TY - JOUR
T1 - Residential cooling using separated and coupled precooling and thermal energy storage strategies
AU - Nelson, James
AU - Johnson, Nathan G.
AU - Chinimilli, Prudhvi Tej
AU - Zhang, Wenlong
N1 - Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/10/15
Y1 - 2019/10/15
N2 - Increased residential cooling loads often correlate with peak electricity demand in warm and temperate climates. Solutions such as precooling and thermal energy storage (TES) being separately shown to shift energy use to off-peak times and reduce electricity expenses for commercial and residential applications. This study advances prior research to jointly implement and optimize precooling and TES strategies, and further develops a precooling strategy that dynamically adjusts precooling set points with respect to outdoor temperatures. Six strategies for residential cooling are evaluated and compared on metrics including system sizing, intraday dispatch, electricity use, energy expenditures, and investment rate of return. Case study data include a simulated one-year period for the cities of Phoenix, Los Angeles, and Kona in the United States. After accounting for capital cost, operating cost savings, and discount factors, a smart thermostat with the proposed dynamic precooling technique is found to have the best return on investment with payback rates between 0.2 and 6.2 years across all locations and rate structures. However, if technology costs lower or electricity rates change, it could be beneficial to use a combined approach with TES and precooling that gives the greatest reduction in daily on-peak demand and energy use at 75.6% and 78.5%, respectively. These individual and combined strategies provide value to ratepayers and electric utilities by reducing energy expenditures and shifting cooling loads to reduce system-wide peak demand.
AB - Increased residential cooling loads often correlate with peak electricity demand in warm and temperate climates. Solutions such as precooling and thermal energy storage (TES) being separately shown to shift energy use to off-peak times and reduce electricity expenses for commercial and residential applications. This study advances prior research to jointly implement and optimize precooling and TES strategies, and further develops a precooling strategy that dynamically adjusts precooling set points with respect to outdoor temperatures. Six strategies for residential cooling are evaluated and compared on metrics including system sizing, intraday dispatch, electricity use, energy expenditures, and investment rate of return. Case study data include a simulated one-year period for the cities of Phoenix, Los Angeles, and Kona in the United States. After accounting for capital cost, operating cost savings, and discount factors, a smart thermostat with the proposed dynamic precooling technique is found to have the best return on investment with payback rates between 0.2 and 6.2 years across all locations and rate structures. However, if technology costs lower or electricity rates change, it could be beneficial to use a combined approach with TES and precooling that gives the greatest reduction in daily on-peak demand and energy use at 75.6% and 78.5%, respectively. These individual and combined strategies provide value to ratepayers and electric utilities by reducing energy expenditures and shifting cooling loads to reduce system-wide peak demand.
KW - Building energy
KW - Electricity rates
KW - Load shift
KW - Precooling
KW - Techno-economic analysis
KW - Thermal energy storage
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U2 - 10.1016/j.apenergy.2019.113414
DO - 10.1016/j.apenergy.2019.113414
M3 - Article
AN - SCOPUS:85067231838
SN - 0306-2619
VL - 252
JO - Applied Energy
JF - Applied Energy
M1 - 113414
ER -