Adaptive and dynamic ant colony search algorithm for optimal distribution systems reinforcement strategy

S. Favuzza, G. Graditi, E. Riva Sanseverino

Research output: Contribution to journalArticle

30 Citations (Scopus)

Abstract

The metaheuristic technique of Ant Colony Search has been revised here in order to deal with dynamic search optimization problems having a large search space and mixed integer variables. The problem to which it has been applied is an electrical distribution systems management problem. This kind of issues is indeed getting increasingly complicated due to the introduction of new energy trading strategies, new environmental constraints and new technologies. In particular, in this paper, the problem of finding the optimal reinforcement strategy to provide reliable and economic service to customers in a given time frame is investigated. Utilities indeed need efficient software tools to take decisions in this new complex scenario. In past times, utilities project the load growth for several years and then estimate when the capacity limit will be exceeded. Designers then consider some feasible alternatives and select the optimal one in terms of performance and costs. In this paper, the Distributed Generation, DG, technology considered in compound solutions with the installation of feeder and substations is viewed as a new option for solving distribution systems capacity problems, along several years. The objective 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 optimal paths in extremely large or non-finite spaces. The proposed algorithm uses an adaptive parameter in order to push exploration or exploitation as the search procedure stops in a local minimum. The algorithm allows the easy investigation of these kinds of complex problems, and allows to make useful comparisons as the intervention strategy and type of DG sources vary. © 2006 Springer Science + Business Media, Inc.
Original languageEnglish
Pages (from-to)31 - 42
Number of pages12
JournalApplied Intelligence
Volume24
Issue number1
DOIs
Publication statusPublished - Feb 2006
Externally publishedYes

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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