S. Wogrin, D. Tejada-Arango, S. Pineda, J. Morales
{"title":"什么样的时间段聚合方法最适合可再生能源和储能的电力系统运行模式?","authors":"S. Wogrin, D. Tejada-Arango, S. Pineda, J. Morales","doi":"10.1109/SEST.2019.8849027","DOIUrl":null,"url":null,"abstract":"In this paper we compare two cutting-edge time-period aggregation methodologies for power system models that consider both renewables and storage technologies: the chronological time-period clustering; and, the enhanced representative period approach. Such methodologies are used in order to reduce the computational burden of highly complex optimization models while not compromising the quality of the results. With this paper, we identify which method works best, and under which conditions, in order to reproduce the outcomes of the hourly benchmark model.","PeriodicalId":158839,"journal":{"name":"2019 International Conference on Smart Energy Systems and Technologies (SEST)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"What time-period aggregation method works best for power system operation models with renewables and storage?\",\"authors\":\"S. Wogrin, D. Tejada-Arango, S. Pineda, J. Morales\",\"doi\":\"10.1109/SEST.2019.8849027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we compare two cutting-edge time-period aggregation methodologies for power system models that consider both renewables and storage technologies: the chronological time-period clustering; and, the enhanced representative period approach. Such methodologies are used in order to reduce the computational burden of highly complex optimization models while not compromising the quality of the results. With this paper, we identify which method works best, and under which conditions, in order to reproduce the outcomes of the hourly benchmark model.\",\"PeriodicalId\":158839,\"journal\":{\"name\":\"2019 International Conference on Smart Energy Systems and Technologies (SEST)\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 International Conference on Smart Energy Systems and Technologies (SEST)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SEST.2019.8849027\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 International Conference on Smart Energy Systems and Technologies (SEST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SEST.2019.8849027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
What time-period aggregation method works best for power system operation models with renewables and storage?
In this paper we compare two cutting-edge time-period aggregation methodologies for power system models that consider both renewables and storage technologies: the chronological time-period clustering; and, the enhanced representative period approach. Such methodologies are used in order to reduce the computational burden of highly complex optimization models while not compromising the quality of the results. With this paper, we identify which method works best, and under which conditions, in order to reproduce the outcomes of the hourly benchmark model.