Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms Plus Data Structures Equals Evolution Programs
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiobjective evolutionary algorithms for electric power dispatch problem
IEEE Transactions on Evolutionary Computation
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This paper proposes a multiobjective dispatch model to operate hydroelectric power plants. The model is composed of two algorithms that are based on Genetic Algorithms. The first algorithm is used for the static dispatch of generating units and is aimed at maximizing plant efficiency on an hourly basis. The second step is a multiobjective technique for the daily operation of generating units. The two objectives are to maximize the plant efficiency and to minimize the number of startups and shutdowns of generating units. Data from a Brazilian power plant were used in the simulation of a daily operation. A daily load curve contains 24 static problems, each one solved on average in approximately 2 minutes. The second step was executed in approximately 99 seconds. The proposed model proved suitable for the daily operation of the hydroelectric power plant studied, given the low computational time, satisfactory efficiency and low number of generating units startups and shutdowns (only 12).