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Automatic Coordination of Internet-Connected Thermostats for Power Balancing and Frequency Control in Smart Microgrids

Author

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  • Saeid Bashash

    (Department of Mechanical Engineering, San Jose State University, San Jose, CA, 95192, USA)

  • Kai Lun Lee

    (Department of Mechanical Engineering, San Jose State University, San Jose, CA, 95192, USA)

Abstract

This paper proposes a novel feedback control strategy, so-called clock-like controller (CLC), to balance power supply and demand in smart microgrids by adjusting the setpoint temperatures of air conditioning (AC) loads. In the CLC algorithm, the grid operator communicates with the individual thermostats via the Internet and adjusts their setpoints by discrete temperature intervals (e.g., ±0.5 °C). Numerical simulations indicate that the proposed algorithm is able to deliver a smooth controllability of the aggregate AC power despite discrete temperature offsets. It can also be used for peak load shedding to mitigate the power generation cost. The CLC algorithm is then integrated into the grid frequency control problem, in which both power generators and loads in the network attempt to regulate the frequency of the system despite disturbances from demand, renewable sources, and local weather conditions. An autonomous microgrid model including a steam and a hydro generator, a solar energy source, and a large number of thermostatic loads is developed to evaluate and demonstrate the proposed method. Simulation results indicate that the AC loads with CLC algorithm can help maintain the power system frequency during extreme events when demand exceeds the maximum generation capacity available to the network. Under normal conditions, the contribution of demand-side control is marginalized by the fast responding generators, because of time delays in the frequency measurement and internet communication network.

Suggested Citation

  • Saeid Bashash & Kai Lun Lee, 2019. "Automatic Coordination of Internet-Connected Thermostats for Power Balancing and Frequency Control in Smart Microgrids," Energies, MDPI, vol. 12(10), pages 1-23, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1936-:d:232841
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    References listed on IDEAS

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    1. Zesen Wang & Yanmei Tang & Xiao Chen & Xiangyang Men & Jun Cao & Haifeng Wang, 2018. "Optimized Daily Dispatching Strategy of Building- Integrated Energy Systems Considering Vehicle to Grid Technology and Room Temperature Control," Energies, MDPI, vol. 11(5), pages 1-19, May.
    2. Ericson, Torgeir, 2009. "Direct load control of residential water heaters," Energy Policy, Elsevier, vol. 37(9), pages 3502-3512, September.
    3. Mohammed M. Olama & Teja Kuruganti & James Nutaro & Jin Dong, 2018. "Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid," Energies, MDPI, vol. 11(7), pages 1-15, July.
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    Cited by:

    1. Siyue Lu & Baoqun Zhang & Longfei Ma & Hui Xu & Yuantong Li & Shaobing Yang, 2023. "Economic Load-Reduction Strategy of Central Air Conditioning Based on Convolutional Neural Network and Pre-Cooling," Energies, MDPI, vol. 16(13), pages 1-22, June.
    2. Andrew Ly & Saeid Bashash, 2020. "Fast Transactive Control for Frequency Regulation in Smart Grids with Demand Response and Energy Storage," Energies, MDPI, vol. 13(18), pages 1-23, September.

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