Knowledge guided genetic algorithm for optimal contracting strategy in a typical standing reserve market
Li, F. and Lindquist, T. M., 2003. Knowledge guided genetic algorithm for optimal contracting strategy in a typical standing reserve market. In: Power Engineering Society General Meeting, 2003, IEEE, 2003-01-01.
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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.
|Item Type||Conference or Workshop Items (Paper)|
|Creators||Li, F.and Lindquist, T. M.|
|Uncontrolled 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|
|Departments||Faculty of Engineering & Design > Electronic & Electrical Engineering|
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