IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v188y2019ics0360544219317499.html
   My bibliography  Save this article

Quantification of electricity flexibility in demand response: Office building case study

Author

Listed:
  • Chen, Yongbao
  • Chen, Zhe
  • Xu, Peng
  • Li, Weilin
  • Sha, Huajing
  • Yang, Zhiwei
  • Li, Guowen
  • Hu, Chonghe

Abstract

Electric demand flexibility in buildings has been deemed to be a promising demand response resource, particularly for large commercial buildings, and it can provide grid-responsive support. A building with a higher electricity flexibility potential has a higher degree of involvement with the grid response. If the electricity flexibility potential of a building is known, building operators can properly alleviate peak loads and maximize economic benefits through precise control in demand response programs. Previously, there was no standard way to quantify electricity flexibility, and it was difficult to evaluate a given building without experiments and tests. Thus, a systematic approach is proposed to quantify building electricity flexibility. The flexibility contributions include building thermal mass; lights; heating, ventilation, and air conditioning (HVAC) systems, and occupant behaviors. This proposed model has been validated by the instantiation of an office building case on the Dymola platform. For a typical office building, the results show that the electricity flexibility resource not only comes from the HVAC system, but also thermal mass and occupant behavior to a large degree, and buildings with energy flexibility can cut down much of their load during peak load time without compromising on the occupant's comfort.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:energy:v:188:y:2019:i:c:s0360544219317499
    DOI: 10.1016/j.energy.2019.116054
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544219317499
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2019.116054?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. 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.
    2. 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.
    3. Arteconi, A. & Hewitt, N.J. & Polonara, F., 2012. "State of the art of thermal storage for demand-side management," Applied Energy, Elsevier, vol. 93(C), pages 371-389.
    4. 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.
    5. Mohseni, Amin & Mortazavi, Seyed Saeidollah & Ghasemi, Ahmad & Nahavandi, Ali & Talaei abdi, Masoud, 2017. "The application of household appliances' flexibility by set of sequential uninterruptible energy phases model in the day-ahead planning of a residential microgrid," Energy, Elsevier, vol. 139(C), pages 315-328.
    6. Chen, Yongbao & Xu, Peng & Chu, Yiyi & Li, Weilin & Wu, Yuntao & Ni, Lizhou & Bao, Yi & Wang, Kun, 2017. "Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings," Applied Energy, Elsevier, vol. 195(C), pages 659-670.
    7. Zhang, Lingxi & Good, Nicholas & Mancarella, Pierluigi, 2019. "Building-to-grid flexibility: Modelling and assessment metrics for residential demand response from heat pump aggregations," Applied Energy, Elsevier, vol. 233, pages 709-723.
    8. 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.
    9. Sehar, Fakeha & Pipattanasomporn, Manisa & Rahman, Saifur, 2016. "An energy management model to study energy and peak power savings from PV and storage in demand responsive buildings," Applied Energy, Elsevier, vol. 173(C), pages 406-417.
    10. 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.
    11. Rodríguez-García, Javier & Álvarez-Bel, Carlos & Carbonell-Carretero, José-Francisco & Alcázar-Ortega, Manuel & Peñalvo-López, Elisa, 2016. "A novel tool for the evaluation and assessment of demand response activities in the industrial sector," Energy, Elsevier, vol. 113(C), pages 1136-1146.
    12. Razmara, M. & Bharati, G.R. & Hanover, Drew & Shahbakhti, M. & Paudyal, S. & Robinett, R.D., 2017. "Building-to-grid predictive power flow control for demand response and demand flexibility programs," Applied Energy, Elsevier, vol. 203(C), pages 128-141.
    13. Xue, Xue & Wang, Shengwei & Sun, Yongjun & Xiao, Fu, 2014. "An interactive building power demand management strategy for facilitating smart grid optimization," Applied Energy, Elsevier, vol. 116(C), pages 297-310.
    14. Nuytten, Thomas & Claessens, Bert & Paredis, Kristof & Van Bael, Johan & Six, Daan, 2013. "Flexibility of a combined heat and power system with thermal energy storage for district heating," Applied Energy, Elsevier, vol. 104(C), pages 583-591.
    15. 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.
    16. Hedegaard, Karsten & Mathiesen, Brian Vad & Lund, Henrik & Heiselberg, Per, 2012. "Wind power integration using individual heat pumps – Analysis of different heat storage options," Energy, Elsevier, vol. 47(1), pages 284-293.
    17. Turner, W.J.N. & Walker, I.S. & Roux, J., 2015. "Peak load reductions: Electric load shifting with mechanical pre-cooling of residential buildings with low thermal mass," Energy, Elsevier, vol. 82(C), pages 1057-1067.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. 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).
    3. 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.
    4. 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).
    5. 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.
    6. 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.
    7. 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).
    8. Alessia Arteconi & Fabio Polonara, 2018. "Assessing the Demand Side Management Potential and the Energy Flexibility of Heat Pumps in Buildings," Energies, MDPI, vol. 11(7), pages 1-19, July.
    9. 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.
    10. 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.
    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. 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).
    13. Monika Hall & Achim Geissler, 2020. "Load Control by Demand Side Management to Support Grid Stability in Building Clusters," Energies, MDPI, vol. 13(19), pages 1-15, October.
    14. 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).
    15. Pallonetto, Fabiano & De Rosa, Mattia & D’Ettorre, Francesco & Finn, Donal P., 2020. "On the assessment and control optimisation of demand response programs in residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    16. Amadeh, Ali & Lee, Zachary E. & Zhang, K. Max, 2022. "Quantifying demand flexibility of building energy systems under uncertainty," Energy, Elsevier, vol. 246(C).
    17. 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.
    18. Wei, Zhichen & Calautit, John, 2023. "Predictive control of low-temperature heating system with passive thermal mass energy storage and photovoltaic system: Impact of occupancy patterns and climate change," Energy, Elsevier, vol. 269(C).
    19. Zhou, Yuekuan & Zheng, Siqian, 2020. "Machine-learning based hybrid demand-side controller for high-rise office buildings with high energy flexibilities," Applied Energy, Elsevier, vol. 262(C).
    20. Dmytro Romanchenko & Emil Nyholm & Mikael Odenberger & Filip Johnsson, 2019. "Flexibility Potential of Space Heating Demand Response in Buildings for District Heating Systems," Energies, MDPI, vol. 12(15), pages 1-23, July.

    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:energy:v:188:y:2019:i:c:s0360544219317499. 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.journals.elsevier.com/energy .

    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.