This paper presents a new algorithm for solving unit commitment (UC) problems using a binaryreal\ncoded genetic algorithm based on k-means clustering technique. UC is a NP-hard nonlinear\nmixed-integer optimization problem, encountered as one of the toughest problems in power systems,\nin which some power generating units are to be scheduled in such a way that the forecasted\ndemand is met at minimum production cost over a time horizon. In the proposed algorithm, the\nalgorithm integrates the main features of a binary-real coded genetic algorithm (GA) and k-means\nclustering technique. The binary coded GA is used to obtain a feasible commitment schedule for\neach generating unit; while the power amounts generated by committed units are determined by\nusing real coded GA for the feasible commitment obtained in each interval. k-means clustering algorithm\ndivides population into a specific number of subpopulations with dynamic size. In this\nway, using k-means clustering algorithm allows the use of different GA operators with the whole\npopulation and avoids the local problem minima. The effectiveness of the proposed technique is\nvalidated on a test power system available in the literature. The proposed algorithm performance\nis found quite satisfactory in comparison with the previously reported results.
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