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Super-relaxation of space–time-quantized ensemble of energy loads to curtail their synchronization after demand response perturbation

Author

Listed:
  • Luchnikov, I.
  • Métivier, D.
  • Ouerdane, H.
  • Chertkov, M.

Abstract

Ensembles of thermostatically controlled loads (TCL) provide a significant demand response reserve for the system operator to balance power grids. However, this also results in the parasitic synchronization of individual devices within the ensemble leading to long post-demand-response oscillations in the integrated energy consumption of the ensemble. The synchronization is eventually destructed by fluctuations, thus leading to the (pre-demand response) steady state; however, this natural desynchronization, or relaxation to a statistically steady state, is too long. A resolution of this problem consists in measuring the ensemble’s instantaneous consumption and using it as a feedback to stochastic switching of the ensemble’s devices between on- and off-states. A simplified continuous-time model showed that carefully tuned nonlinear feedback results in a fast (super-) relaxation of the ensemble energy consumption. Since both state information and control signals are discrete, the actual TCL devices operation is space–time quantized, and this must be considered for realistic TCL ensemble modeling. Here, assuming that states are characterized by indoor temperature (quantifying comfort) and air conditioner regime (on, off), we construct a discrete model based on the probabilistic description of state transitions. We demonstrate that super-relaxation holds in such a more realistic setting, and that while it is stable against randomness in the stochastic matrix of the quantized model, it remains sensitive to the time discretization scheme. Aiming to achieve a balance between super-relaxation and customer’s comfort, we analyze the dependence of super-relaxation on details of the space–time quantization, and provide a simple analytical criterion to avoid undesirable oscillations in consumption.

Suggested Citation

  • Luchnikov, I. & Métivier, D. & Ouerdane, H. & Chertkov, M., 2021. "Super-relaxation of space–time-quantized ensemble of energy loads to curtail their synchronization after demand response perturbation," Applied Energy, Elsevier, vol. 285(C).
  • Handle: RePEc:eee:appene:v:285:y:2021:i:c:s0306261920317827
    DOI: 10.1016/j.apenergy.2020.116419
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    References listed on IDEAS

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    1. A. Bensoussan & K. Sung & S. Yam, 2013. "Linear–Quadratic Time-Inconsistent Mean Field Games," Dynamic Games and Applications, Springer, vol. 3(4), pages 537-552, December.
    2. O׳Connell, Niamh & Pinson, Pierre & Madsen, Henrik & O׳Malley, Mark, 2014. "Benefits and challenges of electrical demand response: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 686-699.
    3. Lynch, Muireann Á. & Nolan, Sheila & Devine, Mel T. & O’Malley, Mark, 2019. "The impacts of demand response participation in capacity markets," Applied Energy, Elsevier, vol. 250(C), pages 444-451.
    4. Liu, Yang & Mauter, Meagan S., 2020. "Assessing the demand response capacity of U.S. drinking water treatment plants," Applied Energy, Elsevier, vol. 267(C).
    5. Auer, Hans & Haas, Reinhard, 2016. "On integrating large shares of variable renewables into the electricity system," Energy, Elsevier, vol. 115(P3), pages 1592-1601.
    6. Wohlfarth, Katharina & Klobasa, Marian & Gutknecht, Ralph, 2020. "Demand response in the service sector – Theoretical, technical and practical potentials," Applied Energy, Elsevier, vol. 258(C).
    7. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Zhang, Zhen, 2018. "Coordination optimization of multiple thermostatically controlled load groups in distribution network with renewable energy," Applied Energy, Elsevier, vol. 231(C), pages 456-467.
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    Cited by:

    1. Song, Yuguang & Chen, Fangjian & Xia, Mingchao & Chen, Qifang, 2022. "The interactive dispatch strategy for thermostatically controlled loads based on the source–load collaborative evolution," Applied Energy, Elsevier, vol. 309(C).

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