Study On The Method For Dynamic Reactive Power Optimization Based On Time-Space Decoupling

The control of reactive power is a very important problem related to the quality and stability of voltage and the economic operation of power system. The traditional reactive power optimization is for a particular case of the system load. In the operation of power system, due to the changing load, the traditional reactive power optimization can not fully meet the needs of the actual operation. The change of load in every node and the action number constraints of the control equipments are considered in dynamic reactive power optimization. It is overly optimized through action time allocated rationally. The dynamic reactive power optimization is a complex programming problem which is spatial-temporal distributed, nonlinear, mixed integer programmer.Due to the space-time coupling of dynamic reactive power optimization, it is divided into two parts: one is the time decoupling, the other is the space decoupling. A new heuristic strategy is developed for time coupling. A new heuristic strategy is developed for time coupling. A dynamic reactive power optimization is changed into several static reactive power optimizations. Then the static reactive power optimization is space-decoupled with the cultural differential evolution based on neighborhood topology algorithm to get the final optimal results.A new method is proposed for solving dynamic reactive power optimization, based on the further study on the mathematical model and the methods for dynamic reactive power optimization. First of all, the forecasted continuous curve of one day is divided into 24 segments. The load of every segment is calculated by integral mean value theorem. Through flow calculation, the initial time of equipment action is allocated. The pre-action schedule of equipment is formatted. Due to the correlation of different equipments in the operation of the power network, secondly the action time of equipments is readjusted by the correlation of different equipment and the maximum number of the equipment action. The dynamic reactive power optimization is considered overly. Finally the examples are simulated with the algorithm. The results show that the proposed method is simple and effective, the schedule of the equipment action is obtained and a better optimal result is obtained in the process of dealing dynamic reactive power optimization.