Arpan Koirala, T. Acker, D. Van Hertem, Juliano Camargo, R. D’hulst
{"title":"分配系统概率评估的一般多项式混沌与粗糙蒙特卡罗","authors":"Arpan Koirala, T. Acker, D. Van Hertem, Juliano Camargo, R. D’hulst","doi":"10.1109/PMAPS47429.2020.9183453","DOIUrl":null,"url":null,"abstract":"Recent evolutions in low voltage distribution system (LVDS), e.g., distributed generation and electric vehicles, have introduced a higher level of uncertainty. To determine the probability of violating grid constraints, e.g., undervoltage, such system must be assessed using a probabilistic power flow, which considers these uncertainties. Several approaches exist, including simulation-based and analytical methods. A well-known example of the simulation-based methods is the crude Monte Carlo (MC) approach which is very common in scientific computation due to its simplicity. Recently, analytical methods such as the general polynomial chaos (gPC) approach have gained increasing interest. This paper illustrates the effectiveness of the gPC approach compared to the MC method in determining the uncertainty of certain grid measures. Both methods are compared with respect to computational time and accuracy using a small test case with stochastic input which coheres to a univariate continuous distribution.","PeriodicalId":126918,"journal":{"name":"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"General Polynomial Chaos vs Crude Monte Carlo for Probabilistic Evaluation of Distribution Systems\",\"authors\":\"Arpan Koirala, T. Acker, D. Van Hertem, Juliano Camargo, R. D’hulst\",\"doi\":\"10.1109/PMAPS47429.2020.9183453\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recent evolutions in low voltage distribution system (LVDS), e.g., distributed generation and electric vehicles, have introduced a higher level of uncertainty. To determine the probability of violating grid constraints, e.g., undervoltage, such system must be assessed using a probabilistic power flow, which considers these uncertainties. Several approaches exist, including simulation-based and analytical methods. A well-known example of the simulation-based methods is the crude Monte Carlo (MC) approach which is very common in scientific computation due to its simplicity. Recently, analytical methods such as the general polynomial chaos (gPC) approach have gained increasing interest. This paper illustrates the effectiveness of the gPC approach compared to the MC method in determining the uncertainty of certain grid measures. Both methods are compared with respect to computational time and accuracy using a small test case with stochastic input which coheres to a univariate continuous distribution.\",\"PeriodicalId\":126918,\"journal\":{\"name\":\"2020 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"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/PMAPS47429.2020.9183453\",\"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/PMAPS47429.2020.9183453","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
General Polynomial Chaos vs Crude Monte Carlo for Probabilistic Evaluation of Distribution Systems
Recent evolutions in low voltage distribution system (LVDS), e.g., distributed generation and electric vehicles, have introduced a higher level of uncertainty. To determine the probability of violating grid constraints, e.g., undervoltage, such system must be assessed using a probabilistic power flow, which considers these uncertainties. Several approaches exist, including simulation-based and analytical methods. A well-known example of the simulation-based methods is the crude Monte Carlo (MC) approach which is very common in scientific computation due to its simplicity. Recently, analytical methods such as the general polynomial chaos (gPC) approach have gained increasing interest. This paper illustrates the effectiveness of the gPC approach compared to the MC method in determining the uncertainty of certain grid measures. Both methods are compared with respect to computational time and accuracy using a small test case with stochastic input which coheres to a univariate continuous distribution.