Ultra-short term wind power forecasting method based on time series method

Abstract

The invention discloses an ultra-short term wind power forecasting method based on a time series method. The forecasting method comprises the following steps: 1. gathering wind measurement data of an anemometer tower adjacent to a wind power station; 2. processing data: carrying out smoothing and stabilizing treatments on the wind speed recorded by the anemometer tower; 3. carrying out wind speedprediction modeling by utilizing the time series method, respectively modeling the wind speed data subjected to the smoothing and stabilizing treatments in the step 2 according to a forecasting time resolution, and establishing 16 prediction models, and figuring out the forecast wind speed within 0-4 hours; and 4. calculating the ultra-short term wind power: calculating the prediction models which are used for forecasting the wind speed input wind power to obtain a wind power prediction result within the forecast validity. By utilizing the method provided by the invention, the wind power output within 0-4 hours of the wind power station can be forecast in a rolling mode, reasonable data support can be provided for electric network frequency modulation and the maintenance of the stable operation of the electric network, so that a dispatching department can adopt solutions in advance for various mutational situations caused by haste changes of the wind power during the electric network operation by the dispatching department.

Description

【Technical field】 [0001] The invention relates to the field of wind power generation systems, in particular to an ultra-short-term wind power forecasting method based on a time series method. 【Background technique】 [0002] Since the 20th century, the world economy has developed rapidly, the demand for energy has doubled, and the energy industry has developed on a large scale. However, conventional energy is not only facing the problem of depletion, but also due to the massive use of fossil energy, the direct emission of greenhouse gases and aerosols into the atmosphere has caused global warming and increasingly serious environmental pollution, which has a negative impact on the natural ecosystem, social economy and human body. Both pose a serious threat to health and have drawn great attention from the international community and the public. In order to better overcome the constraints of the contradiction between energy supply and demand, promote the sustainable developmen...

AI Technical Summary

Problems solved by technology

[0006] Short-term wind power forecasting can provide the forecast results of wind power output power in the next 24 hours of the wind farm, which is of great significance for the formulation of dispatching plans; If the short-term wind speed forecast of the method wants to accurately reflect the changing law of wind speed, the large-scale forecast field used must accurately describe the weather characteristics of the area where the wind farm is located.

As far as the existing weather forecasting technology is concerned, it is difficult to make accurate calculations for the future weather situation

Secondly, the characteristics of the numerical model itself also limit the accuracy of the wind speed forecast. It is difficult for the numerical model to accurately describe the underlying surface conditions such as the terrain, vegetation and roughness changes around the wind farm.

In addition, global warming makes sudden weather events more frequent, and possible sudden changes in weather will greatly increase the error of forecasting. Therefore, only using 24-hour short-term forecasts is not enough to deal with various situations in the actual operation of the power grid.

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