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Impact of thermostatically controlled loads' demand response activation on aggregated power: A field experiment

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  • Lakshmanan, Venkatachalam
  • Marinelli, Mattia
  • Kosek, Anna M.
  • Nørgård, Per B.
  • Bindner, Henrik W.

Abstract

This paper describes the impacts of different types of DR (demand response) activation on TCLs' (thermostatically controlled loads) aggregated power. The different parties: power system operators, DR service providers (or aggregators) and consumers, have different objectives in relation to DR activation. The outcome of this experimental study quantifies the actual flexibility of household TCLs and the consequence for the different parties with respect to power behaviour. Each DR activation method adopts different scenarios to meet the power reduction, and has different impacts on the parameters. The experiments are conducted with real domestic refrigerators representing TCL. Activating refrigerators for DR with a delay reduces the ISE (integral square error) in power limitation by 28.46%, overshoot by 7.69%. The delay in refrigerator activation causes reduction in power ramp down rate by 39.90%, ramp up rate by 21.30% and the instantaneous average temperature increases by 0.13% in comparison with the scenario without activation delay.

Suggested Citation

  • Lakshmanan, Venkatachalam & Marinelli, Mattia & Kosek, Anna M. & Nørgård, Per B. & Bindner, Henrik W., 2016. "Impact of thermostatically controlled loads' demand response activation on aggregated power: A field experiment," Energy, Elsevier, vol. 94(C), pages 705-714.
    • Handle: RePEc:eee:energy:v:94:y:2016:i:c:p:705-714
    DOI: 10.1016/j.energy.2015.11.050
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    References listed on IDEAS

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    1. Koltsaklis, Nikolaos E. & Liu, Pei & Georgiadis, Michael C., 2015. "An integrated stochastic multi-regional long-term energy planning model incorporating autonomous power systems and demand response," Energy, Elsevier, vol. 82(C), pages 865-888.
    2. Stadler, Ingo, 2008. "Power grid balancing of energy systems with high renewable energy penetration by demand response," Utilities Policy, Elsevier, vol. 16(2), pages 90-98, June.
    3. Gils, Hans Christian, 2014. "Assessment of the theoretical demand response potential in Europe," Energy, Elsevier, vol. 67(C), pages 1-18.
    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.
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    Cited by:

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    3. Diaz-Londono, Cesar & Enescu, Diana & Ruiz, Fredy & Mazza, Andrea, 2020. "Experimental modeling and aggregation strategy for thermoelectric refrigeration units as flexible loads," Applied Energy, Elsevier, vol. 272(C).
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    5. Bomela, Walter & Zlotnik, Anatoly & Li, Jr-Shin, 2018. "A phase model approach for thermostatically controlled load demand response," Applied Energy, Elsevier, vol. 228(C), pages 667-680.
    6. Alejandro Martín-Crespo & Sergio Saludes-Rodil & Enrique Baeyens, 2021. "Flexibility Management with Virtual Batteries of Thermostatically Controlled Loads: Real-Time Control System and Potential in Spain," Energies, MDPI, vol. 14(6), pages 1-18, March.
    7. Nicolas A. Campbell & Patrick E. Phelan & Miguel Peinado-Guerrero & Jesus R. Villalobos, 2021. "Improved Air-Conditioning Demand Response of Connected Communities over Individually Optimized Buildings," Energies, MDPI, vol. 14(18), pages 1-17, September.
    8. Srđan Skok & Ahmed Mutapčić & Renata Rubesa & Mario Bazina, 2020. "Transmission Power System Modeling by Using Aggregated Distributed Generation Model Based on a TSO—DSO Data Exchange Scheme," Energies, MDPI, vol. 13(15), pages 1-15, August.
    9. O'Connell, Sarah & Reynders, Glenn & Keane, Marcus M., 2021. "Impact of source variability on flexibility for demand response," Energy, Elsevier, vol. 237(C).
    10. Lakshmanan, Venkatachalam & Marinelli, Mattia & Hu, Junjie & Bindner, Henrik W., 2016. "Provision of secondary frequency control via demand response activation on thermostatically controlled loads: Solutions and experiences from Denmark," Applied Energy, Elsevier, vol. 173(C), pages 470-480.
    11. Andersen, Frits Møller & Baldini, Mattia & Hansen, Lars Gårn & Jensen, Carsten Lynge, 2017. "Households’ hourly electricity consumption and peak demand in Denmark," Applied Energy, Elsevier, vol. 208(C), pages 607-619.

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