Grid Of Discrete Reactive Power Optimization Algorithm

Reactive power optimization is very important for ensuring security and economics of power system. Now there's much necessity about how to make the best use of reactive power sources to reduce power loss for the economic operation of power system. Reactive power optimization problem is a complicated Nonlinear Mixed Integer Programming problem with discrete and continuous variables on object function and conditions. Based on summarization of experience of some scholars, in this thesis, reactive power optimization is based on Primal-dual Interior Point Method and Genetic Algorithm.This paper takes primal-dual interior point method to resolve the power system reactive optimization problem. This method's advantages are that counting time has little relation with the size of problem and counting time doesn't follow the increase of the size of problem to rise distinct. This method's disadvantage is that discrete variables, such as taps of transformer and groups of capacitor, can't be resolved in primal-dual interior point method. So, Branch and Bound Method is applied to solve this problem. This paper tested the above methods with IEEE standard systems. The result indicates that to resolve the discrete variables with simple branch and cut method is feasible.This paper also takes genetic algorithm to resolve the power system reactive optimization problem. Genetic Algorithm is efficient in global optimization. It can deal with discrete variable conveniently. Based on power system, an Improved Genetic Algorithm is given as follows: a dynamic retribution factor is employed in the fitness function; a decimal coding method is used; the initial population is customized to spread in the whole solution space; dynamic crossover factor and dynamic mutation factor are employed; mutation is carried out near the current value to satisfy the device restriction. The calculation results show that the algorithm has a good performance in convergence speed and global optimization. After compared the result based on the Improved Genetic Algorithm with the result based on primal-dual interior-point method, The Improved Genetic Algorithm is used as main tool. The local power supply network of one City is used to validate the proposed algorithm. The calculation results show that it can solve the configuration of transformer ratio and capacitor volume quickly. It's valuable and applicable for current power system.