Li Jing, Zhou Xiangyu, Li Tao, Liu Yue, Wu Qinghua
{"title":"Integrated optimization of smart home appliances under energy management system","authors":"Li Jing, Zhou Xiangyu, Li Tao, Liu Yue, Wu Qinghua","doi":"10.1109/DTPI55838.2022.9998973","DOIUrl":null,"url":null,"abstract":"Smart appliance operation optimization enables consumers to control and schedule the operation time of home appliances, minimize energy costs, peak-to-average ratio (PAR), and avoid peak load demands. In this paper, a general architecture of a home energy management system is developed in a smart electricity consumption scenario, providing customers with a novel, energy-efficient scheduling method. The optimization problem is to optimize the energy saving of household appliances based on the time-of-use electricity pricing scheme. To optimize the formulated problem, this paper uses the Gurobi optimizer and compares it with the particle swarm optimization (PSO) algorithm to show its effectiveness. Rooftop photovoltaic (PV) systems are integrated with the system to show the cost-effectiveness of the equipment.","PeriodicalId":409822,"journal":{"name":"2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 2nd International Conference on Digital Twins and Parallel Intelligence (DTPI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DTPI55838.2022.9998973","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
Smart appliance operation optimization enables consumers to control and schedule the operation time of home appliances, minimize energy costs, peak-to-average ratio (PAR), and avoid peak load demands. In this paper, a general architecture of a home energy management system is developed in a smart electricity consumption scenario, providing customers with a novel, energy-efficient scheduling method. The optimization problem is to optimize the energy saving of household appliances based on the time-of-use electricity pricing scheme. To optimize the formulated problem, this paper uses the Gurobi optimizer and compares it with the particle swarm optimization (PSO) algorithm to show its effectiveness. Rooftop photovoltaic (PV) systems are integrated with the system to show the cost-effectiveness of the equipment.