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Thermostats for the Smart Grid: Models, Benchmarks, and Insights

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  • Yong Liang
  • David I. Levine
  • Zuo-Jun (Max) Shen

Abstract

We model two existing thermostats and one novel thermostat to see how well they operate under dynamic pricing. The existing thermostats include a traditional thermostat with set temperature goals and a rigid thermostat that minimizes cost while always keeping temperature within a rigid predetermined range. We contrast both with a novel optimizing thermostat that finds the optimal tradeoff between comfort and cost. We compare the thermostats’ performance both theoretically and via numerical simulations. The simulations show that, under plausible assumptions, the optimizing thermostat’s advantage is economically large. Importantly, the electricity demand of the rigid thermostat (but not the optimizing thermostat) ceases to respond to electricity prices on precisely the days when the electricity grid tends to be near capacity. These are the times when demand response is the most socially valuable to avoid massive price spikes. The social benefits of the optimizing thermostat may provide incentives for utilities and regulators to encourage its adoption.

Suggested Citation

  • Yong Liang & David I. Levine & Zuo-Jun (Max) Shen, 2012. "Thermostats for the Smart Grid: Models, Benchmarks, and Insights," The Energy Journal, , vol. 33(4), pages 60-96, October.
    • Handle: RePEc:sae:enejou:v:33:y:2012:i:4:p:60-96
    DOI: 10.5547/01956574.33.4.4
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    2. Tu, Gengyang & Faure, Corinne & Schleich, Joachim & Guetlein, Marie-Charlotte, 2021. "The heat is off! The role of technology attributes and individual attitudes in the diffusion of Smart thermostats – findings from a multi-country survey," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    3. Chappin, Emile J.L. & Schleich, Joachim & Guetlein, Marie-Charlotte & Faure, Corinne & Bouwmans, Ivo, 2022. "Linking of a multi-country discrete choice experiment and an agent-based model to simulate the diffusion of smart thermostats," Technological Forecasting and Social Change, Elsevier, vol. 180(C).
    4. Wang, Yong & Li, Lin, 2013. "Time-of-use based electricity demand response for sustainable manufacturing systems," Energy, Elsevier, vol. 63(C), pages 233-244.
    5. Jahangir Hossain & Aida. F. A. Kadir & Ainain. N. Hanafi & Hussain Shareef & Tamer Khatib & Kyairul. A. Baharin & Mohamad. F. Sulaima, 2023. "A Review on Optimal Energy Management in Commercial Buildings," Energies, MDPI, vol. 16(4), pages 1-40, February.
    6. Saha, Kiran Kumar & Sukavanam, N., 2023. "Existence and uniqueness of blow-up solution to a fully fractional thermostat model," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    7. Rocha, Paula & Kaut, Michal & Siddiqui, Afzal S., 2016. "Energy-efficient building retrofits: An assessment of regulatory proposals under uncertainty," Energy, Elsevier, vol. 101(C), pages 278-287.
    8. Shiljkut, Vladimir M. & Rajakovic, Nikola Lj., 2015. "Demand response capacity estimation in various supply areas," Energy, Elsevier, vol. 92(P3), pages 476-486.

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