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Abstract
In this paper, an application of genetic-embedded fuzzy systems is proposed to solve a hydroelectric generation scheduling problem. In the proposed approach, the system was fuzzified with respect to objectives and constraints. A genetic algorithm (GA) was included to further enhance the process of tuning membership functions. By using this approach, membership mappings for important parameters can be optimally adjusted and as a result, the computational performance is improved. The proposed approach has been tested on Taiwan Power System (Taipower) through the utility data. Test results have demonstrated the feasibility and effectiveness of the proposed approach for the applications.
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