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Centralized model predictive control strategy for thermal comfort and residential energy management

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  • Seal, Sayani
  • Boulet, Benoit
  • Dehkordi, Vahid R.

Abstract

A novel implementation strategy of a centralized model predictive control (MPC) is proposed for a zone based comfort and energy management in a residential building. A photovoltaic (PV) solar system and a stationary home battery unit are considered. A heat pump (HP) is used as the primary heating unit. The electric baseboards (BB) are used as a supplementary system. The MPC controls the HP and BB power inputs for comfort management, as well as the energy flow among the PV, the battery and the bidirectional grid system. Variable Time-of-Use (ToU) rates are considered for the energy cost calculation and Feed-in-Tariff (FiT) is considered for selling energy to the grid. A 13.5% reduction in the energy cost is achieved with the centralized MPC as compared to a rule based energy management strategy. The solar energy generation and battery storage contribute to approximately 31% saving.

Suggested Citation

  • Seal, Sayani & Boulet, Benoit & Dehkordi, Vahid R., 2020. "Centralized model predictive control strategy for thermal comfort and residential energy management," Energy, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:energy:v:212:y:2020:i:c:s0360544220315644
    DOI: 10.1016/j.energy.2020.118456
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    References listed on IDEAS

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    Cited by:

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    2. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    3. Sun, Chunhua & Chen, Jiali & Cao, Shanshan & Gao, Xiaoyu & Xia, Guoqiang & Qi, Chengying & Wu, Xiangdong, 2021. "A dynamic control strategy of district heating substations based on online prediction and indoor temperature feedback," Energy, Elsevier, vol. 235(C).
    4. Karol Bot & Inoussa Laouali & António Ruano & Maria da Graça Ruano, 2021. "Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques," Energies, MDPI, vol. 14(18), pages 1-27, September.
    5. Sun, Hongchang & Niu, Yanlei & Li, Chengdong & Zhou, Changgeng & Zhai, Wenwen & Chen, Zhe & Wu, Hao & Niu, Lanqiang, 2022. "Energy consumption optimization of building air conditioning system via combining the parallel temporal convolutional neural network and adaptive opposition-learning chimp algorithm," Energy, Elsevier, vol. 259(C).
    6. Yang, Chao & Liu, Kaijia & Jiao, Xiaohong & Wang, Weida & Chen, Ruihu & You, Sixiong, 2022. "An adaptive firework algorithm optimization-based intelligent energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 239(PB).
    7. Tarragona, Joan & Pisello, Anna Laura & Fernández, Cèsar & de Gracia, Alvaro & Cabeza, Luisa F., 2021. "Systematic review on model predictive control strategies applied to active thermal energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    8. Hou, Juan & Li, Haoran & Nord, Natasa, 2022. "Nonlinear model predictive control for the space heating system of a university building in Norway," Energy, Elsevier, vol. 253(C).
    9. Zhongda Lu & Qilong Wang & Fengxia Xu & Mingqing Fan & Chuanshui Peng & Shiwei Yan, 2023. "Double-Layer SOC and SOH Equalization Scheme for LiFePO 4 Battery Energy Storage System Using MAS Blackboard System," Energies, MDPI, vol. 16(14), pages 1-14, July.
    10. Sun, Chunhua & Liu, Yiting & Cao, Shanshan & Chen, Jiali & Xia, Guoqiang & Wu, Xiangdong, 2022. "Identification of control regularity of heating stations based on cross-correlation function dynamic time delay method," Energy, Elsevier, vol. 246(C).
    11. Li, Qing & Zhang, Lianying & Zhang, Limao & Wu, Xianguo, 2021. "Optimizing energy efficiency and thermal comfort in building green retrofit," Energy, Elsevier, vol. 237(C).

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