{"title":"基于交叉熵法的可再生能源投资组合规划","authors":"Clares Loren C. Jalocon, A. Nerves","doi":"10.1109/APPEEC.2013.6837305","DOIUrl":null,"url":null,"abstract":"The study presents a method for renewable energy portfolio planning through a Monte-Carlo optimization approach called the Cross Entropy method. Renewable energy portfolio planning is applied to the Philippine setting by considering multiple factors composed of economic and environmental metrics such as capital and production costs, and the corresponding cost for emissions. Policy mechanisms such as the Renewable Portfolio Standard (RPS) and carbon-cap are policy mechanisms that can also be incorporated in the model. For various scenarios of the economic and environmental factors and policy mechanisms, the study yielded the corresponding optimum investment and operation schedules for the planned generating plants.","PeriodicalId":330524,"journal":{"name":"2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Renewable energy portfolio planning using the cross-entropy method\",\"authors\":\"Clares Loren C. Jalocon, A. Nerves\",\"doi\":\"10.1109/APPEEC.2013.6837305\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The study presents a method for renewable energy portfolio planning through a Monte-Carlo optimization approach called the Cross Entropy method. Renewable energy portfolio planning is applied to the Philippine setting by considering multiple factors composed of economic and environmental metrics such as capital and production costs, and the corresponding cost for emissions. Policy mechanisms such as the Renewable Portfolio Standard (RPS) and carbon-cap are policy mechanisms that can also be incorporated in the model. For various scenarios of the economic and environmental factors and policy mechanisms, the study yielded the corresponding optimum investment and operation schedules for the planned generating plants.\",\"PeriodicalId\":330524,\"journal\":{\"name\":\"2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)\",\"volume\":\"12 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/APPEEC.2013.6837305\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/APPEEC.2013.6837305","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Renewable energy portfolio planning using the cross-entropy method
The study presents a method for renewable energy portfolio planning through a Monte-Carlo optimization approach called the Cross Entropy method. Renewable energy portfolio planning is applied to the Philippine setting by considering multiple factors composed of economic and environmental metrics such as capital and production costs, and the corresponding cost for emissions. Policy mechanisms such as the Renewable Portfolio Standard (RPS) and carbon-cap are policy mechanisms that can also be incorporated in the model. For various scenarios of the economic and environmental factors and policy mechanisms, the study yielded the corresponding optimum investment and operation schedules for the planned generating plants.