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.
Highlights
The unit commitment (UC) problem, one of the most important tasks of operational planning of power systems, which has a significant influence on secure and economic operation of power systems [1]
We propose a new approach for solving UC problems using a binary-real coded genetic algorithm based on k-means clustering technique to integrate the main features of the both algorithms; where a binary-real coded GA, in which the binary part deals with the scheduling of units and the real part determines power output levels of committed generating units. k-means clustering technique is used in order to avoid the local minima problem; where the population can be divided into a specific number of subpopulations
This paper investigates the unit commitment problem by genetic algorithm based on k-mean clustering algorithm which integrates the main features of a binary-real coded genetic algorithm (GA) and k-means clustering technique
Summary
The unit commitment (UC) problem, one of the most important tasks of operational planning of power systems, which has a significant influence on secure and economic operation of power systems [1]. Research efforts have concentrated on efficient and near-optimal UC algorithms which can be applied to realistic power systems and have reasonable storage and computation time requirements Such alternative algorithms studied for the UC problem can be divided into two classes [4]: deterministic methods and meta-heuristic methods. The investigated deterministic methods include Priority List (PL) [5], Dynamic Programming (DP) [6], branch-and-bound method [7], Lagrangean Relaxation (LR) [8] and Mixed Integer Linear Programming (MILP) [9] These methods suffer from the quality of final solution are not guaranteed, the “curse of dimensionality” if the size of a system is large, applied to small UC problems, required major assumptions that limit the solution space because it is difficult achieve a balance between the efficiency and the accuracy of the model, and may not provide feasible solutions to the relaxed problem due to the inherent non-convexity of the UC problem [10]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
