Optimization of Residential Demand Response Program Cost with Consideration for Occupants Thermal Comfort and Privacy
This addresses the problem of balancing cost savings with occupant comfort and privacy in residential energy management for smart home users, representing an incremental improvement with specific methodological enhancements.
This study developed a multi-objective simulation model for residential demand response programs that optimizes costs while considering occupants' thermal comfort and privacy, using a non-intrusive occupancy forecasting approach. Results showed that considering uncertainty increased costs by 36% but using demand response reduced demand by shifting appliance operations and lowered costs by 13.2%.
Residential consumers can use the demand response program (DRP) if they can utilize the home energy management system (HEMS), which reduces consumer costs by automatically adjusting air conditioning (AC) setpoints and shifting some appliances to off-peak hours. If HEMS knows occupancy status, consumers can gain more economic benefits and thermal comfort. However, for the building occupancy status, direct sensing is costly, inaccurate, and intrusive for residents. So, forecasting algorithms could serve as an effective alternative. The goal of this study is to present a non-intrusive, accurate, and cost-effective approach, to develop a multi-objective simulation model for the application of DRPs in a smart residential house, where (a) electrical load demand reduction, (b) adjustment in thermal comfort (AC) temperature setpoints, and (c) , worst cases scenario approach is very conservative. Because that is unlikely all uncertain parameters take their worst values at all times. So, the flexible robust counterpart optimization along with uncertainty budgets is developed to consider uncertainty realistically. Simulated results indicate that considering uncertainty increases the costs by 36 percent and decreases the AC temperature setpoints. Besides, using DRPs reduces demand by shifting some appliance operations to off-peak hours and lowers costs by 13.2 percent.