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Community-scale residential air conditioning control for effective grid management

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  • Cole, Wesley J.
  • Rhodes, Joshua D.
  • Gorman, William
  • Perez, Krystian X.
  • Webber, Michael E.
  • Edgar, Thomas F.

Abstract

This paper investigates the potential for coordinated control of a large number of residential air conditioning systems to achieve substantial reductions in peak electricity demand. To do so, an extensive data set including home energy audits, homeowner surveys, and electricity meter measurements from homes in Austin, Texas, USA, was used to build a simulated community of 900 homes. Based on a reduced-order modeling strategy and an economic model predictive control approach, we analyze the effects of the community of homes responding optimally to variations in wholesale market electricity prices. We find that when exposed to dynamic pricing, peak demand from residential electricity consumption is shifted to earlier in the day, and is lower than the peak where no intervention is made. We also consider centralized and decentralized strategies for minimizing the peak demand of the community. For this simulated community, we find that centralized, coordinated control of residential air conditioning systems reduces overall peak by 8.8% but increases total energy consumption by 13.3%. Decentralized control reduces overall peak by 5.7%, demonstrating that the value of information sharing for peak reduction is 3.1%. It is also shown that properly tuned penalty terms allow a penalty-based decentralized controller to approach the optimal solution obtained by a centralized controller without the requirement of information sharing.

Suggested Citation

  • Cole, Wesley J. & Rhodes, Joshua D. & Gorman, William & Perez, Krystian X. & Webber, Michael E. & Edgar, Thomas F., 2014. "Community-scale residential air conditioning control for effective grid management," Applied Energy, Elsevier, vol. 130(C), pages 428-436.
    • Handle: RePEc:eee:appene:v:130:y:2014:i:c:p:428-436
    DOI: 10.1016/j.apenergy.2014.05.067
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    6. Ren, Haoshan & Sun, Yongjun & Albdoor, Ahmed K. & Tyagi, V.V. & Pandey, A.K. & Ma, Zhenjun, 2021. "Improving energy flexibility of a net-zero energy house using a solar-assisted air conditioning system with thermal energy storage and demand-side management," Applied Energy, Elsevier, vol. 285(C).
    7. Neves, Diana & Pina, André & Silva, Carlos A., 2015. "Demand response modeling: A comparison between tools," Applied Energy, Elsevier, vol. 146(C), pages 288-297.
    8. Cui, Borui & Fan, Cheng & Munk, Jeffrey & Mao, Ning & Xiao, Fu & Dong, Jin & Kuruganti, Teja, 2019. "A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses," Applied Energy, Elsevier, vol. 236(C), pages 101-116.
    9. Hirvonen, Janne & Kayo, Genku & Hasan, Ala & Sirén, Kai, 2014. "Local sharing of cogeneration energy through individually prioritized controls for increased on-site energy utilization," Applied Energy, Elsevier, vol. 135(C), pages 350-363.
    10. Shiljkut, Vladimir M. & Rajakovic, Nikola Lj., 2015. "Demand response capacity estimation in various supply areas," Energy, Elsevier, vol. 92(P3), pages 476-486.
    11. Tabares-Velasco, Paulo Cesar & Speake, Andrew & Harris, Maxwell & Newman, Alexandra & Vincent, Tyrone & Lanahan, Michael, 2019. "A modeling framework for optimization-based control of a residential building thermostat for time-of-use pricing," Applied Energy, Elsevier, vol. 242(C), pages 1346-1357.
    12. Ahmad Murtaza Ershad & Robert Pietzcker & Falko Ueckerdt & Gunnar Luderer, 2020. "Managing Power Demand from Air Conditioning Benefits Solar PV in India Scenarios for 2040," Energies, MDPI, vol. 13(9), pages 1-19, May.
    13. Kai Ma & Chenliang Yuan & Jie Yang & Zhixin Liu & Xinping Guan, 2017. "Switched Control Strategies of Aggregated Commercial HVAC Systems for Demand Response in Smart Grids," Energies, MDPI, vol. 10(7), pages 1-18, July.
    14. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    15. Adhikari, Rajendra & Pipattanasomporn, M. & Rahman, S., 2018. "An algorithm for optimal management of aggregated HVAC power demand using smart thermostats," Applied Energy, Elsevier, vol. 217(C), pages 166-177.
    16. Malik, Anam & Haghdadi, Navid & MacGill, Iain & Ravishankar, Jayashri, 2019. "Appliance level data analysis of summer demand reduction potential from residential air conditioner control," Applied Energy, Elsevier, vol. 235(C), pages 776-785.
    17. Tina, Giuseppe Marco & Aneli, Stefano & Gagliano, Antonio, 2022. "Technical and economic analysis of the provision of ancillary services through the flexibility of HVAC system in shopping centers," Energy, Elsevier, vol. 258(C).
    18. Heine, Karl & Tabares-Velasco, Paulo Cesar & Deru, Michael, 2021. "Design and dispatch optimization of packaged ice storage systems within a connected community," Applied Energy, Elsevier, vol. 298(C).
    19. Haosen Qin & Zhen Yu & Tailu Li & Xueliang Liu & Li Li, 2022. "Heating Control Strategy Based on Dynamic Programming for Building Energy Saving and Emission Reduction," IJERPH, MDPI, vol. 19(21), pages 1-27, October.

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