IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v326y2022ics0306261922012806.html
   My bibliography  Save this article

Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics

Author

Listed:
  • Yang, Shiyu
  • Oliver Gao, H.
  • You, Fengqi

Abstract

Incorporating phase-change material (PCM) wallboards into building envelopes can help reduce electricity costs associated with heating and enhance demand flexibility in dynamic building energy management in response to electricity price volatility. The conventional control strategies adopted in current building energy management systems are ‘reactive’ in nature, hence, they are not ready to exploit the full potential of PCM wallboards for price-based, demand-responsive building control applications. This study proposes a model predictive control (MPC) framework for price-based, demand-responsive control of PCM-wallboard-enhanced space heating systems to minimize electricity costs and maximize demand flexibility by fully exploiting PCM wallboards. A high-fidelity, linear PCM wallboard and building thermodynamics model are developed for computationally efficient building control. The model is then incorporated into an MPC framework considering the PCM wallboard dynamics and dynamic electricity prices. A simulation case study is conducted for a single-family house employing the proposed MPC approach to control its space heating system. Compared to conventional control, the MPC approach reduces space heating electricity costs by more than 60 %. The MPC approach also shifts more than 80 % of peak heating loads to off-peak periods, much higher than conventional control achieves (less than40 %). The simulation results also show good synergistic benefits of MPC in fully exploiting PCM wallboards in optimizing building space heating operation: achieving additional 31.1 % electricity cost savings and 38.7 % peak load shifting than without leveraging PCM wallboards. In contrast, conventional control cannot achieve such synergistic benefits. The proposed MPC framework is also proven computationally viable for real-time building control.

Suggested Citation

  • Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics," Applied Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922012806
    DOI: 10.1016/j.apenergy.2022.120023
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922012806
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.120023?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    2. Wang, Huakeer & Lu, Wei & Wu, Zhigen & Zhang, Guanhua, 2020. "Parametric analysis of applying PCM wallboards for energy saving in high-rise lightweight buildings in Shanghai," Renewable Energy, Elsevier, vol. 145(C), pages 52-64.
    3. Blum, David & Wang, Zhe & Weyandt, Chris & Kim, Donghun & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Field demonstration and implementation analysis of model predictive control in an office HVAC system," Applied Energy, Elsevier, vol. 318(C).
    4. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2021. "Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems," Applied Energy, Elsevier, vol. 297(C).
    5. Castillo, Victhalia Zapata & Boer, Harmen-Sytze de & Muñoz, Raúl Maícas & Gernaat, David E.H.J. & Benders, René & van Vuuren, Detlef, 2022. "Future global electricity demand load curves," Energy, Elsevier, vol. 258(C).
    6. El Mehdi Er Raqabi & Wenkai Li, 2022. "An Electric Vehicle Migration Framework," Working Papers EMS_2022_03, Research Institute, International University of Japan.
    7. Francisco Ferreira da Silva & João F. P. Fernandes & Paulo José da Costa Branco, 2022. "Superconducting Electric Power Systems: R&D Advancements," Energies, MDPI, vol. 15(19), pages 1-10, October.
    8. Kuznik, Frédéric & Virgone, Joseph, 2009. "Experimental assessment of a phase change material for wall building use," Applied Energy, Elsevier, vol. 86(10), pages 2038-2046, October.
    9. Kishore, Ravi Anant & Bianchi, Marcus V.A. & Booten, Chuck & Vidal, Judith & Jackson, Roderick, 2021. "Enhancing building energy performance by effectively using phase change material and dynamic insulation in walls," Applied Energy, Elsevier, vol. 283(C).
    10. 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).
    11. Chen, Wei-Han & You, Fengqi, 2022. "Sustainable building climate control with renewable energy sources using nonlinear model predictive control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    12. Ipseeta Nanda & Rajesh De, 2022. "Study Of Electromagnetic Radiation On Flower," Matrix Science Mathematic (MSMK), Zibeline International Publishing, vol. 6(2), pages 58-63, November.
    13. 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).
    14. Devaux, Paul & Farid, Mohammed Mehdi, 2017. "Benefits of PCM underfloor heating with PCM wallboards for space heating in winter," Applied Energy, Elsevier, vol. 191(C), pages 593-602.
    15. Finck, Christian & Li, Rongling & Kramer, Rick & Zeiler, Wim, 2018. "Quantifying demand flexibility of power-to-heat and thermal energy storage in the control of building heating systems," Applied Energy, Elsevier, vol. 209(C), pages 409-425.
    16. Pallonetto, Fabiano & De Rosa, Mattia & Milano, Federico & Finn, Donal P., 2019. "Demand response algorithms for smart-grid ready residential buildings using machine learning models," Applied Energy, Elsevier, vol. 239(C), pages 1265-1282.
    17. Barzin, Reza & Chen, John J.J. & Young, Brent R. & Farid, Mohammed M., 2015. "Application of PCM underfloor heating in combination with PCM wallboards for space heating using price based control system," Applied Energy, Elsevier, vol. 148(C), pages 39-48.
    18. João Pedro F. Trovão & Minh Cao Ta, 2022. "Electric Vehicle Efficient Power and Propulsion Systems," Energies, MDPI, vol. 15(11), pages 1-4, May.
    19. Lamrani, B. & Johannes, K. & Kuznik, F., 2021. "Phase change materials integrated into building walls: An updated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    20. Zhou, Guobing & Yang, Yongping & Xu, Hong, 2011. "Performance of shape-stabilized phase change material wallboard with periodical outside heat flux waves," Applied Energy, Elsevier, vol. 88(6), pages 2113-2121, June.
    21. Mavrigiannaki, A. & Ampatzi, E., 2016. "Latent heat storage in building elements: A systematic review on properties and contextual performance factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 852-866.
    22. Gholamibozanjani, Gohar & Tarragona, Joan & Gracia, Alvaro de & Fernández, Cèsar & Cabeza, Luisa F. & Farid, Mohammed M., 2018. "Model predictive control strategy applied to different types of building for space heating," Applied Energy, Elsevier, vol. 231(C), pages 959-971.
    23. Sun, Xiaoqin & Medina, Mario A. & Lee, Kyoung Ok & Jin, Xing, 2018. "Laboratory assessment of residential building walls containing pipe-encapsulated phase change materials for thermal management," Energy, Elsevier, vol. 163(C), pages 383-391.
    24. , Yangriani, 2022. "Electronic Customer Relationship Management," OSF Preprints myp8g, Center for Open Science.
    25. Gohar Gholamibozanjani & Mohammed Farid, 2021. "A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings," Energies, MDPI, vol. 14(7), pages 1-39, March.
    26. Maturo, Anthony & Buonomano, Annamaria & Athienitis, Andreas, 2022. "Design for energy flexibility in smart buildings through solar based and thermal storage systems: Modelling, simulation and control for the system optimization," Energy, Elsevier, vol. 260(C).
    27. Paul L. J. Helgers & James A. H. Stotz & Haruki Sanada & Yoji Kunihashi & Klaus Biermann & Paulo V. Santos, 2022. "Flying electron spin control gates," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    28. Tarragona, Joan & Fernández, Cèsar & de Gracia, Alvaro, 2020. "Model predictive control applied to a heating system with PV panels and thermal energy storage," Energy, Elsevier, vol. 197(C).
    29. Finck, Christian & Li, Rongling & Zeiler, Wim, 2020. "Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration," Applied Energy, Elsevier, vol. 263(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hu, Guoqing & You, Fengqi, 2023. "An AI framework integrating physics-informed neural network with predictive control for energy-efficient food production in the built environment," Applied Energy, Elsevier, vol. 348(C).
    2. Wei, Zhichen & Calautit, John Kaiser, 2024. "Field experiment testing of a low-cost model predictive controller (MPC) for building heating systems and analysis of phase change material (PCM) integration," Applied Energy, Elsevier, vol. 360(C).
    3. Xie, Jiahan & Ajagekar, Akshay & You, Fengqi, 2023. "Multi-Agent attention-based deep reinforcement learning for demand response in grid-responsive buildings," Applied Energy, Elsevier, vol. 342(C).
    4. Jia, Hongjie & Wang, Xiaoyu & Jin, Xiaolong & Cheng, Lin & Mu, Yunfei & Yu, Xiaodan & Wei, Wei, 2024. "Optimal pricing of integrated community energy system for building prosumers with P2P multi-energy trading," Applied Energy, Elsevier, vol. 365(C).
    5. Ajagekar, Akshay & Decardi-Nelson, Benjamin & You, Fengqi, 2024. "Energy management for demand response in networked greenhouses with multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 355(C).
    6. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control for Demand- and Market-Responsive building energy management by leveraging active latent heat storage," Applied Energy, Elsevier, vol. 327(C).
    7. Xiao, Tianqi & You, Fengqi, 2023. "Building thermal modeling and model predictive control with physically consistent deep learning for decarbonization and energy optimization," Applied Energy, Elsevier, vol. 342(C).
    8. Wang, Pengcheng & Liu, Zhongbing & Zhang, Ling & Wang, Zhe & Fan, Jianhua, 2023. "Inversion of extinction coefficient and refractive index of variable transparency solid–solid phase change material based on a hybrid model under real climatic conditions," Applied Energy, Elsevier, vol. 341(C).
    9. Zhang, Shufan & Zhou, Nan & Feng, Wei & Ma, Minda & Xiang, Xiwang & You, Kairui, 2023. "Pathway for decarbonizing residential building operations in the US and China beyond the mid-century," Applied Energy, Elsevier, vol. 342(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control for Demand- and Market-Responsive building energy management by leveraging active latent heat storage," Applied Energy, Elsevier, vol. 327(C).
    2. Pilotti, L. & Colombari, M. & Castelli, A.F. & Binotti, M. & Giaconia, A. & Martelli, E., 2023. "Simultaneous design and operational optimization of hybrid CSP-PV plants," Applied Energy, Elsevier, vol. 331(C).
    3. Gohar Gholamibozanjani & Mohammed Farid, 2021. "A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings," Energies, MDPI, vol. 14(7), pages 1-39, March.
    4. Gholamibozanjani, Gohar & Farid, Mohammed, 2020. "Application of an active PCM storage system into a building for heating/cooling load reduction," Energy, Elsevier, vol. 210(C).
    5. Abdullah-Al-Nahid, Syed & Jamal, Taskin & Aziz, Tareq & Bhuiyan, Ashraf Hossain & Khan, Tafsir Ahmed, 2023. "Additive linear modelling and genetic algorithm based electric vehicle outlook and policy formulation for decarbonizing the future transport sector of Bangladesh," Transport Policy, Elsevier, vol. 136(C), pages 21-46.
    6. Lamrani, B. & Johannes, K. & Kuznik, F., 2021. "Phase change materials integrated into building walls: An updated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
    7. Prakash, Abhijith & Ashby, Rohan & Bruce, Anna & MacGill, Iain, 2023. "Quantifying reserve capabilities for designing flexible electricity markets: An Australian case study with increasing penetrations of renewables," Energy Policy, Elsevier, vol. 177(C).
    8. Gholamibozanjani, Gohar & Farid, Mohammed, 2020. "A comparison between passive and active PCM systems applied to buildings," Renewable Energy, Elsevier, vol. 162(C), pages 112-123.
    9. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    10. Xu, Jiacheng & Liang, Yingzong & Luo, Xianglong & Chen, Jianyong & Yang, Zhi & Chen, Ying, 2023. "Towards cost-effective osmotic power harnessing: Mass exchanger network synthesis for multi-stream pressure-retarded osmosis systems," Applied Energy, Elsevier, vol. 330(PA).
    11. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    12. Pirasaci, Tolga, 2020. "Investigation of phase state and heat storage form of the phase change material (PCM) layer integrated into the exterior walls of the residential-apartment during heating season," Energy, Elsevier, vol. 207(C).
    13. Wei, Zhichen & Calautit, John Kaiser, 2024. "Field experiment testing of a low-cost model predictive controller (MPC) for building heating systems and analysis of phase change material (PCM) integration," Applied Energy, Elsevier, vol. 360(C).
    14. Kong, Xiangfei & Jie, Pengfei & Yao, Chengqiang & Liu, Yun, 2017. "Experimental study on thermal performance of phase change material passive and active combined using for building application in winter," Applied Energy, Elsevier, vol. 206(C), pages 293-302.
    15. 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).
    16. Hechelmann, Ron-Hendrik & Paris, Aaron & Buchenau, Nadja & Ebersold, Felix, 2023. "Decarbonisation strategies for manufacturing: A technical and economic comparison," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    17. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    18. Morovat, Navid & Athienitis, Andreas K. & Candanedo, José Agustín & Nouanegue, Hervé Frank, 2024. "Heuristic model predictive control implementation to activate energy flexibility in a fully electric school building," Energy, Elsevier, vol. 296(C).
    19. Bimaganbetova, Madina & Memon, Shazim Ali & Sheriyev, Almas, 2020. "Performance evaluation of phase change materials suitable for cities representing the whole tropical savanna climate region," Renewable Energy, Elsevier, vol. 148(C), pages 402-416.
    20. Lee, Haksung & Ozaki, Akihito, 2018. "Sensitivity analysis for optimization of renewable-energy-based air-circulation-type temperature-control system," Applied Energy, Elsevier, vol. 230(C), pages 317-329.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922012806. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.