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The National Energy Policy Act of 1992 allows open access to transmission lines. The electric utility industry is in the transition from operating in a monopolistic environment to one that is less regulated. For an electric utility to operate in this new environment, a new algorithm is needed to optimally schedule generating units in the needed response time of an electric power broker. Past methods of unit commitment scheduling are either too computationally slow or do not produce optimal unit commitment schedules. Unit commitment scheduling is the problem of determining the optimal set of generating units within a power system to be used during the next one to seven days. Mathematically, unit commitment scheduling is a mixed integer problem typically with thousands of variables and a large, complex set of constraints.
This dissertation investigates applying a genetic algorithm to the unit commitment scheduling problem. Genetic algorithms are an optimization technique based on the operations observed in natural selection and genetics. The resulting algorithm of this research has three attributes that make it very attractive for unit commitment scheduling. The first attribute is the algorithm can consistently find good unit commitment schedules in a reasonable amount of computation time. The second attribute is the algorithm can produce multiple unit commitment schedules in one execution. The last attribute is that the algorithm performance increases with the addition of true costed constraints. Results are given for three different utilities for 24 and 48 hour unit commitment schedules and are compared to DYNAMICS.