Unit Commitment by Adaptive Particle Swarm Optimization

• Saber Ahmed Yousuf

  University of the Ryukyus

• Senjyu Tomonobu

  University of the Ryukyus

• Miyagi Tsukasa

  University of the Ryukyus

• Urasaki Naomitsu

  University of the Ryukyus

• Funabashi Toshihisa

  Meidensha Corporation

Abstract

This paper presents an Adaptive Particle Swarm Optimization (APSO) for Unit Commitment (UC) problem. APSO reliably and accurately tracks a continuously changing solution. By analyzing the social model of standard PSO for the UC problem of variable size and load demand, adaptive criteria are applied on PSO parameters and the global best particle (knowledge) based on the diversity of fitness. In this proposed method, PSO parameters are automatically adjusted using Gaussian modification. To increase the knowledge, the global best particle is updated instead of a fixed one in each generation. To avoid the method to be frozen, idle particles are reset. The real velocity is digitized (0/1) by a logistic function for binary UC. Finally, the benchmark data and methods are used to show the effectiveness of the proposed method.

Journal

• IEEJ Transactions on Power and Energy

  IEEJ Transactions on Power and Energy 127 (1), 155-163, 2007

  The Institute of Electrical Engineers of Japan

Keywords

• adaptive particle swarm optimization
• economic load dispatch
• unit commitment
• Gaussian distribution

Details

• CRID: 1390282679580206208
• NII Article ID: 10018456526
• NII Book ID: AN10136334
• DOI: 10.1541/ieejpes.127.155
• ISSN: 13488147
• ISSN: 03854213
• NDL BIB ID: 8625982
• Web Site: https://ndlsearch.ndl.go.jp/books/R000000004-I8625982
• Web Site: http://www.jstage.jst.go.jp/article/ieejpes/127/1/127_1_155/_pdf
• Text Lang: en
• Data Source:
  • JaLC
  • NDL
  • Crossref
  • CiNii Articles
• Abstract License Flag: Disallowed