Reserve Capacity Optimization Based On Granular Computing Under Wind Power Integrated

With large-scale wind power integrated, it makes difficult to control the uncontrollable power grid operation, such as power quality deterioration, uneven distribution of power flow, overload power lines and difficulties in adjusting to power scheduling and so on. These issues seriously affect the stability of power system. In addition, changes of the distribution of reserve capacity influence reliability of the system. Therefore, reserve capacity optimization is an important means to improve the system to accept more wind power and mitigate the adverse effects, and can improve reliability and make full use of resources and energy supply. It’s a multi-objective problem. Therefore, the conventional algorithm for solving such problem would not applicable. Starting from wind power integrated will influence to power system reliability, this paper studies granular computing combined with multiple population genetic algorithm, to optimizate reserve capacity contained wind farms under a given reliability level.Firstly, this paper studied the current development of wind power at home and abroad, as well as influence on system reliability and reserve capacity. Based on studies of time series method, a wind forecasting model was used to simulate wind farms and lay the foundation to research on the impact on the power system reliability.Secondly, the paper focused on the impact of wind farms and reliability of the power system, using sequential sequential Monte Carlo method to evaluate reliability of power system containing wind farms.In addition, a multiple population genetic algorithm based on interactive coordination optimization is proposed and used to optimize power system economic dispatch problem. Through the elite strategy and migration strategy, multiple population genetic algorithm can coordinate development of multiple population simultaneously. The migration strategy can exchange information between different population through immigration operator in the process, and the elite strategy can ensure the optimal solution by artificial selection operator. Then, the simulation result shows that multiple population genetic algorithm is effective in optimizing multi-objective problem in power system.Finally, this paper put forward granular computing combining with the multiple population genetic algorithm. After classificating the solution space into granularity, the method optimized the problem rapidly through eliminating Bad particles, and then optimized reserve capacity with wind power integrated. The proposed method is verified effective through the results and analysis.