Multiobjective Transmission Expansion Planning considering Multiple Generation Scenarios

Carlos Adrian Correa, Ricardo Bolanos, Antonio H. Escobar Zuluaga

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper shows a methodology for solving the Transmission Expansion Planning (TEP) problem when Multiple Generation Scenarios (MGS) are considered. MGS are a result of the market based environment introduced by electricity deregulation. The solution to this problem is carried out by using multiobjective evolutionary strategies for the optimization process, implementing a new hybrid modified NSGA-II/Chu-Beasley algorithm. The proposed methodology is validated using the 6-bus Garver system and the IEEE-24 bus system. The TEP is based on the DC model of the network and non-linear interior point method is used to initialize the population. A set of Pareto optimal expansion plans with different levels of cost and load shedding is found for each system, showing the robustness of the proposed approach.

Original languageEnglish
Title of host publication2014 IEEE PES Transmission and Distribution Conference and Exposition, PES T and D-LA 2014 - Conference Proceedings
EditorsPaola Beltran, Andres Aldana
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479962501
DOIs
StatePublished - 10 Nov 2014
Externally publishedYes
Event2014 IEEE PES Transmission and Distribution Conference and Exposition - Latin America, PES T and D-LA 2014 - Medellin, Colombia
Duration: 10 Sep 201413 Sep 2014

Publication series

Name2014 IEEE PES Transmission and Distribution Conference and Exposition, PES T and D-LA 2014 - Conference Proceedings
Volume2014-October

Conference

Conference2014 IEEE PES Transmission and Distribution Conference and Exposition - Latin America, PES T and D-LA 2014
Country/TerritoryColombia
CityMedellin
Period10/09/1413/09/14

Keywords

  • Pareto front
  • Transmission planning
  • generation scenarios
  • multiobjective optimization

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