{"title":"基于SOM-Kmeans算法的自备电厂参与新能源消纳状态判断方法","authors":"Jijun Dong, Zhijun Bai, Xiaohua Liu, Hangming Liu, D. Peng, Huirong Zhao, Mingming Pan, Jindou Yuan","doi":"10.1109/CEECT55960.2022.10030132","DOIUrl":null,"url":null,"abstract":"The high proportion of fluctuating renewable energy connected to the grid puts forward new requirements for the stability and flexibility of the power system. Under the environment of supply side structural change and new electricity reform, how to coordinate the development of self-owned power plants and power grids faces new challenges. The judgment of the conditions for the participation of self-owned power plants in new energy consumption is an important prerequisite for the source-network interaction of the power plant. Therefore, this paper proposes a method based on SOM-Kmeans algorithm to judge the participation conditions of self-owned power plants in the interactive scene of new energy consumption. Firstly, based on the daily wind speed and illumination intensity data within a year, the sunrise force data of fans and photovoltaic power generation units are obtained through the new energy output model. Secondly, the new energy output scenes are clustered and cut, and the typical new energy output scenes are obtained based on the SOM-Kmeans algorithm. Finally, the typical output scenes are analyzed, and the response quantity and response time conditions of the self-supplied power plant are judged by combining the day-ahead generation plan data of the self-supplied power plant.","PeriodicalId":187017,"journal":{"name":"2022 4th International Conference on Electrical Engineering and Control Technologies (CEECT)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Condition Judgment Method of Self-owned Power Plant Participating in New Energy Consumption Based on SOM-Kmeans Algorithm\",\"authors\":\"Jijun Dong, Zhijun Bai, Xiaohua Liu, Hangming Liu, D. Peng, Huirong Zhao, Mingming Pan, Jindou Yuan\",\"doi\":\"10.1109/CEECT55960.2022.10030132\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The high proportion of fluctuating renewable energy connected to the grid puts forward new requirements for the stability and flexibility of the power system. Under the environment of supply side structural change and new electricity reform, how to coordinate the development of self-owned power plants and power grids faces new challenges. The judgment of the conditions for the participation of self-owned power plants in new energy consumption is an important prerequisite for the source-network interaction of the power plant. Therefore, this paper proposes a method based on SOM-Kmeans algorithm to judge the participation conditions of self-owned power plants in the interactive scene of new energy consumption. Firstly, based on the daily wind speed and illumination intensity data within a year, the sunrise force data of fans and photovoltaic power generation units are obtained through the new energy output model. Secondly, the new energy output scenes are clustered and cut, and the typical new energy output scenes are obtained based on the SOM-Kmeans algorithm. Finally, the typical output scenes are analyzed, and the response quantity and response time conditions of the self-supplied power plant are judged by combining the day-ahead generation plan data of the self-supplied power plant.\",\"PeriodicalId\":187017,\"journal\":{\"name\":\"2022 4th International Conference on Electrical Engineering and Control Technologies (CEECT)\",\"volume\":\"95 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 4th International Conference on Electrical Engineering and Control Technologies (CEECT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CEECT55960.2022.10030132\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 4th International Conference on Electrical Engineering and Control Technologies (CEECT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEECT55960.2022.10030132","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Condition Judgment Method of Self-owned Power Plant Participating in New Energy Consumption Based on SOM-Kmeans Algorithm
The high proportion of fluctuating renewable energy connected to the grid puts forward new requirements for the stability and flexibility of the power system. Under the environment of supply side structural change and new electricity reform, how to coordinate the development of self-owned power plants and power grids faces new challenges. The judgment of the conditions for the participation of self-owned power plants in new energy consumption is an important prerequisite for the source-network interaction of the power plant. Therefore, this paper proposes a method based on SOM-Kmeans algorithm to judge the participation conditions of self-owned power plants in the interactive scene of new energy consumption. Firstly, based on the daily wind speed and illumination intensity data within a year, the sunrise force data of fans and photovoltaic power generation units are obtained through the new energy output model. Secondly, the new energy output scenes are clustered and cut, and the typical new energy output scenes are obtained based on the SOM-Kmeans algorithm. Finally, the typical output scenes are analyzed, and the response quantity and response time conditions of the self-supplied power plant are judged by combining the day-ahead generation plan data of the self-supplied power plant.