Electric Distribution Network Expansion Under Load-Evolution Uncertainty Using an Immune System Inspired Algorithm

Eduardo G. Carrano, Frederico G. Guimaraes, Ricardo H. C. Takahashi, Oriane M. Neto, Felipe Campelo

Research output: Contribution to journalArticle

Abstract

This paper addresses the problem of electric distribution network expansion under condition of uncertainty in the evolution of node loads in a time horizon. An immune-based evolutionary optimization algorithm is developed here, in order to find not only the optimal network, but also a set of suboptimal ones, for a given most probable scenario. A Monte-Carlo simulation of the future load conditions is performed, evaluating each such solution within a set of other possible scenarios. A dominance analysis is then performed in order to compare the candidate solutions, considering the objectives of: smaller infeasibility rate, smaller nominal cost, smaller mean cost and smaller fault cost. The design outcome is a network that has a satisfactory behavior under the considered scenarios. Simulation results show that the proposed approach leads to resulting networks that can be rather different from the networks that would be found via a conventional design procedure: reaching more robust performances under load evolution uncertainties.
Original languageEnglish
Pages (from-to)851 - 861
JournalIEEE Transactions on Power Systems
Volume22
Issue number2
DOIs
Publication statusPublished - May 2007

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