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Assessing the performance of uncertainty-aware transactive controls for building thermal energy storage systems

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  • Yu, Min Gyung
  • Pavlak, Gregory S.

Abstract

Energy storage systems provide a wide range of technological approaches to manage the balance between energy supply and demand in the electric grid. With the increasing uncertainty and variability that comes with wide-spread adoption of grid-scale and behind-the-meter renewable energy, it is imperative to develop stochastic operational planning and control approaches that can account for uncertainty in future conditions. Although, coordination of multiple thermal energy storage resources can support the transition to low carbon energy by enabling valuable system flexibility, few stochastic planning and control approaches have been developed for coordinating building-level thermal energy storage resources. In addition, there is also a need to analyze the potential benefits of an aggregator-level stochastic control framework versus applying stochastic planning and controls at each building individually. This work addresses these needs by developing an uncertainty-aware transactive control (UA-Tx) framework for an aggregator to coordinate the thermal energy storage (TES) assets of multiple buildings. A two-stage stochastic optimization framework is formulated for day-ahead energy procurement that considers uncertainty in building occupancy patterns, weather conditions, and real-time energy prices of the following day. In the second stage, possible recourse decisions through modifying TES operation are also considered. The dispatch of TES operational strategies is implemented through transactive controls, which use market mechanisms and customer preferences to achieve changes in building demand. During real-time operation, a local demand response aggregator determines the transactive clearing prices to dispatch the flexibility enabled by TES. Simulation case studies were conducted to demonstrate the capabilities of the uncertainty-aware aggregator control framework compared to the performance of applying intelligent controllers at each individual building. Up to 3.7% energy cost savings were observed for buildings under the UA-Tx aggregator control framework. Other potential benefits of the control approach are also discussed, along with anticipated future extensions.

Suggested Citation

  • Yu, Min Gyung & Pavlak, Gregory S., 2021. "Assessing the performance of uncertainty-aware transactive controls for building thermal energy storage systems," Applied Energy, Elsevier, vol. 282(PB).
    • Handle: RePEc:eee:appene:v:282:y:2021:i:pb:s0306261920315233
    DOI: 10.1016/j.apenergy.2020.116103
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    References listed on IDEAS

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    Cited by:

    1. Yu, Min Gyung & Pavlak, Gregory S., 2023. "Risk-aware sizing and transactive control of building portfolios with thermal energy storage," Applied Energy, Elsevier, vol. 332(C).
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    3. Renata Rodrigues Lautert & Wagner da Silva Brignol & Luciane Neves Canha & Olatunji Matthew Adeyanju & Vinícius Jacques Garcia, 2022. "A Flexible-Reliable Operation Model of Storage and Distributed Generation in a Biogas Power Plant," Energies, MDPI, vol. 15(9), pages 1-21, April.
    4. Maurer, Jona & Tschuch, Nicolai & Krebs, Stefan & Bhattacharya, Kankar & Cañizares, Claudio & Hohmann, Sören, 2023. "Toward transactive control of coupled electric power and district heating networks," Applied Energy, Elsevier, vol. 332(C).
    5. Xiong, Chengyan & Sun, Zhe & Meng, Qinglong & Li, Zeyang & Wei, Yingan & Zhao, Fan & Jiang, Le, 2022. "A simplified improved transactive control of air-conditioning demand response for determining room set-point temperature: Experimental studies," Applied Energy, Elsevier, vol. 323(C).
    6. Tarragona, Joan & Pisello, Anna Laura & Fernández, Cèsar & Cabeza, Luisa F. & Payá, Jorge & Marchante-Avellaneda, Javier & de Gracia, Alvaro, 2022. "Analysis of thermal energy storage tanks and PV panels combinations in different buildings controlled through model predictive control," Energy, Elsevier, vol. 239(PC).
    7. Huang, Bowen & Huang, Sen & Ma, Xu & Katipamula, Srinivas & Wu, Di & Lutes, Robert, 2023. "Stochastic scheduling for commercial building cooling systems: considering uncertainty in zone temperature prediction," Applied Energy, Elsevier, vol. 346(C).

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