Demand response for residential buildings based on dynamic price of electricity

Abstract

In the U.S., heating, ventilating and air conditioning (HVAC) systems are the largest consumers of electrical energy and a major contributor to peak demand. To reduce both peak load and energy cost, the set-point temperature of HVAC can be controlled depending on the electricity price. This paper presents a proposed controller that curtails peak load as well as saves electricity cost while maintaining reasonable thermal comfort. The controller changes set-point temperature when the retail price is higher than customers preset price. To evaluate the performance of the newly developed demand response controller, detailed energy models for two residential buildings are developed to analyze HVAC power consumption for different house sizes and floor plans. The house models are assumed to be located in Austin, Texas, USA and generated with OpenStudio and EnergyPlus. The design of internal load and occupation schedule are based on a residential energy consumption survey and experimental data by the Pecan Street Project, Austin, TX. In addition, historical data from Austin Energy for residential customers, 2012 is used to calibrate two house models. In addition, this paper uses the historical real-time wholesale price data for the Electricity Reliability Council of Texas (ERCOT) wholesale electricity market to model the two types of real-time tariffs that many utilities in the U.S. currently use to generate dynamic pricing for demand response programs. The simulation results show that our demand response controller could provide up to 10.8% of energy cost savings by using the proposed controller with dynamic pricing. While avoiding significant discomfort due to temperature change. Also, the results present potential for saving considering peak load by 24.7% and total electrical energy saving for HVAC in homes by 4.3% annually.