{"title":"电力市场模拟的概率技术性能比较","authors":"P. Fonseka, Z. Dong, T. K. Saha","doi":"10.1109/PES.2009.5275873","DOIUrl":null,"url":null,"abstract":"This paper compares the performances of two probabilistic techniques, namely (i) two-point estimate (2PE) method and (ii) Monte Carlo (MC) method, when modelling the uncertainties in market simulation. The demand, bid price, and capacity offer quantities are modelled as uncertain variables. A generalized zonal market model has been analysed with relaxed generation and network representation. A simplified version of National Electricity Market (NEM) of Australia has been studied and results are discussed.","PeriodicalId":258632,"journal":{"name":"2009 IEEE Power & Energy Society General Meeting","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A performance comparison of probabilistic techniques for electricity market simulation\",\"authors\":\"P. Fonseka, Z. Dong, T. K. Saha\",\"doi\":\"10.1109/PES.2009.5275873\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper compares the performances of two probabilistic techniques, namely (i) two-point estimate (2PE) method and (ii) Monte Carlo (MC) method, when modelling the uncertainties in market simulation. The demand, bid price, and capacity offer quantities are modelled as uncertain variables. A generalized zonal market model has been analysed with relaxed generation and network representation. A simplified version of National Electricity Market (NEM) of Australia has been studied and results are discussed.\",\"PeriodicalId\":258632,\"journal\":{\"name\":\"2009 IEEE Power & Energy Society General Meeting\",\"volume\":\"71 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-07-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE Power & Energy Society General Meeting\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PES.2009.5275873\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE Power & Energy Society General Meeting","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PES.2009.5275873","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A performance comparison of probabilistic techniques for electricity market simulation
This paper compares the performances of two probabilistic techniques, namely (i) two-point estimate (2PE) method and (ii) Monte Carlo (MC) method, when modelling the uncertainties in market simulation. The demand, bid price, and capacity offer quantities are modelled as uncertain variables. A generalized zonal market model has been analysed with relaxed generation and network representation. A simplified version of National Electricity Market (NEM) of Australia has been studied and results are discussed.
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