Reducing Residential Peak Electricity Demand with Mechanical Pre-Cooling of Building Thermal Mass

Abstract

This study used an advanced airflow, energy and humidity modelling tool to evaluate the potential for residential mechanical pre-cooling strategies to reduce peak electricity demand. Simulations were performed for a typical new home in all US DOE Climate Zones. The results show that the effectiveness of pre-cooling is highly dependent on climate zone and the selected pre-cooling strategy. The expected energy trade-off between cooling peak energy savings and increased off-peak energy use is also shown. Best pre-cooling results for most climates were obtained using a short pre-cooling time window with a high pre-cooling set point temperature. All pre-cooling strategies caused the annual cooling energy demand of the simulated buildings to increase. However, pre-cooling for long time periods with a low temperature set point can eliminate up to 97% of the annual peak cooling load of the building.

Assessing the time-sensitive impacts of energy efficiency and flexibility in the US building sector

“…reduction of cooling electricity usage during these hours [44,45]. We base the shift measure on a previous study [46], which simulates a mechanical precooling measure in a home with thermal performance typical of new construction. We assume a 65% reduction in cooling load during peak hours for this measure and a peak-to-penalty energy ratio of 3.19, defined as the ratio of peak reduction to any additional increase in off-peak electricity demand beyond that attributable to pre-cooling.…”

Cost-Saving through Pre-Cooling: A Case Study of Sydney

“…Moreover, climate, pre-cooling duration, setpoint temperature during pre-cooling, and setpoint temperature during peak hours, change the benefits of pre-cooling scenarios [5,7]. Several studies have been conducted to investigate how the above parameters affect pre-cooling cost saving and peak AC demand reduction [8][9][10][11], by proposing different pre-cooling scenarios in terms of duration, setpoint temperauture, and considering different electricity tariff rates. Furthermore, some researchers have developed optimization frameworks to find the best pre-cooling strategy for a given case study [12][13][14].…”