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Predictive control based assessment of building demand flexibility in fixed time windows

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  • Vašak, Mario
  • Banjac, Anita
  • Hure, Nikola
  • Novak, Hrvoje
  • Kovačević, Marko

Abstract

This paper introduces a simple strategy for modular building energy management with explicit demand response based on a three-level hierarchical model predictive control that engages the entire building in optimal operation and financially viable flexibility provision. Financial viability of the assessed flexibility capacity is guaranteed regardless of the flexibility activation scenario, under the assumption that the entire building flexibility capacity is accepted. The strategy is verified on three pilot buildings. The selected pilot buildings have diverse configurations and are tested under different conditions to show the broad applicability of the developed approach. The analysis of the building flexibility is focused on particular characteristic days in the heating and cooling season and provides a comparison of the overall building operation costs for the following three control options applied to the building: hierarchical control with and without flexibility provision as well as conventional control. In this way, it is possible to quantify the benefits achievable exactly due to the advanced energy management system operation on the site. Flexibility sources and amounts within the building are analyzed in detail for each specific case.

Suggested Citation

  • Vašak, Mario & Banjac, Anita & Hure, Nikola & Novak, Hrvoje & Kovačević, Marko, 2023. "Predictive control based assessment of building demand flexibility in fixed time windows," Applied Energy, Elsevier, vol. 329(C).
    • Handle: RePEc:eee:appene:v:329:y:2023:i:c:s030626192201501x
    DOI: 10.1016/j.apenergy.2022.120244
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    References listed on IDEAS

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    1. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    2. Ilkay Oksuz & Umut Ugurlu, 2019. "Neural Network Based Model Comparison for Intraday Electricity Price Forecasting," Energies, MDPI, vol. 12(23), pages 1-14, November.
    3. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    4. Vázquez-Canteli, José R. & Nagy, Zoltán, 2019. "Reinforcement learning for demand response: A review of algorithms and modeling techniques," Applied Energy, Elsevier, vol. 235(C), pages 1072-1089.
    5. Mirakhorli, Amin & Dong, Bing, 2018. "Model predictive control for building loads connected with a residential distribution grid," Applied Energy, Elsevier, vol. 230(C), pages 627-642.
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