Abstract
In order to improve the accuracy of wind power ramp forecasting and reduce the threat of ramps to the safe operation of power systems, a wind power ramp event forecast model based on feature extraction and deep learning is proposed in this work. Firstly, the Optimized Swinging Door Algorithm (OpSDA) is introduced to detect wind power ramp events, and the extraction results of ramp features, such as the ramp rate, are obtained. Then, a ramp forecast model based on a deep learning network is established. The historical wind power and its ramp features are used as the input of the forecast model, thereby strengthening the model’s learning for ramp features and preventing ramp features from being submerged in the complex wind power signal. A Convolutional Neural Network (CNN) is adopted to extract features from model inputs to obtain the coupling relationship between wind power and ramp features, and Long Short-Term Memory (LSTM) is utilized to learn the time-series relationship of the data. The forecast wind power is used as the output of the model, based on which the ramp forecast result is obtained after the ramp detection. Finally, the wind power data from the Elia website is used to verify the forecast performance of the proposed method for wind power ramp events.
Highlights
As renewable clean energy, wind energy is widely used in China
The overall idea of multi-step forecasting based on ramp features and deep learning proposed in this paper is as follows: Step 1: Optimized Swinging Door Algorithm (OpSDA) is used to detect historical wind power, and four types of ramp features are extracted; Step 2: A sliding window is used to divide the input, composed of wind power and ramp features, that is input into the Convolutional Neural Network (CNN)–Long Short-Term Memory (LSTM) model
Ramp forecast results for long-term wind power can be obtained by CNN–LSTM, which is conducive to the safe operation of the power system and economic dispatch
Summary
Wind energy is widely used in China. According to the statistical results released by the National Energy Administration, the installed capacity of national wind power integration has increased by 25.74 million kW in 2019, with an installed gross capacity of 210 million kW [1]. In [18], were the spatial features of wind power considered, but the combination of a deep neural network and multi-task learning was adopted, and the input from multiple wind farms was simultaneously received to forecast ramps based on the spatial correlation of wind farms. It is still uncertain whether these feature data, used as the inputs in existing studies, are directly related to ramp features. The wind power forecast data are detected again to obtain the forecast results of ramp events
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