IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v195y2017icp1047-1054.html
   My bibliography  Save this article

Quantifying demand flexibility based on structural thermal storage and comfort management of non-residential buildings: A comparison between hot and cold climate zones

Author

Listed:
  • Hurtado, L.A.
  • Rhodes, J.D.
  • Nguyen, P.H.
  • Kamphuis, I.G.
  • Webber, M.E.

Abstract

Recently, demand flexibility has been highlighted as a promising distributed resource from the customer side, especially from industrial customers like commercial buildings, capable of providing grid support services. However, the quantification of demand flexibility is a complex process that requires a methodology including the requirements of both the grid operators and the customers. This paper proposes a novel approach to quantify the available demand flexibility of individual buildings, while taking into account the underlying building energy physics. The proposed approach constructs on the operational flexibility concept from the power systems, and extends it to include a comfort domain, identifying different flexibility parameters with the aim of giving a better insight into the flexibility potential of commercial buildings. This method includes a development of building energy simulations to assess the effects of weather variations, construction types, and comfort constrains on demand flexibility. The proposed quantification method has been validated using 15 different office building models and two different climate zones, i.e., the Netherlands and Texas, US. The results presented in this paper suggest that buildings located in a hot climate could offer higher flexibility potential during shorter time ranges, while buildings in a cold climate could offer lower flexibility potential but during longer time ranges. Determining these differences could potentially facilitate the dispatch of flexible demand resources, to assess their real potential, and to schedule demand flexibility between stakeholders.

Suggested Citation

  • Hurtado, L.A. & Rhodes, J.D. & Nguyen, P.H. & Kamphuis, I.G. & Webber, M.E., 2017. "Quantifying demand flexibility based on structural thermal storage and comfort management of non-residential buildings: A comparison between hot and cold climate zones," Applied Energy, Elsevier, vol. 195(C), pages 1047-1054.
    • Handle: RePEc:eee:appene:v:195:y:2017:i:c:p:1047-1054
    DOI: 10.1016/j.apenergy.2017.03.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261917302210
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2017.03.004?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. Kondziella, Hendrik & Bruckner, Thomas, 2016. "Flexibility requirements of renewable energy based electricity systems – a review of research results and methodologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 10-22.
    2. Xue, Xue & Wang, Shengwei & Yan, Chengchu & Cui, Borui, 2015. "A fast chiller power demand response control strategy for buildings connected to smart grid," Applied Energy, Elsevier, vol. 137(C), pages 77-87.
    3. Alizadeh, M.I. & Parsa Moghaddam, M. & Amjady, N. & Siano, P. & Sheikh-El-Eslami, M.K., 2016. "Flexibility in future power systems with high renewable penetration: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1186-1193.
    4. 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.
    5. Rajeev, T. & Ashok, S., 2015. "Dynamic load-shifting program based on a cloud computing framework to support the integration of renewable energy sources," Applied Energy, Elsevier, vol. 146(C), pages 141-149.
    6. Quan, Hao & Srinivasan, Dipti & Khambadkone, Ashwin M. & Khosravi, Abbas, 2015. "A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources," Applied Energy, Elsevier, vol. 152(C), pages 71-82.
    7. Shi, Huaizhou & Blaauwbroek, Niels & Nguyen, Phuong H. & Kamphuis, René (I.G.), 2016. "Energy management in Multi-Commodity Smart Energy Systems with a greedy approach," Applied Energy, Elsevier, vol. 167(C), pages 385-396.
    8. Arteconi, Alessia & Ciarrocchi, Eleonora & Pan, Quanwen & Carducci, Francesco & Comodi, Gabriele & Polonara, Fabio & Wang, Ruzhu, 2017. "Thermal energy storage coupled with PV panels for demand side management of industrial building cooling loads," Applied Energy, Elsevier, vol. 185(P2), pages 1984-1993.
    9. Stötzer, Martin & Hauer, Ines & Richter, Marc & Styczynski, Zbigniew A., 2015. "Potential of demand side integration to maximize use of renewable energy sources in Germany," Applied Energy, Elsevier, vol. 146(C), pages 344-352.
    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. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    12. Gao, Dian-ce & Sun, Yongjun & Lu, Yuehong, 2015. "A robust demand response control of commercial buildings for smart grid under load prediction uncertainty," Energy, Elsevier, vol. 93(P1), pages 275-283.
    13. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2016. "Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage," Applied Energy, Elsevier, vol. 163(C), pages 93-104.
    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, Jianhui & Chen, Wujun & Zhang, Sihao & Yin, Yue & Li, Yipo & Yang, Deqing, 2018. "Thermal characteristics and comfort assessment of enclosed large-span membrane stadiums," Applied Energy, Elsevier, vol. 229(C), pages 728-735.
    2. Li, Yinan & Yang, Wentao & He, Ping & Chen, Chang & Wang, Xiaonan, 2019. "Design and management of a distributed hybrid energy system through smart contract and blockchain," Applied Energy, Elsevier, vol. 248(C), pages 390-405.
    3. Huang, Sen & Ye, Yunyang & Wu, Di & Zuo, Wangda, 2021. "An assessment of power flexibility from commercial building cooling systems in the United States," Energy, Elsevier, vol. 221(C).
    4. Klein, Konstantin & Herkel, Sebastian & Henning, Hans-Martin & Felsmann, Clemens, 2017. "Load shifting using the heating and cooling system of an office building: Quantitative potential evaluation for different flexibility and storage options," Applied Energy, Elsevier, vol. 203(C), pages 917-937.
    5. 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).
    6. Muhanji, Steffi O. & Farid, Amro M., 2020. "An enterprise control methodology for the techno-economic assessment of the energy water nexus," Applied Energy, Elsevier, vol. 260(C).
    7. Ma, Huan & Sun, Qinghan & Chen, Qun & Zhao, Tian & He, Kelun, 2023. "Exergy-based flexibility cost indicator and spatio-temporal coordination principle of distributed multi-energy systems," Energy, Elsevier, vol. 267(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. Heidenthaler, Daniel & Leeb, Markus & Schnabel, Thomas & Huber, Hermann, 2021. "Comparative analysis of thermally activated building systems in wooden and concrete structures regarding functionality and energy storage on a simulation-based approach," Energy, Elsevier, vol. 233(C).
    10. Jennifer Date & José A. Candanedo & Andreas K. Athienitis, 2021. "A Methodology for the Enhancement of the Energy Flexibility and Contingency Response of a Building through Predictive Control of Passive and Active Storage," Energies, MDPI, vol. 14(5), pages 1-28, March.
    11. Cui, Borui & Fan, Cheng & Munk, Jeffrey & Mao, Ning & Xiao, Fu & Dong, Jin & Kuruganti, Teja, 2019. "A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses," Applied Energy, Elsevier, vol. 236(C), pages 101-116.
    12. Lechl, Michael & Fürmann, Tim & de Meer, Hermann & Weidlich, Anke, 2023. "A review of models for energy system flexibility requirements and potentials using the new FLEXBLOX taxonomy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    13. Efkarpidis, Nikolaos A. & Vomva, Styliani A. & Christoforidis, Georgios C. & Papagiannis, Grigoris K., 2022. "Optimal day-to-day scheduling of multiple energy assets in residential buildings equipped with variable-speed heat pumps," Applied Energy, Elsevier, vol. 312(C).
    14. Gou, Xing & Chen, Qun & He, Ke-Lun, 2022. "Real-time quantification for dynamic heat storage characteristic of district heating system and its application in dispatch of integrated energy system," Energy, Elsevier, vol. 259(C).
    15. Chul-Ho Kim & Seung-Eon Lee & Kang-Soo Kim, 2018. "Analysis of Energy Saving Potential in High-Performance Building Technologies under Korean Climatic Conditions," Energies, MDPI, vol. 11(4), pages 1-34, April.
    16. Luo, Na & Langevin, Jared & Chandra-Putra, Handi & Lee, Sang Hoon, 2022. "Quantifying the effect of multiple load flexibility strategies on commercial building electricity demand and services via surrogate modeling," Applied Energy, Elsevier, vol. 309(C).
    17. Yuchun Li & Yinghua Han & Jinkuan Wang & Qiang Zhao, 2018. "A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort," Energies, MDPI, vol. 11(12), pages 1-20, December.
    18. Liang, Zheming & Bian, Desong & Zhang, Xiaohu & Shi, Di & Diao, Ruisheng & Wang, Zhiwei, 2019. "Optimal energy management for commercial buildings considering comprehensive comfort levels in a retail electricity market," Applied Energy, Elsevier, vol. 236(C), pages 916-926.
    19. Matthias Eydner & Lu Wan & Tobias Henzler & Konstantinos Stergiaropoulos, 2022. "Real-Time Grid Signal-Based Energy Flexibility of Heating Generation: A Methodology for Optimal Scheduling of Stratified Storage Tanks," Energies, MDPI, vol. 15(5), pages 1-31, February.

    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. 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. 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.
    3. Wang, Huilong & Wang, Shengwei & Tang, Rui, 2019. "Development of grid-responsive buildings: Opportunities, challenges, capabilities and applications of HVAC systems in non-residential buildings in providing ancillary services by fast demand responses," Applied Energy, Elsevier, vol. 250(C), pages 697-712.
    4. Harder, Nick & Qussous, Ramiz & Weidlich, Anke, 2020. "The cost of providing operational flexibility from distributed energy resources," Applied Energy, Elsevier, vol. 279(C).
    5. Ayón, X. & Gruber, J.K. & Hayes, B.P. & Usaola, J. & Prodanović, M., 2017. "An optimal day-ahead load scheduling approach based on the flexibility of aggregate demands," Applied Energy, Elsevier, vol. 198(C), pages 1-11.
    6. Cruz, Marco R.M. & Fitiwi, Desta Z. & Santos, Sérgio F. & Catalão, João P.S., 2018. "A comprehensive survey of flexibility options for supporting the low-carbon energy future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 338-353.
    7. Fu, Yangyang & O'Neill, Zheng & Wen, Jin & Pertzborn, Amanda & Bushby, Steven T., 2022. "Utilizing commercial heating, ventilating, and air conditioning systems to provide grid services: A review," Applied Energy, Elsevier, vol. 307(C).
    8. Cui, Borui & Gao, Dian-ce & Xiao, Fu & Wang, Shengwei, 2017. "Model-based optimal design of active cool thermal energy storage for maximal life-cycle cost saving from demand management in commercial buildings," Applied Energy, Elsevier, vol. 201(C), pages 382-396.
    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. 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.
    11. 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).
    12. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    13. Yuchun Li & Yinghua Han & Jinkuan Wang & Qiang Zhao, 2018. "A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort," Energies, MDPI, vol. 11(12), pages 1-20, December.
    14. Lee, Junghun & Yoo, Seunghwan & Kim, Jonghun & Song, Doosam & Jeong, Hakgeun, 2018. "Improvements to the customer baseline load (CBL) using standard energy consumption considering energy efficiency and demand response," Energy, Elsevier, vol. 144(C), pages 1052-1063.
    15. Gomez-Herrera, Juan A. & Anjos, Miguel F., 2018. "Optimal collaborative demand-response planner for smart residential buildings," Energy, Elsevier, vol. 161(C), pages 370-380.
    16. 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.
    17. Lau, E.T. & Yang, Q. & Stokes, L. & Taylor, G.A. & Forbes, A.B. & Clarkson, P. & Wright, P.S. & Livina, V.N., 2015. "Carbon savings in the UK demand side response programmes," Applied Energy, Elsevier, vol. 159(C), pages 478-489.
    18. Andre Leippi & Markus Fleschutz & Michael D. Murphy, 2022. "A Review of EV Battery Utilization in Demand Response Considering Battery Degradation in Non-Residential Vehicle-to-Grid Scenarios," Energies, MDPI, vol. 15(9), pages 1-22, April.
    19. Mauro Lafratta & Matthew Leach & Rex B. Thorpe & Mark Willcocks & Eve Germain & Sabeha K. Ouki & Achame Shana & Jacquetta Lee, 2021. "Economic and Carbon Costs of Electricity Balancing Services: The Need for Secure Flexible Low-Carbon Generation," Energies, MDPI, vol. 14(16), pages 1-21, August.
    20. 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).

    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:195:y:2017:i:c:p:1047-1054. 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.