CN-122000878-A - Method and device for generating multi-new-energy-source base power-output combined scene

CN122000878ACN 122000878 ACN122000878 ACN 122000878ACN-122000878-A

Abstract

The invention discloses a method and a device for generating a multi-new-energy-source-base output combined scene, and belongs to the technical field of electrical engineering. Furthermore, the SARIMA-Copula model is utilized to obtain the output combination scene set of the multi-new-energy base, so that the space-time correlation among the output of the multi-base can be described, the time sequence-space combination modeling can be realized by the model, and further, a more accurate multi-new-energy base output combination scene is obtained. The method is suitable for planning a new energy power system, can improve the simulation precision of multi-base combined output and the new energy consumption capability, optimize the energy storage configuration and the power grid extension scheme, provide more accurate output prediction for new energy generators, and enhance the robustness of scheduling decisions.

Inventors

AI XIAOMENG
WEN JINYU
LIU YONGXING
XU CHUANYU
LUO TIANJIAO
WEI RENBO
He Tingke
CUI SHICHANG
FANG JIAKUN
YAO WEI

Assignees

华中科技大学

Dates

Publication Date: 20260508
Application Date: 20260123

Claims (10)

1. The method for generating the multi-new-energy-source base power-output combined scene is characterized by comprising the following steps of: s1, inputting historical output data of multiple new energy bases into a constructed seasonal differential autoregressive moving average SARIMA model so that the SARIMA model outputs output prediction data; s2, drawing a multi-element frequency histogram by utilizing an edge probability distribution function corresponding to the output prediction data; s3, selecting an optimal Copula function according to tail characteristics of the multi-element frequency histogram to construct a SARIMA-Copula model; s4, generating a random point output sequence of the multi-new energy base at each moment by utilizing the SARIMA-Copula model, and performing inverse operation on an edge distribution function corresponding to the random point output sequence to obtain an output combined scene set of the multi-new energy base; S5, reducing the output combined scene set of the multi-new-energy base to obtain a typical scene set.
2. The method for generating the multi-new-energy-source-base power-output combined scene as claimed in claim 1, wherein the selecting an optimal Copula function according to the tail characteristics of the multi-frequency histogram to construct the SARIMA-Copula model comprises: If the tail characteristic of the multi-element frequency histogram of the output prediction data is symmetrical tail and tail progressive independent characteristic, selecting a Gaussian Copula function as the optimal Copula function; If the tail characteristic of the multi-element frequency histogram of the output prediction data is symmetrical tail and tail progressive correlation characteristic, selecting a t-Copula function as the optimal Copula function; If the tail characteristic of the multi-element frequency histogram of the output prediction data is an asymmetric tail and is sensitive to an upper tail or a lower tail, selecting a Gumbel Copula function as the optimal Copula function; and if the tail characteristic of the multi-element frequency histogram of the output prediction data is symmetrical tail and the upper tail and lower tail are moderately correlated, selecting a Frank Copula function as the optimal Copula function.
3. The method for generating the multi-new-energy-source-base output combined scene according to claim 1 is characterized by comprising the steps of determining a clustering center and the clustering number of a K-means clustering method, and simplifying the multi-new-energy-source-base output combined scene set by using the K-means clustering method to obtain a target scene set.
4. The method for generating the multi-new-energy-source-base-power-combining scene as claimed in claim 3, wherein the determining process of the clustering centers of the K-means clustering method is characterized in that the average value of the power of all scenes is used as the clustering center if the number N of the clustering centers is set to be equal to 1, 2 scenes with the farthest Euclidean distance are selected as the clustering centers if the number N of the clustering centers is set to be equal to 2, and two scenes with the farthest Euclidean distance and the average value of the power of all scenes are selected as the initial three clustering centers if the number N of the clustering centers is set to be equal to 3.
5. The method for generating the multi-new energy base output combined scene according to claim 4, wherein the determining process of the clustering centers of the K-means clustering method is characterized in that if the number N of the clustering centers is set to be larger than 3, the scenes with the average output values of all the scenes and the two scenes with the farthest Euclidean distances are selected as initial three clustering centers, euclidean distances between each scene and a plurality of determined clustering centers are calculated respectively, the scene with the farthest Euclidean distance sum of the plurality of determined clustering centers is selected as the next clustering center, and the like until N clustering centers are found.
6. The method for generating the multi-new energy base power combining scene as claimed in claim 3, wherein the clustering number determining process of the K-means clustering method is to select the corresponding clustering number as the optimal clustering number when the cluster error square sum is obviously reduced along with the increase of the clustering number.
7. The method for generating the multi-new energy base power combining scene as claimed in claim 3, wherein the clustering number determining process of the K-means clustering method is that the square sum of errors in clusters under each clustering number in a search range is calculated, SSE indexes and cluster analysis CH indexes are calculated, and the clustering number corresponding to the maximum CH index is found near the inflection point of the line graph which tends to be gentle and corresponds to the SSE indexes and is used as the optimal clustering number.
8. The utility model provides a generating device of many new forms of energy base power combination scene which characterized in that includes: The prediction module is used for inputting the historical output data of the multiple new energy bases into the constructed seasonal differential autoregressive moving average SARIMA model so that the SARIMA model outputs output prediction data; The conversion module is used for drawing a multi-element frequency histogram by utilizing an edge probability distribution function corresponding to the output prediction data; The construction module is used for selecting an optimal Copula function according to the tail characteristics of the multi-element frequency histogram so as to construct a SARIMA-Copula model; The processing module is used for generating a random point output sequence of the multi-new energy base at each moment by utilizing the SARIMA-Copula model, and performing inverse operation on an edge distribution function corresponding to the random point output sequence to obtain an output combined scene set of the multi-new energy base; and the reduction module is used for reducing the output combined scene set of the multi-new-energy base to obtain a typical scene set.
9. An electronic device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any one of claims 1 to 7 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.

Description

Method and device for generating multi-new-energy-source base power-output combined scene Technical Field The invention belongs to the technical field of electrical engineering, and particularly relates to a method and a device for generating a multi-new-energy-source base power-output combined scene. Background With the acceleration of the global energy structure to the deep transformation in the low carbonization and cleaning directions, new energy power generation technologies represented by wind energy and solar energy photovoltaics are rapidly developed, the installed capacity and the power generation duty ratio in a power system are continuously and rapidly increased, and the new energy power generation technology is gradually one of main power supplies. However, new energy power generation is highly dependent on meteorological resources, and its output exhibits significant intermittence, strong volatility, and inherent uncertainty. These inherent characteristics fundamentally change the traditional "source-follower" operating mode of the power system, and bring serious challenges to long-term planning, medium-term scheduling and real-time operation of the system in all directions and multiple time scales. Especially, in the background of regional power grid interconnection and trans-regional power transmission, multiple new energy bases often need to operate jointly to realize resource complementation and optimal configuration. In this case, the output characteristics of the bases in different geographic positions are not independent of each other, but have complex time-space correlation. For example, under the influence of the same large weather system, wind power stations within hundreds of kilometers can simultaneously experience output peaks or valleys to form positive correlation in space so as to exacerbate the power fluctuation of the whole network, and wind and light resources in different climatic regions can show complementarity or negative correlation in time so as to bring potential opportunity and coordination difficulty to system balance. The space-time correlation structure makes the joint output behavior of a plurality of new energy bases far more complex than that of single site superposition, and the probability distribution of the joint output behavior shows high-dimension nonlinear coupling characteristics. However, the conventional single scene generation method is difficult to accurately describe the joint probability distribution, so that the reliability of the power system optimization decision is reduced. Disclosure of Invention Aiming at the defects or improvement demands of the prior art, the invention provides a method and a device for generating a multi-new-energy-source base power-output combined scene, and aims to solve the technical problem that the reliability of an optimization decision of a power system is reduced due to the fact that the traditional single scene generation method is difficult to accurately describe the combined probability distribution. In order to achieve the above object, according to one aspect of the present invention, there is provided a method for generating a multi-new energy base power combining scene, including: s1, inputting historical output data of multiple new energy bases into a constructed seasonal differential autoregressive moving average SARIMA model so that the SARIMA model outputs output prediction data; s2, drawing a multi-element frequency histogram by utilizing an edge probability distribution function corresponding to the output prediction data; s3, selecting an optimal Copula function according to tail characteristics of the multi-element frequency histogram to construct a SARIMA-Copula model; s4, generating a random point output sequence of the multi-new energy base at each moment by utilizing the SARIMA-Copula model, and performing inverse operation on an edge distribution function corresponding to the random point output sequence to obtain an output combined scene set of the multi-new energy base; S5, reducing the output combined scene set of the multi-new-energy base to obtain a typical scene set. Further, the selecting an optimal Copula function according to the tail characteristic of the multi-element frequency histogram to construct a SARIMA-Copula model includes: If the tail characteristic of the multi-element frequency histogram of the output prediction data is symmetrical tail and tail progressive independent characteristic, selecting a Gaussian Copula function as the optimal Copula function; If the tail characteristic of the multi-element frequency histogram of the output prediction data is symmetrical tail and tail progressive correlation characteristic, selecting a t-Copula function as the optimal Copula function; If the tail characteristic of the multi-element frequency histogram of the output prediction data is an asymmetric tail and is sensitive to an upper tail or a lower tail, selecting a Gumbel Copula fun