Keywords: unit commitment, genetic algorithms, GA, dynamic programming, Lagrangian relaxation, simulated annealing, utility systems, generation scheduling, power generation, India
Genetic algorithm-based simulated annealing method for solving unit commitment problem in utility system
This paper presents a new approach to solve the short-term unit commitment problem using genetic algorithm-based simulated annealing method for utility system. The objective of this paper is to find the generation scheduling such that the total operating cost can be minimised when subjected to a variety of constraints. This also means that it is desirable to find the optimal generating unit commitment in the power system for the next H hours. Genetic Algorithms (GAs) are general-purpose optimisation techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as neural section, genetic recombination and survival of the fittest. In this, the unit commitment schedule is coded as a string of symbols. An initial population of parent solutions is generated at random. Here, each schedule is formed by committing all the units according to their initial status ('flat start'). Here, the parents are obtained from a predefined set of solutions, i.e., each and every solution is adjusted to meet the requirements. Then, a random recommitment is carried out with respect to the unit's minimum downtimes. Simulated Annealing (SA) improves the status. A 66-bus utility power system with 12 generating units in India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different IEEE test systems consisting of 24, 57 and 175 buses. Numerical results are shown comparing the cost solutions and computation time obtained by using the GA method and other conventional methods.