Knowledge guided genetic algorithm for optimal contracting strategy in a typical standing reserve market

F Li, T M Lindquist

Research output: Contribution to conferencePaper

6 Citations (Scopus)

Abstract

This paper proposes a knowledge guided genetic algorithm (GA) when used for searching for the optimal contracting strategy in a typical standing reserve market. The knowledge is effectively used for significantly reducing search space. The knowledge is obtained by identifying subset of tenders that have similar contract patterns among low cost solutions and relative large impact on the final solution results. Once the subset is detected, it is then fixed throughout the subsequent GA searches so that the GA can work in a much reduced problem space and concentrate on areas that need most attentions. This search space reduction has demonstrated on a system with 83 tenders. The simulation results clearly show that the search space reduction has significantly improved final solution cost and solution robustness when comes to meet operating reserve requirements.
Original languageEnglish
Pages863 Vol. 2
Publication statusPublished - 2003
EventPower Engineering Society General Meeting, 2003, IEEE -
Duration: 1 Jan 2003 → …

Conference

ConferencePower Engineering Society General Meeting, 2003, IEEE
Period1/01/03 → …

Keywords

  • search space reduction
  • costing
  • power system simulation
  • genetic algorithms
  • genetic algorithm
  • GA
  • final solution cost
  • subset is detection
  • optimal contracting strategy
  • solution robustness
  • set theory
  • power markets
  • standing reserve market

Fingerprint Dive into the research topics of 'Knowledge guided genetic algorithm for optimal contracting strategy in a typical standing reserve market'. Together they form a unique fingerprint.

Cite this