A genetic algorithm solution to the unit commitment problem

Journal Article · Thu Feb 01 00:00:00 EST 1996 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/59.485989· OSTI ID:244732

Kazarlis, S A; Bakirtzis, A G; Petridis, V ^[1]

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1. Aristotle Univ. of Thessaloniki (Greece). Dept. of Electrical and Computer Engineering

This paper presents a Genetic Algorithm (GA) solution to the Unit Commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple Ga algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the Varying Quality Function technique and adding problem specific operators, satisfactory solutions to the Unit Commitment problem were obtained. Test results for systems of up to 100 units and comparisons with results obtained using Lagrangian Relaxation and Dynamic Programming are also reported.

• OSTI ID: 244732
• Report Number(s): CONF-950103-; ISSN 0885-8950; TRN: IM9627%%54
• Journal Information: IEEE Transactions on Power Systems, Vol. 11, Issue 1; Conference: Winter meeting of the IEEE Power Engineering Society, New York, NY (United States), 29 Jan - 2 Feb 1995; Other Information: PBD: Feb 1996
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
POWER SYSTEMS
OPERATION
POWER PLANTS
ENERGY EXPENSES
ECONOMICS