Two-phase Optimization Method Of Multi-period Reactive Power And Voltage Control

Reactive power optimization is one of the most control methods to ensure power system operation securely and economically, and an effective measure to improve the voltage profile and reduce the loss. Study the problem of reactive power optimization has the great significance in theory and practical application. The problem of traditional reactive power optimization is static reactive power optimization under the circumstance of special load. But the load is changing in any time in the practical power system, so the traditional reactive power optimization can't meet the practical need. Reactive power optimization considering the load changing and action number constraints of control devices is called as dynamic reactive power optimization.For studying on dynamic reactive power optimization, A two-phase optimization method of multi-period reactive power and voltage control is proposed to confine the action times of control devices. In the first phase, a static model of reactive power optimization is established, which takes into account of active loss minimization. This paper studies the flow of solution to reactive power optimization with applied by Genetic Algorithm, and improves the solution of reactive power optimization based Genetic Algorithm on base of utilizing features of reactive power optimization, presents full project based improved Genetic Algorithm with including code mode, selection operator, crossover operator, mutation operator and termination conditions. To solve this problem, this paper presents this improved Genetic Algorithm to seek out some optimal states of each time interval over a dispatching period, these optimal states form a state space of control devices. Based on the first phase, in the second phase, Dynamic Programming or improved Genetic Algorithm is adopted to determine the shortest transfer route of control devices'states over the whole dispatch period. And accordingly the system power loss, voltage, and equipment action times can be optimized. Furthermore, this method implements parallel process easily.To demonstrate the effectiveness of the proposed approach, the IEEE-14 and IEEE-30 node system based on the forecasted load curves are built to simulate the dynamic reactive power optimization. It is concluded that a greater loss reduction can be achieved by the proposed method, and the optimal schedule is simple, and the proposed method has online application foreground.