Adaptive and Dynamic Ant Colony Search Algorithm for Optimal Distribution Systems Reinforcement Strategy

Eleonora Riva Sanseverino, Salvatore Favuzza, Giorgio Graditi, Giorgio Graditi

Risultato della ricerca: Article

30 Citazioni (Scopus)

Abstract

The metaheuristic technique of Ant Colony Search has been revised here in order to deal with dynamic searchoptimization problems having a large search space and mixed integer variables. The problem to which it has been applied isan electrical distribution systems management problem. This kind of issues is indeed getting increasingly complicated dueto the introduction of new energy trading strategies, new environmental constraints and new technologies. In particular, inthis paper, the problem of finding the optimal reinforcement strategy to provide reliable and economic service to customersin a given time frame is investigated. Utilities indeed need efficient software tools to take decisions in this new complexscenario. In past times, utilities project the load growth for several years and then estimate when the capacity limit willbe exceeded. Designers then consider some feasible alternatives and select the optimal one in terms of performance andcosts. In this paper, the Distributed Generation, DG, technology considered in compound solutions with the installation offeeder and substations is viewed as a new option for solving distribution systems capacity problems, along several years. Theobjective to be minimized is therefore the overall cost of distribution systems reinforcement strategy in a given timeframe.An application on a medium size network is carried out using the proposed technique that allows the identification of optimalpaths in extremely large or non-finite spaces. The proposed algorithm uses an adaptive parameter in order to push explorationor exploitation as the search procedure stops in a local minimum. The algorithm allows the easy investigation of these kindsof complex problems, and allows to make useful comparisons as the intervention strategy and type of DG sources vary.
Lingua originaleEnglish
pagine (da-a)31-42
Numero di pagine12
RivistaApplied Intelligence
Volume24
Stato di pubblicazionePublished - 2006

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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