Short-term Wind Power Interval Prediction Based on Wind Speed of Numerical Weather Prediction and Monte Carlo Method

With the increasing penetration rate of wind power in modern power grid, the optimal operation of power systems has higher requirements for the reliability of wind power interval prediction. The existing wind power interval prediction usually aims at the overall error of the historical data or different output levels to perform classification error modeling, which is difficult to reflect the adaptability of the prediction model to different wind conditions. Based on this, this paper proposes a short-term wind power interval prediction model based on numerical weather prediction (NWP) wind speed and Monte Carlo method. First, according to the NWP wind speed, the point prediction error of the historical period is hierarchically clustered, and the empirical distribution model is used to fit the probability distribution of errors with different wind conditions. Then, Monte Carlo sampling is performed on the value of the cumulative empirical distribution probability corresponding to the NWP wind speed at the prediction moment, and at a given confidence level, the power fluctuation interval that may occur at each predicted point in the short term is solved. Finally, taking the operation data of a wind farm in Jilin Province of China as an example, compared with the commonly used probability prediction method, the reliability of the proposed method is verified. © 2021 Automation of Electric Power Systems Press.