{"title":"Analysis of power demand response for smart home in Germany from end-user's perspective based on high resolution power demand model","authors":"N. H. Mallick, M. H. Maruf","doi":"10.1109/CEEICT.2016.7873071","DOIUrl":null,"url":null,"abstract":"This study forecasts the load profile of Germany for household scenarios till 2050 based on Markov chain process. Taking into account the penetration level of photovoltaic (PV) solar technology, technological improvement of efficient home appliances, battery support, load management and demand response scheduling, a high resolution smart home demand profile is created. At the end, combining of all, a highly annual aggregated demand profile is produced which shows daily variations on demand load profile for fixed tariff scenarios up to 44% with respect to the present demand profile. On the other hand it is found that, Time of Use (TOU) tariff shows a variation of 64% with respect to the present demand profile.","PeriodicalId":240329,"journal":{"name":"2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEEICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CEEICT.2016.7873071","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
This study forecasts the load profile of Germany for household scenarios till 2050 based on Markov chain process. Taking into account the penetration level of photovoltaic (PV) solar technology, technological improvement of efficient home appliances, battery support, load management and demand response scheduling, a high resolution smart home demand profile is created. At the end, combining of all, a highly annual aggregated demand profile is produced which shows daily variations on demand load profile for fixed tariff scenarios up to 44% with respect to the present demand profile. On the other hand it is found that, Time of Use (TOU) tariff shows a variation of 64% with respect to the present demand profile.