Wind Power Probabilistic Prediction Optimization Algorithm Based on Working Condition Identification

Abstract

In this paper, a wind power probabilistic prediction optimization algorithm based on working condition identification and probabilistic prediction is proposed. Based on the historical data of power forecasting in different wind resource regions, the probability distribution of wind power forecasting error is estimated by working condition identification and kernel density function, the wind power bandwidth forecasting result is generated by the scenario sampling method, and the daily power balance plan is formulated in combination with the grid load fluctuation. The actual optimization calculation results show that the method in this paper can save the reserve capacity and peak shaving margin of the power grid, and improve the economy and safety of wind power consumption.