A regional wind-solar resource assessment method based on K-means clustering

Abstract

The invention relates to a method for regional wind-solar power generation resource assessment method based on K-means clustering, comprising the following steps: pre-processing original wind-photovoltaic data sequence by using a virtual power generation system model, and converting the original wind speed and light intensity data into capacity coefficient; constructing the index of regional resource coordination coefficient, quantifying the complementarity among different kinds of power generation resources in different locations, and forming a new data set; using a PCA method to deal with anew data set and reducing the dimension of a new data set; and using a K-means clustering method to extract the complementary pattern of the regional wind-solar power generation resources and visualizes the result. The invention reduces the dimension of the original data set of the regional wind-solar power generation resource assessment, takes into account the requirements of high-dimensional data calculation and visualization, and has high engineering practical application value and advancement; and comprehensive considers the complementarity of regional power generation resources, can guidethe planning of a regional complementary power generation system and improves the resource utilization efficiency.

Description

technical field [0001] The invention relates to the field of new energy power generation, in particular to a method for evaluating regional wind power generation resources based on K-means clustering. Background technique [0002] In recent years, wind power generation technology has developed rapidly, and the installed capacity of wind power and photovoltaic units has continued to increase. Wind power generation has irreplaceable advantages such as cleanliness and pollution-free. However, due to the inherent randomness of resources, the output of wind and solar power generation is volatile, which poses new challenges to the safe operation of the power grid. At the same time, wind and wind power resources also have inherent complementarity. Making full use of wind and wind complementarity can smooth the output curve after aggregation, reduce the overall output volatility, reduce the dependence of wind and wind power generation on power grid climbing backup and energy storag...

AI Technical Summary

Problems solved by technology

[0005] At present, the research on resource assessment of regional wind power generation is not rich and in-depth enough, and there is a lack of a widely used method

Existing methods can not take into account the requirements of high-dimensional data calculation and visualization, and the evaluation results of regional wind power resource complementarity are difficult to apply to actual projects, which reduces the practicability

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