TITLE:
Binary-Real Coded Genetic Algorithm Based k-Means Clustering for Unit Commitment Problem
AUTHORS:
Mai A. Farag, M. A. El-Shorbagy, I. M. El-Desoky, A. A. El-Sawy, A. A. Mousa
KEYWORDS:
Unit Commitment (UC), Genetic Algorithm (GA), k-Means Clustering Technique
JOURNAL NAME:
Applied Mathematics,
Vol.6 No.11,
October
22,
2015
ABSTRACT: This paper presents a new algorithm for solving unit commitment (UC) problems using a binary-real coded genetic algorithm based on k-means clustering technique. UC is a NP-hard nonlinear mixed-integer optimization problem, encountered as one of the toughest problems in power systems, in which some power generating units are to be scheduled in such a way that the forecasted demand is met at minimum production cost over a time horizon. In the proposed algorithm, the algorithm integrates the main features of a binary-real coded genetic algorithm (GA) and k-means clustering technique. The binary coded GA is used to obtain a feasible commitment schedule for each generating unit; while the power amounts generated by committed units are determined by using real coded GA for the feasible commitment obtained in each interval. k-means clustering algorithm divides population into a specific number of subpopulations with dynamic size. In this way, using k-means clustering algorithm allows the use of different GA operators with the whole population and avoids the local problem minima. The effectiveness of the proposed technique is validated on a test power system available in the literature. The proposed algorithm performance is found quite satisfactory in comparison with the previously reported results.