IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v239y2019icp252-267.html
   My bibliography  Save this article

Measured and modeled performance of internal mass as a thermal energy battery for energy flexible residential buildings

Author

Listed:
  • Oliveira Panão, Marta J.N.
  • Mateus, Nuno M.
  • Carrilho da Graça, G.

Abstract

To increase the penetration of photovoltaic renewable energy (PV RE) in the residential building sector energy mix, the mismatch between mid-day production and evening electrical energy demand must be bridged. The use of heat pumps for space heating of residential buildings creates an opportunity for energy flexible residential buildings that rely on low-cost thermal energy storage systems. This paper explores the ‘building as battery’ concept (BaB), i.e. the use of the structural thermal capacity as a heat storage medium in winter. In these systems the building internal mass is preheated during solar daytime hours (using a PV RE powered heat pump) and cooled by discharge into the room air after sunset, for space heating. This study presents measured and simulated performance of BaB systems used for thermal energy storage in living rooms of three different apartment buildings with variable thermal insulation levels, ranging from typical southern Europe 1980s construction to a insulated apartment (Passive House certified). To assist in interpreting the results and facilitate their integration in simplified simulation of BaB systems in smart grids the model parameters of a simplified Resistance-Capacitance model were found by fitting the experimental results. The results show that, for apartments with high thermal mass, thermal insulation is the key driver of BaB system thermal efficiency, with the highly insulated apartment reaching BaB efficiencies of 60–80%. For existing apartments with a low thermal insulation, the BaB approach is only effective when space heating is required right after sunset (just when PV RE powered heat pump charging finishes).

Suggested Citation

  • Oliveira Panão, Marta J.N. & Mateus, Nuno M. & Carrilho da Graça, G., 2019. "Measured and modeled performance of internal mass as a thermal energy battery for energy flexible residential buildings," Applied Energy, Elsevier, vol. 239(C), pages 252-267.
    • Handle: RePEc:eee:appene:v:239:y:2019:i:c:p:252-267
    DOI: 10.1016/j.apenergy.2019.01.200
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919302120
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.01.200?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. Reilly, Aidan & Kinnane, Oliver, 2017. "The impact of thermal mass on building energy consumption," Applied Energy, Elsevier, vol. 198(C), pages 108-121.
    2. Le Dréau, J. & Heiselberg, P., 2016. "Energy flexibility of residential buildings using short term heat storage in the thermal mass," Energy, Elsevier, vol. 111(C), pages 991-1002.
    3. Gouveia, João Pedro & Seixas, Júlia & Mestre, Ana, 2017. "Daily electricity consumption profiles from smart meters - Proxies of behavior for space heating and cooling," Energy, Elsevier, vol. 141(C), pages 108-122.
    4. Kwon, Pil Seok & Østergaard, Poul, 2014. "Assessment and evaluation of flexible demand in a Danish future energy scenario," Applied Energy, Elsevier, vol. 134(C), pages 309-320.
    5. Stinner, Sebastian & Huchtemann, Kristian & Müller, Dirk, 2016. "Quantifying the operational flexibility of building energy systems with thermal energy storages," Applied Energy, Elsevier, vol. 181(C), pages 140-154.
    6. De Coninck, Roel & Helsen, Lieve, 2016. "Quantification of flexibility in buildings by cost curves – Methodology and application," Applied Energy, Elsevier, vol. 162(C), pages 653-665.
    7. 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.
    8. Verbeke, Stijn & Audenaert, Amaryllis, 2018. "Thermal inertia in buildings: A review of impacts across climate and building use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2300-2318.
    9. 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.
    10. Salpakari, Jyri & Rasku, Topi & Lindgren, Juuso & Lund, Peter D., 2017. "Flexibility of electric vehicles and space heating in net zero energy houses: an optimal control model with thermal dynamics and battery degradation," Applied Energy, Elsevier, vol. 190(C), pages 800-812.
    11. Reynders, Glenn & Diriken, Jan & Saelens, Dirk, 2017. "Generic characterization method for energy flexibility: Applied to structural thermal storage in residential buildings," Applied Energy, Elsevier, vol. 198(C), pages 192-202.
    12. Yin, Rongxin & Kara, Emre C. & Li, Yaping & DeForest, Nicholas & Wang, Ke & Yong, Taiyou & Stadler, Michael, 2016. "Quantifying flexibility of commercial and residential loads for demand response using setpoint changes," Applied Energy, Elsevier, vol. 177(C), pages 149-164.
    13. Johra, Hicham & Heiselberg, Per, 2017. "Influence of internal thermal mass on the indoor thermal dynamics and integration of phase change materials in furniture for building energy storage: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 19-32.
    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. Massimiliano Manfren & Maurizio Sibilla & Lamberto Tronchin, 2021. "Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector," Energies, MDPI, vol. 14(3), pages 1-29, January.
    2. 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).
    3. 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).
    4. Camille Pajot & Nils Artiges & Benoit Delinchant & Simon Rouchier & Frédéric Wurtz & Yves Maréchal, 2019. "An Approach to Study District Thermal Flexibility Using Generative Modeling from Existing Data," Energies, MDPI, vol. 12(19), pages 1-22, September.
    5. Massimiliano Manfren & Karla M. Gonzalez-Carreon & Patrick A. B. James, 2024. "Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps," Energies, MDPI, vol. 17(4), pages 1-22, February.
    6. Silvia Erba & Alessandra Barbieri, 2022. "Measured Indoor Environmental Data in a Retrofitted Multiapartment Building to Assess Energy Flexibility and Thermal Safety during Winter Power Outages," Data, MDPI, vol. 7(7), pages 1-14, July.
    7. Ren, Haoshan & Sun, Yongjun & Albdoor, Ahmed K. & Tyagi, V.V. & Pandey, A.K. & Ma, Zhenjun, 2021. "Improving energy flexibility of a net-zero energy house using a solar-assisted air conditioning system with thermal energy storage and demand-side management," Applied Energy, Elsevier, vol. 285(C).
    8. Zahra Fallahi & Gregor P. Henze, 2019. "Interactive Buildings: A Review," Sustainability, MDPI, vol. 11(14), pages 1-26, July.
    9. Perera, A.T.D. & Nik, Vahid M. & Wickramasinghe, P.U. & Scartezzini, Jean-Louis, 2019. "Redefining energy system flexibility for distributed energy system design," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Ziras, Charalampos & Heinrich, Carsten & Pertl, Michael & Bindner, Henrik W., 2019. "Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data," Applied Energy, Elsevier, vol. 242(C), pages 1407-1421.
    11. Silvia Erba & Lorenzo Pagliano, 2021. "Combining Sufficiency, Efficiency and Flexibility to Achieve Positive Energy Districts Targets," Energies, MDPI, vol. 14(15), pages 1-32, August.
    12. Michael D. Murphy & Paul D. O’Sullivan & Guilherme Carrilho da Graça & Adam O’Donovan, 2021. "Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods," Energies, MDPI, vol. 14(4), pages 1-24, February.
    13. Li, Yanxue & Wang, Zixuan & Xu, Wenya & Gao, Weijun & Xu, Yang & Xiao, Fu, 2023. "Modeling and energy dynamic control for a ZEH via hybrid model-based deep reinforcement learning," Energy, Elsevier, vol. 277(C).
    14. Xiaoyi Zhang & Weijun Gao & Yanxue Li & Zixuan Wang & Yoshiaki Ushifusa & Yingjun Ruan, 2021. "Operational Performance and Load Flexibility Analysis of Japanese Zero Energy House," IJERPH, MDPI, vol. 18(13), pages 1-19, June.
    15. Hyeunguk Ahn & Jingjing Liu & Donghun Kim & Rongxin Yin & Tianzhen Hong & Mary Ann Piette, 2021. "How Can Floor Covering Influence Buildings’ Demand Flexibility?," Energies, MDPI, vol. 14(12), pages 1-17, June.

    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. 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).
    2. 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.
    3. Li, Han & Johra, Hicham & de Andrade Pereira, Flavia & Hong, Tianzhen & Le Dréau, Jérôme & Maturo, Anthony & Wei, Mingjun & Liu, Yapan & Saberi-Derakhtenjani, Ali & Nagy, Zoltan & Marszal-Pomianowska,, 2023. "Data-driven key performance indicators and datasets for building energy flexibility: A review and perspectives," Applied Energy, Elsevier, vol. 343(C).
    4. 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).
    5. Finck, Christian & Li, Rongling & Zeiler, Wim, 2019. "Economic model predictive control for demand flexibility of a residential building," Energy, Elsevier, vol. 176(C), pages 365-379.
    6. Chen, Yongbao & Chen, Zhe & Xu, Peng & Li, Weilin & Sha, Huajing & Yang, Zhiwei & Li, Guowen & Hu, Chonghe, 2019. "Quantification of electricity flexibility in demand response: Office building case study," Energy, Elsevier, vol. 188(C).
    7. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    8. Liu, Mingzhe & Heiselberg, Per, 2019. "Energy flexibility of a nearly zero-energy building with weather predictive control on a convective building energy system and evaluated with different metrics," Applied Energy, Elsevier, vol. 233, pages 764-775.
    9. Nik, Vahid M. & Moazami, Amin, 2021. "Using collective intelligence to enhance demand flexibility and climate resilience in urban areas," Applied Energy, Elsevier, vol. 281(C).
    10. Bampoulas, Adamantios & Saffari, Mohammad & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2021. "A fundamental unified framework to quantify and characterise energy flexibility of residential buildings with multiple electrical and thermal energy systems," Applied Energy, Elsevier, vol. 282(PA).
    11. Sebastian Berg & Lasse Blaume & Benedikt Nilges, 2023. "Quantifying the Operational Flexibility of Distributed Cross-Sectoral Energy Systems for the Integration of Volatile Renewable Electricity Generation," Energies, MDPI, vol. 17(1), pages 1-17, December.
    12. Perera, A.T.D. & Nik, Vahid M. & Wickramasinghe, P.U. & Scartezzini, Jean-Louis, 2019. "Redefining energy system flexibility for distributed energy system design," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    13. Amadeh, Ali & Lee, Zachary E. & Zhang, K. Max, 2022. "Quantifying demand flexibility of building energy systems under uncertainty," Energy, Elsevier, vol. 246(C).
    14. 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).
    15. Junker, Rune Grønborg & Azar, Armin Ghasem & Lopes, Rui Amaral & Lindberg, Karen Byskov & Reynders, Glenn & Relan, Rishi & Madsen, Henrik, 2018. "Characterizing the energy flexibility of buildings and districts," Applied Energy, Elsevier, vol. 225(C), pages 175-182.
    16. Awan, Muhammad Bilal & Sun, Yongjun & Lin, Wenye & Ma, Zhenjun, 2023. "A framework to formulate and aggregate performance indicators to quantify building energy flexibility," Applied Energy, Elsevier, vol. 349(C).
    17. Ziras, Charalampos & Heinrich, Carsten & Pertl, Michael & Bindner, Henrik W., 2019. "Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data," Applied Energy, Elsevier, vol. 242(C), pages 1407-1421.
    18. Ehsan Khorsandnejad & Robert Malzahn & Ann-Katrin Oldenburg & Annedore Mittreiter & Christian Doetsch, 2023. "Analysis of Flexibility Potential of a Cold Warehouse with Different Refrigeration Compressors," Energies, MDPI, vol. 17(1), pages 1-22, December.
    19. Zhu, Jie & Niu, Jide & Tian, Zhe & Zhou, Ruoyu & Ye, Chuang, 2022. "Rapid quantification of demand response potential of building HAVC system via data-driven model," Applied Energy, Elsevier, vol. 325(C).
    20. Arteconi, Alessia & Mugnini, Alice & Polonara, Fabio, 2019. "Energy flexible buildings: A methodology for rating the flexibility performance of buildings with electric heating and cooling systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.

    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:239:y:2019:i:c:p:252-267. 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.