{"title":"可再生能源配电网的概率可用输电能力评估","authors":"Hao Sheng, Xiaozhe Wang","doi":"10.1109/EPEC.2017.8286215","DOIUrl":null,"url":null,"abstract":"Rapid increase of renewable energy sources and electric vehicles in utility distribution feeders introduces more and more uncertainties. To investigate how such uncertainties may affect the available delivery capability (ADC) of the distribution network, it is imperative to employ a probabilistic analysis framework. In this paper, a formulation for probabilistic ADC incorporating renewable generators and load variations is proposed; a computationally efficient method to solve the probabilistic ADC is presented, which combines the up-to-date sparse polynomial chaos expansion (PCE) and the continuation method. A numerical example in the IEEE 13 node test feeder is given to demonstrate the accuracy and efficiency of the proposed method.","PeriodicalId":141250,"journal":{"name":"2017 IEEE Electrical Power and Energy Conference (EPEC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Probabilistic available delivery capability assessment of general distribution network with renewables\",\"authors\":\"Hao Sheng, Xiaozhe Wang\",\"doi\":\"10.1109/EPEC.2017.8286215\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rapid increase of renewable energy sources and electric vehicles in utility distribution feeders introduces more and more uncertainties. To investigate how such uncertainties may affect the available delivery capability (ADC) of the distribution network, it is imperative to employ a probabilistic analysis framework. In this paper, a formulation for probabilistic ADC incorporating renewable generators and load variations is proposed; a computationally efficient method to solve the probabilistic ADC is presented, which combines the up-to-date sparse polynomial chaos expansion (PCE) and the continuation method. A numerical example in the IEEE 13 node test feeder is given to demonstrate the accuracy and efficiency of the proposed method.\",\"PeriodicalId\":141250,\"journal\":{\"name\":\"2017 IEEE Electrical Power and Energy Conference (EPEC)\",\"volume\":\"66 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 IEEE Electrical Power and Energy Conference (EPEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EPEC.2017.8286215\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE Electrical Power and Energy Conference (EPEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EPEC.2017.8286215","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Probabilistic available delivery capability assessment of general distribution network with renewables
Rapid increase of renewable energy sources and electric vehicles in utility distribution feeders introduces more and more uncertainties. To investigate how such uncertainties may affect the available delivery capability (ADC) of the distribution network, it is imperative to employ a probabilistic analysis framework. In this paper, a formulation for probabilistic ADC incorporating renewable generators and load variations is proposed; a computationally efficient method to solve the probabilistic ADC is presented, which combines the up-to-date sparse polynomial chaos expansion (PCE) and the continuation method. A numerical example in the IEEE 13 node test feeder is given to demonstrate the accuracy and efficiency of the proposed method.