Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/69017
Title: A priority list-based evolutionary algorithm to solve large scale unit commitment problem
Authors: Srinivasan, D. 
Chazelas, J.
Keywords: Evolutionary Algorithms
Unit Commitment
Issue Date: 2004
Source: Srinivasan, D.,Chazelas, J. (2004). A priority list-based evolutionary algorithm to solve large scale unit commitment problem. 2004 International Conference on Power System Technology, POWERCON 2004 2 : 1746-1751. ScholarBank@NUS Repository.
Abstract: Unit Commitment is a large scale short-term optimization problem, in which the main objective is to schedule generation to minimise the total fuel cost or to maximise the total profit over a study period, subject to a large number of constraints that must be satisfied. There exists no exact solution technique with a reasonable computation time to provide optimum solution to the unit commitment problem. In large systems, the problem becomes increasingly complex due to the enormous number of possible combinations of the on and off states of all the generating units in the power system over all the time-points in the study period. As the power industry undergoes radical restructuring, the value of the improved solutions that today's optimization algorithms might yield for this problem is increasing. This paper presents an efficient algorithm for aiding unit commitment decisions in such environments. An evolutionary Algorithm (EA) with problem specific heuristics and genetic operators has been employed to solve the problem. The initial random population is seeded with good solutions using a Priority List method to increase the speed of convergence and improve efficiency of the algorithm. Test results on systems of varying sizes show superiority of this approach compared to other methods reported in the literature. © 2004 IEEE.
Source Title: 2004 International Conference on Power System Technology, POWERCON 2004
URI: http://scholarbank.nus.edu.sg/handle/10635/69017
ISBN: 0780386108
Appears in Collections:Staff Publications

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