Method for accurately estimating wind power prediction error interval

Abstract

The invention discloses a method for accurately estimating wind power prediction error intervals. The method comprises the following steps: firstly, obtaining historical wind power data of a wind power plant; secondly, calculating wind power predication errors of all prediction points of the wind power plant, and establishing a wind power predication error distribution model; thirdly, establishing an error probability density function according to the distribution of the predication errors; fourthly, obtaining a confidence interval, meeting a certain confidence level, of the predication errors according to a given wind power predication value; and fifthly, calculating the shortest confidence interval through a Lagrange multiplier algorithm. On the basis of point predication, a probability density function of wind power prediction errors is obtained through interval prediction, and the confidence interval under a certain confidence level is calculated by a probability theory. In this way, the reliability of the interval to contain a wind power point predication value is determined, and the precision of wind power interval prediction is effectively improved.

Description

technical field [0001] The invention relates to a method for accurately estimating the wind power prediction error interval, in particular to a solution algorithm for the minimum confidence interval of the wind power prediction error based on non-parametric kernel density estimation. Background technique [0002] my country's wind power generation is developing rapidly, but it faces the challenge of frequent failures in the initial stage. Wind power is an unreliable form of electricity generation due to the fluctuating and intermittent nature of the wind resource. [0003] However, due to the volatility, intermittence and randomness of wind power generation, the accuracy of wind power forecasting is difficult to reach the accuracy level of system load forecasting, and the power system has to configure additional reserve capacity to balance the large error of wind power forecasting. Inaccurate estimation of the error interval will make it difficult to achieve ideal results f...

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Problems solved by technology

[0003] However, due to the volatility, intermittency and randomness of wind power generation, the accuracy of wind power forecasting is difficult to reach the accuracy level of system load forecasting, and the power system has to configure additional reserve capacity to balance the large error of wind power forecasting

Inaccurate estimation of the error interval will make it difficult to achieve ideal results for the configured reserve capacity: 1) The estimation of the error interval is too small, which may easily lead to insufficient spare capacity and cannot guarantee the peak-shaving capability of the system; 2) If the estimation of the error interval is too large, it will cause The waste of spare capacity increases the operating cost of the system

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