Kazemzadeh, R., Moazen, M. (2019). Unit commitment by a fast and new analytical non-iterative method using IPPD table and “λ-logic” algorithm. Journal of Operation and Automation in Power Engineering, 7(1), 27-39. doi: 10.22098/joape.2019.4427.1351

R. Kazemzadeh; M. Moazen. "Unit commitment by a fast and new analytical non-iterative method using IPPD table and “λ-logic” algorithm". Journal of Operation and Automation in Power Engineering, 7, 1, 2019, 27-39. doi: 10.22098/joape.2019.4427.1351

Kazemzadeh, R., Moazen, M. (2019). 'Unit commitment by a fast and new analytical non-iterative method using IPPD table and “λ-logic” algorithm', Journal of Operation and Automation in Power Engineering, 7(1), pp. 27-39. doi: 10.22098/joape.2019.4427.1351

Kazemzadeh, R., Moazen, M. Unit commitment by a fast and new analytical non-iterative method using IPPD table and “λ-logic” algorithm. Journal of Operation and Automation in Power Engineering, 2019; 7(1): 27-39. doi: 10.22098/joape.2019.4427.1351

Unit commitment by a fast and new analytical non-iterative method using IPPD table and “λ-logic” algorithm

^{1}Department of Electrical Power Engineering, Sahand University of Technology, Tabriz, Iran.

^{2}Department of Electrical Engineering, Faculty of Engineering, University of Bonab, Bonab, Iran

Abstract

Many different methods have been presented to solve unit commitment (UC) problem in literature with different advantages and disadvantages. The need for multiple runs, huge computational burden and time, and poor convergence are some of the disadvantages, where are especially considerable in large scale systems. In this paper, a new analytical and non-iterative method is presented to solve UC problem. In the proposed method, improved pre-prepared power demand (IPPD) table is used to solve UC problem, and then analytical “λ-logic” algorithm is used to solve economic dispatch (ED) sub-problem. The analytical and non-iterative nature of the mentioned methods results in simplification of the UC problem solution. Obtaining minimum cost in very small time with only one run is the major advantage of the proposed method. The proposed method has been tested on 10 unit and 40-100 unit systems with consideration of different constraints, such as: power generation limit of units, reserve constraints, minimum up and down times of generating units. Comparing the simulation results of the proposed method with other methods in literature shows that in large scale systems, the proposed method achieves minimum operational cost within minimum computational time.

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