Regional wind power modeling method and device, storage medium and equipment

Abstract

The invention provides a regional wind power modeling method, which comprises the following steps: acquiring historical power generation powers of a plurality of wind power plants in a set region, performing linear correlation analysis and clustering processing on the wind power plants in the region based on the historical power generation powers, and determining a plurality of equivalent wind power plants in the set region; according to each equivalent wind power plant, establishing an edge density function of each equivalent wind power plant by using an orthogonal series method; and according to the edge density function of each equivalent wind power plant, establishing a wind power joint probability model of a set area by using a Pair-Copula function method. Through the method, the calculation number of the wind power plants in the region is reduced, the modeling dimension is reduced, the influence of the random variable boundary effect is avoided, and the probability modeling accuracy of the generated power correlation of the wind power plants is improved.

Description

technical field [0001] The present invention relates to the technical field of wind power generation, in particular to a method, device, storage medium and device for modeling regional wind power. Background technique [0002] Wind power generation has the characteristics of volatility and intermittency, so the power generated by wind farms has the characteristics of strong randomness, obvious intermittency, and lack of power regulation ability, so that wind power cannot be controlled at will. With the continuous growth of wind power installed capacity and grid-connected scale, the random fluctuation of wind farm power has a huge impact on the power system. In addition, there are usually multiple wind farms connected to the power system in a region. When analyzing the regional power system, we often only care about the overall change of the generated power of the wind farms in the region, and simply equivalently treat all wind farms as a random variable. The influence betwe...

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Problems solved by technology

Due to the large number of random variables, the above multi-dimensional kernel density estimation method leads to an increase in the complexity of the solution and a decrease in the accuracy of the results, and it is easy to fall into the curse of dimensionality in the case of high-dimensional operations.

Furthermore, the above-mentioned kernel density estimation method will also be affected by the boundary effect of random variables, and the selection of window width has a great influence on the accuracy of the estimation results, but there is currently no unified method for selecting the window width

The defects of the above-mentioned prior art lead to low accuracy of probabilistic modeling of power generation correlation between wind farms

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