IEEE Transactions on Power Systems · 1996 · 169 citations · 55 references
ArchitectureElectrical EngineeringEnergy EconomicsOptimizationSchedulingEvolutionary ComputationEvolutionary OptimizationGenetic AlgorithmsPopulation-based SearchSchedule OptimizationDomain-specific MutationGenetic Algorithm OptimizationLagrangian RelaxationPower Generation SchedulingUnit Commitment
This paper presents a new unit commitment scheduling algorithm. The proposed algorithm consist of using a genetic algorithm with domain specific mutation operators. The proposed algorithm can easily accommodate any constraint that can be true costed. Robustness of the proposed algorithm is demonstrated by comparison to a Lagrangian relaxation unit commitment algorithm on three different electric utilities. Results show the proposed algorithm finds good unit commitment schedules in a reasonable amount of computation time. Included in the appendix is an explanation of the true costing approach.
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Genetic algorithms in search, optimization, and machine learning
Choice Reviews Online · 1989 · 49.3K citations
D. E. Goldberg · Medical Entomology and Zoology · 1989 · 10.1K citations