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Towards designing an aggregator to activate the energy flexibility of multi-zone buildings using a hierarchical model-based scheme

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  • Khatibi, Mahmood
  • Rahnama, Samira
  • Vogler-Finck, Pierre
  • Dimon Bendtsen, Jan
  • Afshari, Alireza

Abstract

Aggregators are emerging players in the future power markets which aggregate the flexibility of small consumers. This paper proposes a hierarchical model-based scheme to activate the energy flexibility of multi-zone buildings through a direct aggregation mechanism. The novelty lies in considering the power market mechanism in which consumers try to remain committed to their bids without violating their desired comfort levels. In the proposed approach, a high-level control layer determines an hourly energy budget for the whole building according to price signals and reports it to an aggregator. A lower-level dispatch layer then distributes the pre-planned hourly energy budget among different zones. At this level, the emphasis is on keeping the energy consumption as close to the pre-planned budget as possible while satisfying the comfort requirements. In addition, this layer computes the available real-time up and down regulating power and reports them to the aggregator. For comparison, we develop both a centralized and a decentralized model predictive control (MPC) scheme for the high-level control layer. Furthermore, a decentralized MPC with variable prediction horizon is designed for the lower-level dispatch layer. The proposed method is applied to a detailed multi zone building model developed in a high-fidelity simulation environment. The results show that the proposed scheme can keep its commitment to the aggregator to a large extent (by more than 93%) while maintaining the desired comfort levels. In addition, it is seen that the centralized model reduces energy costs and exhibits between 0.5% and 2.5% better commitment to the pre-planned budget in comparison with the decentralized one at the cost of sacrificing comfort to some extent. Moreover, some preliminary results regarding available up and down regulating power for residential buildings are reported for the first time.

Suggested Citation

  • Khatibi, Mahmood & Rahnama, Samira & Vogler-Finck, Pierre & Dimon Bendtsen, Jan & Afshari, Alireza, 2023. "Towards designing an aggregator to activate the energy flexibility of multi-zone buildings using a hierarchical model-based scheme," Applied Energy, Elsevier, vol. 333(C).
    • Handle: RePEc:eee:appene:v:333:y:2023:i:c:s0306261922018190
    DOI: 10.1016/j.apenergy.2022.120562
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    References listed on IDEAS

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    1. Okur, Özge & Voulis, Nina & Heijnen, Petra & Lukszo, Zofia, 2019. "Aggregator-mediated demand response: Minimizing imbalances caused by uncertainty of solar generation," Applied Energy, Elsevier, vol. 247(C), pages 426-437.
    2. 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).
    3. Li, Bingxu & Wu, Bingjie & Peng, Yelun & Cai, Wenjian, 2022. "Tube-based robust model predictive control of multi-zone demand-controlled ventilation systems for energy saving and indoor air quality," Applied Energy, Elsevier, vol. 307(C).
    4. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
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    Cited by:

    1. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).
    2. Cui, Xueyuan & Liu, Shu & Ruan, Guangchun & Wang, Yi, 2024. "Data-driven aggregation of thermal dynamics within building virtual power plants," Applied Energy, Elsevier, vol. 353(PB).
    3. Engel, Jens & Schmitt, Thomas & Rodemann, Tobias & Adamy, Jürgen, 2024. "Hierarchical MPC for building energy management: Incorporating data-driven error compensation and mitigating information asymmetry," Applied Energy, Elsevier, vol. 372(C).
    4. Dai, Shuangfeng & Mansouri, Seyed Amir & Huang, Shoujun & Alharthi, Yahya Z. & Wu, Yongfei & Bagherzadeh, Leila, 2024. "A multi-stage techno-economic model for harnessing flexibility from IoT-enabled appliances and smart charging systems: Developing a competitive local flexibility market using Stackelberg game theory," Applied Energy, Elsevier, vol. 373(C).
    5. Pantoš, Miloš & Lukas, Lucija, 2025. "Enhancing power system reliability through demand flexibility of Grid-Interactive Efficient Buildings: A thermal model-based optimization approach," Applied Energy, Elsevier, vol. 381(C).

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