Study On The Application Of Genetic Algorithm In Unit Commitment Optimization

With the restructuring of power industry, it is more and more important to maintain system running economically in the power system. The unit commitment (UC) optimization is the key problem of the optimal short-term generation scheduling. Since it could bring significant annual financial savings in power generation schedule. From the point of view of mathematics, it is a NP hard combinatorial optimization problem with many constraints and it is difficult to find the optimal solution in theory. Several solution techniques have been applied to this problem to find a good solution. This paper presents the genetic algorithm to solve this problem.As a general and high efficient optimization algorithm, genetic algorithm has already been used in all realms of engineering calculation. Firstly, this paper surveys genetic algorithms and it's applications in power system. Secondly, it proposes genetic algorithms with binary coding, roulette selection and scattered crossover. Its high efficient is verified through testing.A mathematical formulation is provided through researching the character of optimizing UC. Consuming of generator minimization is chosen as the objective function of this formulation, The demand constraint, generation capacity limits, maximum allowable start and stop times limits and etc are taken into account.One illustrative example is given in this paper. How to use the genetic algorithm designed to solve this UC problem is expounded. And the example's particular calculating is given. The result testified that the genetic algorithm can solve this problem better than others. This paper is greatly valuable for the economic operation of power plant. It can improve the competition of the power plant.Finally, The modification of the mathematical formulation of the UC problem and the development's direction of genetic algorithm in future are described.