Research on Day-Ahead Scheduling Strategy of the Power System Includes Wind Power Plants and Photovoltaic Power Stations Based on Big Data Clustering and Filling

Traditional power system scheduling optimization methods cannot fully deal with the massive data brought by the increase of new energy penetration. Aiming at the above problems, this paper proposes a day-ahead scheduling optimization method based on big data clustering and filling for power system includes wind power plants and photovoltaic power stations. Firstly, according to the collected historical data of power grid, the K-means clustering method of big data is used to generate representative load, wind power and solar illumination scene sets. Secondly, a missing value filling method based on historical data assisted scene analysis was proposed. Finally, the day-ahead scheduling model of power system with wind power plants and photovoltaic power stations is established and solved by improved particle swarm optimization algorithm. The simulation results show that this method can improve the filling accuracy of the missing value of the day-ahead dispatching historical data of the power system, and meet the development demand of the power system with new energy.