Author(s)

Mai A. Farag¹, M. A. El-Shorbagy¹, I. M. El-Desoky¹, A. A. El-Sawy^1,2, A. A. Mousa^1,3

¹Department of Basic Engineering Science, Faculty of Engineering, Menoufiya University, Al Minufya, Egypt.
²Department of Mathematics, Faculty of Science, Qassim University, Qassim, Saudi Arabia.
³Department of Mathematics, Faculty of Sciences, Taif University, Taif, Saudi Arabia.

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.

Unit Commitment (UC), Genetic Algorithm (GA), k-Means Clustering Technique

Farag, M. , El-Shorbagy, M. , El-Desoky, I. , El-Sawy, A. and Mousa, A. (2015) Binary-Real Coded Genetic Algorithm Based k-Means Clustering for Unit Commitment Problem. Applied Mathematics, 6, 1873-1890. doi: 10.4236/am.2015.611165.