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Optimal use of thermal energy storage resources in commercial buildings through price-based demand response considering distribution network operation

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  • Kim, Youngjin
  • Norford, Leslie K.

Abstract

Energy storage resources (ESRs) inherent in building structures are a viable, attractive option to improve power system operation by providing demand-side flexibility. This paper proposes a two-stage optimisation framework for price-based demand response of commercial buildings that include variable speed heat pumps (VSHPs). The proposed framework aims at assisting commercial building aggregators to devise a beneficial strategy for exploiting thermal ESRs in response to electricity prices. Specifically, in this paper, the thermal dynamics of VSHPs are modelled in detail using a set of piecewise linear equations for two different methods of room temperature control. The energy consumption and reserve provision of VSHPs, as well as plug-in electric vehicles, are then co-optimised considering the operating conditions of distribution networks (DNs) for the pre- and post-contingency states of wind power generation. Simulation case studies are performed to estimate the effects of building ESRs on the optimal operation of power systems and commercial buildings under various conditions characterised by: (1) temperature control methods, (2) ESR penetration levels, and (3) DN operational constraints.

Suggested Citation

  • Kim, Youngjin & Norford, Leslie K., 2017. "Optimal use of thermal energy storage resources in commercial buildings through price-based demand response considering distribution network operation," Applied Energy, Elsevier, vol. 193(C), pages 308-324.
    • Handle: RePEc:eee:appene:v:193:y:2017:i:c:p:308-324
    DOI: 10.1016/j.apenergy.2017.02.046
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    8. Yoon, Ah-Yun & Kim, Young-Jin & Zakula, Tea & Moon, Seung-Ill, 2020. "Retail electricity pricing via online-learning of data-driven demand response of HVAC systems," Applied Energy, Elsevier, vol. 265(C).
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    10. Fadi Alnaimat & Yasir Rashid, 2019. "Thermal Energy Storage in Solar Power Plants: A Review of the Materials, Associated Limitations, and Proposed Solutions," Energies, MDPI, vol. 12(21), pages 1-19, October.
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    12. Saffari, Mohammad & de Gracia, Alvaro & Fernández, Cèsar & Belusko, Martin & Boer, Dieter & Cabeza, Luisa F., 2018. "Optimized demand side management (DSM) of peak electricity demand by coupling low temperature thermal energy storage (TES) and solar PV," Applied Energy, Elsevier, vol. 211(C), pages 604-616.
    13. Pan, Guangsheng & Gu, Wei & Wu, Zhi & Lu, Yuping & Lu, Shuai, 2019. "Optimal design and operation of multi-energy system with load aggregator considering nodal energy prices," Applied Energy, Elsevier, vol. 239(C), pages 280-295.
    14. Lizana, Jesús & Chacartegui, Ricardo & Barrios-Padura, Angela & Ortiz, Carlos, 2018. "Advanced low-carbon energy measures based on thermal energy storage in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3705-3749.
    15. Blum, D.H. & Arendt, K. & Rivalin, L. & Piette, M.A. & Wetter, M. & Veje, C.T., 2019. "Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems," Applied Energy, Elsevier, vol. 236(C), pages 410-425.
    16. Li, Zening & Su, Su & Jin, Xiaolong & Chen, Houhe, 2021. "Distributed energy management for active distribution network considering aggregated office buildings," Renewable Energy, Elsevier, vol. 180(C), pages 1073-1087.
    17. Cox, Sam J. & Kim, Dongsu & Cho, Heejin & Mago, Pedro, 2019. "Real time optimal control of district cooling system with thermal energy storage using neural networks," Applied Energy, Elsevier, vol. 238(C), pages 466-480.
    18. Fu, Yangyang & O'Neill, Zheng & Wen, Jin & Pertzborn, Amanda & Bushby, Steven T., 2022. "Utilizing commercial heating, ventilating, and air conditioning systems to provide grid services: A review," Applied Energy, Elsevier, vol. 307(C).
    19. Huang, Pei & Fan, Cheng & Zhang, Xingxing & Wang, Jiayuan, 2019. "A hierarchical coordinated demand response control for buildings with improved performances at building group," Applied Energy, Elsevier, vol. 242(C), pages 684-694.
    20. Liang, Zheming & Bian, Desong & Zhang, Xiaohu & Shi, Di & Diao, Ruisheng & Wang, Zhiwei, 2019. "Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market," Applied Energy, Elsevier, vol. 236(C), pages 916-926.
    21. Ghasem Ansari & Reza Keypour, 2023. "Optimizing the Performance of Commercial Demand Response Aggregator Using the Risk-Averse Function of Information-Gap Decision Theory," Sustainability, MDPI, vol. 15(7), pages 1-31, April.
    22. Faqiry, M. Nazif & Edmonds, Lawryn & Wu, Hongyu & Pahwa, Anil, 2020. "Distribution locational marginal price-based transactive day-ahead market with variable renewable generation," Applied Energy, Elsevier, vol. 259(C).

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