Solving unit commitment problem by combining of continuous relaxation method and genetic algorithm

Ken Ichi Tokoro, Yasushi Masuda, Hisakazu Nishino

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

This paper proposes a genetic algorithm for solving a unit commitment problem of electric generators, which formally is a mixed integer nonlinear programming problem. The proposed algorithm finds the optimal ON/OFF status of units by a combination of genetic algorithm and continuous relaxation method. In the proposed algorithm, a chromosome encodes a partial solution, in which the values of some variables are unfixed. The fitness of an individual is evaluated based upon a solution of the problem where all unfixed variables in the chromosome are relaxed to be continuous. Numerical experiments show the satisfactory performance of the proposed algorithm with respect to the solution quality for planning the actual unit commitment schedule.

Original languageEnglish
Title of host publicationProceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
Pages3474-3478
Number of pages5
DOIs
Publication statusPublished - 2008 Dec 1
EventSICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology - Tokyo, Japan
Duration: 2008 Aug 202008 Aug 22

Publication series

NameProceedings of the SICE Annual Conference

Other

OtherSICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology
CountryJapan
CityTokyo
Period08/8/2008/8/22

Keywords

  • Genetic algorithm mixed integer nonlinear optimization
  • Optimization
  • Unit commitment problem

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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  • Cite this

    Tokoro, K. I., Masuda, Y., & Nishino, H. (2008). Solving unit commitment problem by combining of continuous relaxation method and genetic algorithm. In Proceedings of SICE Annual Conference 2008 - International Conference on Instrumentation, Control and Information Technology (pp. 3474-3478). [4655263] (Proceedings of the SICE Annual Conference). https://doi.org/10.1109/SICE.2008.4655263