### Abstract

Original language | English |
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Pages | 863 Vol. 2 |

Publication status | Published - 2003 |

Event | Power Engineering Society General Meeting, 2003, IEEE - Duration: 1 Jan 2003 → … |

### Conference

Conference | Power Engineering Society General Meeting, 2003, IEEE |
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Period | 1/01/03 → … |

### Fingerprint

### 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

### Cite this

*Knowledge guided genetic algorithm for optimal contracting strategy in a typical standing reserve market*. 863 Vol. 2. Paper presented at Power Engineering Society General Meeting, 2003, IEEE, .

**Knowledge guided genetic algorithm for optimal contracting strategy in a typical standing reserve market.** / Li, F; Lindquist, T M.

Research output: Contribution to conference › Paper

}

TY - CONF

T1 - Knowledge guided genetic algorithm for optimal contracting strategy in a typical standing reserve market

AU - Li, F

AU - Lindquist, T M

PY - 2003

Y1 - 2003

N2 - 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.

AB - 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.

KW - search space reduction

KW - costing

KW - power system simulation

KW - genetic algorithms

KW - genetic algorithm

KW - GA

KW - final solution cost

KW - subset is detection

KW - optimal contracting strategy

KW - solution robustness

KW - set theory

KW - power markets

KW - standing reserve market

M3 - Paper

SP - 863 Vol. 2

ER -