Researches On Using Genetic Algorithms In The Selection Of AGC Units

With the power industry of China is going to establish a market-based operational mechanism, the frequency adjusting of power system had to face the problem of dealing with operating performing management and the problem of pricing. This is so important problem that generation, transmission and customers all concern on it. In this thesis, the author brings out the feasibility of studying using the genetic algorithms in the selection of AGC units. Through this method, the power system can realize the optimizing principle that to satisfy the system power and speed request basing minimum cost.There are lots of optimization algorithms in the selection of AGC units, and they all have their own advantages and shortcomings. These shortcomings include too long times in calculating, too many data needing to process, too large ROM in store data. And because of too many situations and restraints in real condition, these methods are hard to bond. So the problem is that we have to find an algorithm which can not only solve the problem of selection of AGC units but also process the selection in all kinds of situation. In recent years, more and more new algorithms spring out to our sight, just as intelligent control, ANN, GA and etc. People find that GA has lots of advantages that the other algorithms not have, so we come out the idea that using GA in selection of AGC units. The purpose of this thesis is try to use GA in units selection. And the calculating result proves that GA is a advanced algorithm adapting AGC units selection.In this thesis, chapter 1 introduces the fundamentals of Automatic Generation Control (AGC) firstly. Then through comparing China' s power market with the newest power market in the world, the author conclude that it is necessary to research the problem of selection of AGC units. Chapter 2 firstly puts forward the mathematical model of the selection of AGC units.After introducing the mathematical model, it concludes some optimization algorithms using in this field in the world now. Finally the author lists the advantages and shortcomings of these algorithms. Chapter 3 is the core of our thesis. It concentrates on the theory of Genetic Algorithm. And through attentively analyzing, author realizes the GA through coding on Matlab platform. In Chapter 4, the author compares Integer Programming Algorithm (IPA) with Genetic Algorithm (GA) under the condition of the same AGC units. The result shows that GA gets more efficiency than IPA. Chapter 5 pays more attention on the work we should do in the future.