{"title":"用于管理和电气化规划的智能微电网运行模拟器","authors":"J. Thornburg, T. Ustun, B. Krogh","doi":"10.1109/POWERAFRICA.2016.7556558","DOIUrl":null,"url":null,"abstract":"Smart grid technology that enables fine-grained monitoring and control of electrical power systems makes it possible to envision new operating strategies and even new business models. Possibilities for leveraging these smart grid tools in the developing world are being explored, particularly with emerging microgrids for rural electrification. In these off-grid systems, smart meters at the residential level are being used to manage the delivery of affordable electricity through novel uses of mobile payments and direct demand-side control at the customer level. In contrast to traditional power systems in the developed world, the available total supply in microgrids, which can incorporate multiple types of generation, is not always sufficient to meet the aggregate demand, even under normal operating conditions. Moreover, both the available supply and the demand are highly variable, limiting the value of deterministic analyses. This paper introduces a simulation tool for assessing the performance of these systems using probabilistic models of supply and demand. A key feature of the tool is the use of stochastic models for the individual loads and supplies, which are aggregated precisely to obtain stochastic models of the system-level behavior. This makes it possible to evaluate and compare system performance for different operating and business strategies that take advantage of the capabilities for fine-grained control. The paper describes the simulator inputs, computations and illustrative results for a case study.","PeriodicalId":177444,"journal":{"name":"2016 IEEE PES PowerAfrica","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":"{\"title\":\"Smart microgrid operation simulator for management and electrification planning\",\"authors\":\"J. Thornburg, T. Ustun, B. Krogh\",\"doi\":\"10.1109/POWERAFRICA.2016.7556558\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Smart grid technology that enables fine-grained monitoring and control of electrical power systems makes it possible to envision new operating strategies and even new business models. Possibilities for leveraging these smart grid tools in the developing world are being explored, particularly with emerging microgrids for rural electrification. In these off-grid systems, smart meters at the residential level are being used to manage the delivery of affordable electricity through novel uses of mobile payments and direct demand-side control at the customer level. In contrast to traditional power systems in the developed world, the available total supply in microgrids, which can incorporate multiple types of generation, is not always sufficient to meet the aggregate demand, even under normal operating conditions. Moreover, both the available supply and the demand are highly variable, limiting the value of deterministic analyses. This paper introduces a simulation tool for assessing the performance of these systems using probabilistic models of supply and demand. A key feature of the tool is the use of stochastic models for the individual loads and supplies, which are aggregated precisely to obtain stochastic models of the system-level behavior. This makes it possible to evaluate and compare system performance for different operating and business strategies that take advantage of the capabilities for fine-grained control. The paper describes the simulator inputs, computations and illustrative results for a case study.\",\"PeriodicalId\":177444,\"journal\":{\"name\":\"2016 IEEE PES PowerAfrica\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"20\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE PES PowerAfrica\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/POWERAFRICA.2016.7556558\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE PES PowerAfrica","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/POWERAFRICA.2016.7556558","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Smart microgrid operation simulator for management and electrification planning
Smart grid technology that enables fine-grained monitoring and control of electrical power systems makes it possible to envision new operating strategies and even new business models. Possibilities for leveraging these smart grid tools in the developing world are being explored, particularly with emerging microgrids for rural electrification. In these off-grid systems, smart meters at the residential level are being used to manage the delivery of affordable electricity through novel uses of mobile payments and direct demand-side control at the customer level. In contrast to traditional power systems in the developed world, the available total supply in microgrids, which can incorporate multiple types of generation, is not always sufficient to meet the aggregate demand, even under normal operating conditions. Moreover, both the available supply and the demand are highly variable, limiting the value of deterministic analyses. This paper introduces a simulation tool for assessing the performance of these systems using probabilistic models of supply and demand. A key feature of the tool is the use of stochastic models for the individual loads and supplies, which are aggregated precisely to obtain stochastic models of the system-level behavior. This makes it possible to evaluate and compare system performance for different operating and business strategies that take advantage of the capabilities for fine-grained control. The paper describes the simulator inputs, computations and illustrative results for a case study.