Optimization of Residential Demand Response Program Cost with
  Consideration for Occupants Thermal Comfort and Privacy
Reza Nematirad M. M. Ardehali Amir Khorsandi
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.