{"title":"最优潮流的机会约束调谐","authors":"Ashley M. Hou, Line A. Roald","doi":"10.1109/PMAPS47429.2020.9183552","DOIUrl":null,"url":null,"abstract":"In this paper, we consider a chance-constrained formulation of the optimal power flow problem to handle uncertainties resulting from renewable generation and load variability. We propose a tuning method that iterates between solving an approximated reformulation of the optimization problem and using a posteriori sample-based evaluations to refine the reformulation. Our method is applicable to both single and joint chance constraints and does not rely on any distributional assumptions on the uncertainty. In a case study for the IEEE 24-bus system, we demonstrate that our method is computationally efficient and enforces chance constraints without over-conservatism.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Chance Constraint Tuning for Optimal Power Flow\",\"authors\":\"Ashley M. Hou, Line A. Roald\",\"doi\":\"10.1109/PMAPS47429.2020.9183552\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we consider a chance-constrained formulation of the optimal power flow problem to handle uncertainties resulting from renewable generation and load variability. We propose a tuning method that iterates between solving an approximated reformulation of the optimization problem and using a posteriori sample-based evaluations to refine the reformulation. Our method is applicable to both single and joint chance constraints and does not rely on any distributional assumptions on the uncertainty. In a case study for the IEEE 24-bus system, we demonstrate that our method is computationally efficient and enforces chance constraints without over-conservatism.\",\"PeriodicalId\":126918,\"journal\":{\"name\":\"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-05-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PMAPS47429.2020.9183552\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PMAPS47429.2020.9183552","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
In this paper, we consider a chance-constrained formulation of the optimal power flow problem to handle uncertainties resulting from renewable generation and load variability. We propose a tuning method that iterates between solving an approximated reformulation of the optimization problem and using a posteriori sample-based evaluations to refine the reformulation. Our method is applicable to both single and joint chance constraints and does not rely on any distributional assumptions on the uncertainty. In a case study for the IEEE 24-bus system, we demonstrate that our method is computationally efficient and enforces chance constraints without over-conservatism.