatics / Biologically-Inspired Computing / Medical Information Science Reference / Medicine & Healthcare

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Abstract

The growing importance of electric distribution systems justifies new investments in their expansion and evolution. It is well known in the literature that optimization techniques can provide better allocation of the financial resources available for such a task, reducing total installation costs and power losses. In this work, the NSGA-II algorithm is used for obtaining a set of efficient solutions with regard to three objective functions, that is cost, reliability, and robustness. Initially, a most likely load scenario is considered for simulation. Next, the performances of the solutions achieved by the NSGA-II are evaluated under different load scenarios, which are generated by means of Monte Carlo Simulations. A Multi-objective Sensitivity Analysis is performed for selecting the most robust solutions. Finally, those solutions are submitted to a local search algorithm to estimate a Pareto set composed of just robust solutions only.

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Robust Design of Power Distribution Systems Using an Enhanced Multi-Objective Genetic Algorithm

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