Evolutionary optimization of electric power distribution using the dandelion code

Jorge Sabattin, Carlos Contreras Bolton, Miguel Arias, Víctor Parada

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Planning primary electric power distribution involves solving an optimization problem using nonlinear components, which makes it difficult to obtain the optimum solution when the problem has dimensions that are found in reality, in terms of both the installation cost and the power loss cost. To tackle this problem, heuristic methods have been used, but even when sacrificing quality, finding the optimum solution still represents a computational challenge. In this paper, we study this problem using genetic algorithms. With the help of a coding scheme based on the dandelion code, these genetic algorithms allow larger instances of the problem to be solved. With the stated approach, we have solved instances of up to 40,000 consumer nodes when considering 20 substations; the total cost deviates 3.1% with respect to a lower bound that considers only the construction costs of the network.

Original languageEnglish
Article number738409
JournalJournal of Electrical and Computer Engineering
DOIs
Publication statusPublished - 21 Mar 2012

ASJC Scopus subject areas

  • Signal Processing
  • Computer Science(all)
  • Electrical and Electronic Engineering

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