Reactive Power Optimization In Power System Based On Adaptive Focusing Particle Swarm Optimization Algorithm

With the rapid development of electric power system, the grids become larger and larger. How to guarantee the power system running economically, safely and stably becomes a focus in the world. Reactive power optimization can optimize reacitive power dispatch and reduce power loss and voltage loss, different controlled variables are chosen to do the adjustment. The increase of variables and non-linear constraints along with the complicated relationship between them make the reactive power optimization problem a complicated, large run mathematical programming one. The swarm intelligence optimization algorithms have the obvious advantages in searching the best solution for the large run, non-linear problem, offering new ways to solve the problem.The traditional reactive power optimization always takes the economical run of the electric power system as the destination, and gets the reactive power optimization results through adjusting the generator voltages, transformer taps and capacitor tanks. However, the voltage stability is often neglected. In fact, in the condition of the electric power market at present, considering the influence of environmental and economical elements, the power system is apt to run closely to the margin status, resulting in the insufficient voltage stable redundancy. Therefore the voltage collapse will happen sometime. So the multiobjective reactive power optimization incorporating static voltage stability was studied in this dissertation, and the corresponding mathematical model was adopted.In the dissertation, the present research and development of the reactive power optimization were reviewed, including kinds of methods for reactive optimization, and the advantages, disadvantages and application of these methods were analyzed. The adaptive focusing particle swarm optimization (AFPSO) proposed in this dissertation was an adaptive swarm intelligence optimization algorithm with preferable ability of global search and search rate based on particle swarm optimization. Based on optimal control principle, index of static voltage stability was introduced and a model of multiobjective reactive power optimization was adopted, where the least active power loss, the best voltage level and the biggist static voltage stability margin were taken into account, using fuzzy set theory to transformed multiobjective optimization problems into a single objective optimization problem. At the same time, the form of penalty function was adopted to deal with the inequality constraints of state-variables about load-bus voltage and generation reactive power. AFPSO applied for optimal reactive power was evaluated on IEEE 30-bus and IEEE 57-bus power system. The simulation results and the comparison results with various optimization algorithms demonstrated that the proposed approach convergeed to better solutions in account precision, convergence stability and time than the earlier refered approaches and the algorithm was able to heighten power system voltage stability during the economical operation, simultaneously, the validity and superiority of AFPSO were proved.