{"title":"能源灵活型笔记本电脑:使用传感器/执行器网络的移动插头负载的需求响应","authors":"N. Murthy","doi":"10.1109/SmartGridComm.2012.6486048","DOIUrl":null,"url":null,"abstract":"This paper explores demand response techniques for managing mobile, distributed loads with on-board electrochemical energy storage over a plug-level sensing/actuating wireless mesh network. We target laptops and construct a power consumption and battery charging model from measurements obtained across a variety of such devices. We then build simulations of charging patterns using the most general cases we observed. Our first simulation study explores a classic demand response scenario in which a large number of loads participate in a typical pre-scheduled demand response (DR) event. We show that we can achieve load curtailments in the range of 30-90% of aggregate baseline load as a function of the duration of a DR event by managing the charging schedules of laptops that randomly enter and leave the control jurisdiction of a DR event participant. In a second simulation study, we investigate a continuous demand response scenario in which charging schedules respond to a fluctuating renewable electricity supply (e.g. wind or solar) and show that we can reduce grid dependence by 26.8-33.8% compared to oblivious charging.","PeriodicalId":143915,"journal":{"name":"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Energy-agile laptops: Demand response of mobile plug loads using sensor/actuator networks\",\"authors\":\"N. Murthy\",\"doi\":\"10.1109/SmartGridComm.2012.6486048\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper explores demand response techniques for managing mobile, distributed loads with on-board electrochemical energy storage over a plug-level sensing/actuating wireless mesh network. We target laptops and construct a power consumption and battery charging model from measurements obtained across a variety of such devices. We then build simulations of charging patterns using the most general cases we observed. Our first simulation study explores a classic demand response scenario in which a large number of loads participate in a typical pre-scheduled demand response (DR) event. We show that we can achieve load curtailments in the range of 30-90% of aggregate baseline load as a function of the duration of a DR event by managing the charging schedules of laptops that randomly enter and leave the control jurisdiction of a DR event participant. In a second simulation study, we investigate a continuous demand response scenario in which charging schedules respond to a fluctuating renewable electricity supply (e.g. wind or solar) and show that we can reduce grid dependence by 26.8-33.8% compared to oblivious charging.\",\"PeriodicalId\":143915,\"journal\":{\"name\":\"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SmartGridComm.2012.6486048\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SmartGridComm.2012.6486048","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Energy-agile laptops: Demand response of mobile plug loads using sensor/actuator networks
This paper explores demand response techniques for managing mobile, distributed loads with on-board electrochemical energy storage over a plug-level sensing/actuating wireless mesh network. We target laptops and construct a power consumption and battery charging model from measurements obtained across a variety of such devices. We then build simulations of charging patterns using the most general cases we observed. Our first simulation study explores a classic demand response scenario in which a large number of loads participate in a typical pre-scheduled demand response (DR) event. We show that we can achieve load curtailments in the range of 30-90% of aggregate baseline load as a function of the duration of a DR event by managing the charging schedules of laptops that randomly enter and leave the control jurisdiction of a DR event participant. In a second simulation study, we investigate a continuous demand response scenario in which charging schedules respond to a fluctuating renewable electricity supply (e.g. wind or solar) and show that we can reduce grid dependence by 26.8-33.8% compared to oblivious charging.