{"title":"考虑用户身份和出行行为异质性的空调集群综合负荷评估","authors":"L. Li, Chaoliang Wang, Songsong Chen","doi":"10.1109/EI256261.2022.10117444","DOIUrl":null,"url":null,"abstract":"The diversification of user needs and the heterogeneity and uncertainty of response behaviors affect the rational use of demand-side resources. In order to more accurately evaluate the demand response behavior of different users and more accurately evaluate the aggregate load of air-conditioning clusters, this paper proposes an air-conditioning cluster load evaluation based on user feature classification considering the uncertainty of user behavior. First, the equivalent thermal parameter (ETP) model of the room is given in combination with the operation mode of the inverter air conditioner and the thermal insulation performance of the house, On the basis of this model, the load model of a single inverter air conditioner is given in combination with the electrical quantity relationship of the inverter air conditioner. Secondly, consider user classification and travel behavior combined with user thermal comfort in the family as a unit, and establish a temperature decision model for different family members at different times. Thirdly, the income level and environmental protection participation of different households are introduced to combine the temperature decision-making model to obtain a 24-hour daily household air-conditioning temperature decision-making model. Combined with the air-conditioning load model, an accurate air-conditioning load aggregation model is obtained. Finally, it is verified that the accuracy of this model is better than the traditional model in the past.","PeriodicalId":413409,"journal":{"name":"2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Aggregated Load Assessment of Air-Conditioning Clusters Considering the Heterogeneity of User Identity and Travel Behavior\",\"authors\":\"L. Li, Chaoliang Wang, Songsong Chen\",\"doi\":\"10.1109/EI256261.2022.10117444\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The diversification of user needs and the heterogeneity and uncertainty of response behaviors affect the rational use of demand-side resources. In order to more accurately evaluate the demand response behavior of different users and more accurately evaluate the aggregate load of air-conditioning clusters, this paper proposes an air-conditioning cluster load evaluation based on user feature classification considering the uncertainty of user behavior. First, the equivalent thermal parameter (ETP) model of the room is given in combination with the operation mode of the inverter air conditioner and the thermal insulation performance of the house, On the basis of this model, the load model of a single inverter air conditioner is given in combination with the electrical quantity relationship of the inverter air conditioner. Secondly, consider user classification and travel behavior combined with user thermal comfort in the family as a unit, and establish a temperature decision model for different family members at different times. Thirdly, the income level and environmental protection participation of different households are introduced to combine the temperature decision-making model to obtain a 24-hour daily household air-conditioning temperature decision-making model. Combined with the air-conditioning load model, an accurate air-conditioning load aggregation model is obtained. Finally, it is verified that the accuracy of this model is better than the traditional model in the past.\",\"PeriodicalId\":413409,\"journal\":{\"name\":\"2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EI256261.2022.10117444\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EI256261.2022.10117444","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Aggregated Load Assessment of Air-Conditioning Clusters Considering the Heterogeneity of User Identity and Travel Behavior
The diversification of user needs and the heterogeneity and uncertainty of response behaviors affect the rational use of demand-side resources. In order to more accurately evaluate the demand response behavior of different users and more accurately evaluate the aggregate load of air-conditioning clusters, this paper proposes an air-conditioning cluster load evaluation based on user feature classification considering the uncertainty of user behavior. First, the equivalent thermal parameter (ETP) model of the room is given in combination with the operation mode of the inverter air conditioner and the thermal insulation performance of the house, On the basis of this model, the load model of a single inverter air conditioner is given in combination with the electrical quantity relationship of the inverter air conditioner. Secondly, consider user classification and travel behavior combined with user thermal comfort in the family as a unit, and establish a temperature decision model for different family members at different times. Thirdly, the income level and environmental protection participation of different households are introduced to combine the temperature decision-making model to obtain a 24-hour daily household air-conditioning temperature decision-making model. Combined with the air-conditioning load model, an accurate air-conditioning load aggregation model is obtained. Finally, it is verified that the accuracy of this model is better than the traditional model in the past.