Optimization of the Electric Power Leveling System Using a Superconducting Magnetic Energy Storage with Genetic Algorithm

Shigeyuki Funabiki, Toshihiko Tanaka, Toshinori Fujii

Research output: Contribution to journalArticle

Abstract

A new optimization method of the electric power leveling system using an SMES is proposed. The SMES is parallelly connected with rolling mills in steel works. The leveling control is based on fuzzy reasoning. The SMES capacity and the scaling factors of the fuzzy controller will be optimized so that the power leveling control can be achieved and then the total cost of the added SMES cost and reduced contract electricity rate becomes lower. The optimization is carried out using the genetic algorithm and the cost reduction of 7.76 billion yen can be achieved. It is confirmed by the power leveling simulation that the proposed optimization method is very effective for designing the power leveling system.

Original languageEnglish
Pages (from-to)1530-1536
Number of pages7
JournalIEEJ Transactions on Industry Applications
Volume123
Issue number12
DOIs
Publication statusPublished - Sep 1 2003
Externally publishedYes

Fingerprint

Energy storage
Genetic algorithms
Leveling (machinery)
Rolling mills
Cost reduction
Costs
Electricity
Controllers
Steel

Keywords

  • genetic algorithm
  • optimization
  • power leveling
  • superconducting magnetic energy storage

ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Cite this

Optimization of the Electric Power Leveling System Using a Superconducting Magnetic Energy Storage with Genetic Algorithm. / Funabiki, Shigeyuki; Tanaka, Toshihiko; Fujii, Toshinori.

In: IEEJ Transactions on Industry Applications, Vol. 123, No. 12, 01.09.2003, p. 1530-1536.

Research output: Contribution to journalArticle

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