{"title":"考虑风电电网可接受性的不规则分布变量的概率潮流计算","authors":"Yu Jie, Haoyu Qi, Shikun Qiu, Xiang Wang, Huiling Zhang","doi":"10.1109/IPEMC.2016.7512506","DOIUrl":null,"url":null,"abstract":"When the wind farm is planned to be constructed, making full use of wind power resources is mainly considered while the acceptable capacity of wind power integrated into tie line is taken insufficient account, causing that the wind power would not be accepted completely and the wind power forecast is limited by the acceptable capacity of tie line. Under the current operation mode, the next-day network load flow is based on the wind power forecast. Because of the inevitable forecast errors in wind generation, the wind power probability distribution would always be given. Considering the acceptable capacity of power grid, the wind power is fitted to distribute irregularly. The irregular distribution input does not satisfy the demand of classical numerical probabilistic load flow calculation. This paper proposes a probabilistic load flow calculation method based on proportional allocation principle and Latin hypercube sampling, which ensures high calculation precision and improves the speed dramatically. The veracity and validity of the method proposed is tested in the IEEE 118-bus system. The irregular probability distribution of wind power is fitted under the restriction of tie line and the simulation results show that the method proposed also has excellent practical value.","PeriodicalId":6857,"journal":{"name":"2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia)","volume":"24 1","pages":"1457-1461"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Probabilistic load flow calculation with irregular distribution variables considering power grid receivability of wind power generation\",\"authors\":\"Yu Jie, Haoyu Qi, Shikun Qiu, Xiang Wang, Huiling Zhang\",\"doi\":\"10.1109/IPEMC.2016.7512506\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"When the wind farm is planned to be constructed, making full use of wind power resources is mainly considered while the acceptable capacity of wind power integrated into tie line is taken insufficient account, causing that the wind power would not be accepted completely and the wind power forecast is limited by the acceptable capacity of tie line. Under the current operation mode, the next-day network load flow is based on the wind power forecast. Because of the inevitable forecast errors in wind generation, the wind power probability distribution would always be given. Considering the acceptable capacity of power grid, the wind power is fitted to distribute irregularly. The irregular distribution input does not satisfy the demand of classical numerical probabilistic load flow calculation. This paper proposes a probabilistic load flow calculation method based on proportional allocation principle and Latin hypercube sampling, which ensures high calculation precision and improves the speed dramatically. The veracity and validity of the method proposed is tested in the IEEE 118-bus system. The irregular probability distribution of wind power is fitted under the restriction of tie line and the simulation results show that the method proposed also has excellent practical value.\",\"PeriodicalId\":6857,\"journal\":{\"name\":\"2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia)\",\"volume\":\"24 1\",\"pages\":\"1457-1461\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IPEMC.2016.7512506\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IPEMC.2016.7512506","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Probabilistic load flow calculation with irregular distribution variables considering power grid receivability of wind power generation
When the wind farm is planned to be constructed, making full use of wind power resources is mainly considered while the acceptable capacity of wind power integrated into tie line is taken insufficient account, causing that the wind power would not be accepted completely and the wind power forecast is limited by the acceptable capacity of tie line. Under the current operation mode, the next-day network load flow is based on the wind power forecast. Because of the inevitable forecast errors in wind generation, the wind power probability distribution would always be given. Considering the acceptable capacity of power grid, the wind power is fitted to distribute irregularly. The irregular distribution input does not satisfy the demand of classical numerical probabilistic load flow calculation. This paper proposes a probabilistic load flow calculation method based on proportional allocation principle and Latin hypercube sampling, which ensures high calculation precision and improves the speed dramatically. The veracity and validity of the method proposed is tested in the IEEE 118-bus system. The irregular probability distribution of wind power is fitted under the restriction of tie line and the simulation results show that the method proposed also has excellent practical value.