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Residential cooling using separated and coupled precooling and thermal energy storage strategies

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  • Nelson, James
  • Johnson, Nathan G.
  • Chinimilli, Prudhvi Tej
  • Zhang, Wenlong

Abstract

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.

Suggested Citation

  • Nelson, James & Johnson, Nathan G. & Chinimilli, Prudhvi Tej & Zhang, Wenlong, 2019. "Residential cooling using separated and coupled precooling and thermal energy storage strategies," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:252:y:2019:i:c:63
    DOI: 10.1016/j.apenergy.2019.113414
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    2. Marwan, Marwan, 2020. "The impact of probability of electricity price spike and outside temperature to define total expected cost for air conditioning," Energy, Elsevier, vol. 195(C).
    3. Naderi, Shayan & Heslop, Simon & Chen, Dong & Watts, Scott & MacGill, Iain & Pignatta, Gloria & Sproul, Alistair, 2023. "Clustering based analysis of residential duck curve mitigation through solar pre-cooling: A case study of Australian housing stock," Renewable Energy, Elsevier, vol. 216(C).
    4. Simon Heslop & Baran Yildiz & Mike Roberts & Dong Chen & Tim Lau & Shayan Naderi & Anna Bruce & Iain MacGill & Renate Egan, 2022. "A Novel Temperature-Independent Model for Estimating the Cooling Energy in Residential Homes for Pre-Cooling and Solar Pre-Cooling," Energies, MDPI, vol. 15(23), pages 1-18, December.
    5. Shu, Lei & Mo, Yunjeong & Zhao, Dong, 2024. "Energy retrofits for smart and connected communities: Scopes and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    6. Wang, Junke & Yik Tang, Choon & Song, Li, 2022. "Analysis of precooling optimization for residential buildings," Applied Energy, Elsevier, vol. 323(C).
    7. Luciana Marques & Wadaed Uturbey & Miguel Heleno, 2021. "An Integer Non-Cooperative Game Approach for the Transactive Control of Thermal Appliances in Energy Communities," Energies, MDPI, vol. 14(21), pages 1-22, October.

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