IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i10p2787-d553376.html
   My bibliography  Save this article

Quantification of Energy Flexibility and Survivability of All-Electric Buildings with Cost-Effective Battery Size: Methodology and Indexes

Author

Listed:
  • Shabnam Homaei

    (Department of Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway)

  • Mohamed Hamdy

    (Department of Civil and Environmental Engineering, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway)

Abstract

All-electric buildings are playing an important role in the electrification plan towards energy-neutral smart cities. Batteries are key components in all-electric buildings that can help the demand-side energy management as a flexibility asset and improve the building survivability in the case of power outages as an active survivability asset. This paper introduces a novel methodology and indexes for determining cost-effective battery sizes. It also explores the possible trade-off between energy flexibility and the survivability of all-electric buildings. The introduced methodology uses IDA-ICE 4.8 as a building performance simulation tool and MATLAB ® 2017 as a post-processing calculation tool for quantifying building energy flexibility and survivability indexes. The proposed methodology is applied to a case study of a Norwegian single-family house, where 10 competitive designs, 16 uncertainty scenarios, and 3 dynamic pricing tariffs suggested by the Norwegian regulators are investigated. The methodology provides informative support for different stakeholders to compare various building designs and dynamic pricing tariffs from the flexibility and survivability points of view. Overall, the results indicate that larger cost-effective batteries usually have higher active survivability and lower energy flexibility from cost- effectiveness perspective. For instance, when the time of use tariff is applied, the cost-effective battery size varies between 40 and 65 kWh (daily storage). This is associated with a cost-effective flexibility index of 0.4–0.55%/kWh and an active survivability index of 63–80%.

Suggested Citation

  • Shabnam Homaei & Mohamed Hamdy, 2021. "Quantification of Energy Flexibility and Survivability of All-Electric Buildings with Cost-Effective Battery Size: Methodology and Indexes," Energies, MDPI, vol. 14(10), pages 1-32, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2787-:d:553376
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/10/2787/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/10/2787/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Clauß, John & Stinner, Sebastian & Sartori, Igor & Georges, Laurent, 2019. "Predictive rule-based control to activate the energy flexibility of Norwegian residential buildings: Case of an air-source heat pump and direct electric heating," Applied Energy, Elsevier, vol. 237(C), pages 500-518.
    2. Hu, Maomao & Xiao, Fu, 2020. "Quantifying uncertainty in the aggregate energy flexibility of high-rise residential building clusters considering stochastic occupancy and occupant behavior," Energy, Elsevier, vol. 194(C).
    3. Niu, Jide & Tian, Zhe & Lu, Yakai & Zhao, Hongfang, 2019. "Flexible dispatch of a building energy system using building thermal storage and battery energy storage," Applied Energy, Elsevier, vol. 243(C), pages 274-287.
    4. Nord, Natasa & Qvistgaard, Live Holmedal & Cao, Guangyu, 2016. "Identifying key design parameters of the integrated energy system for a residential Zero Emission Building in Norway," Renewable Energy, Elsevier, vol. 87(P3), pages 1076-1087.
    5. 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.
    6. Shen, Bo & Ghatikar, Girish & Lei, Zeng & Li, Jinkai & Wikler, Greg & Martin, Phil, 2014. "The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges," Applied Energy, Elsevier, vol. 130(C), pages 814-823.
    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. Nastasi, Benedetto & Mazzoni, Stefano & Groppi, Daniele & Romagnoli, Alessandro & Astiaso Garcia, Davide, 2021. "Solar power-to-gas application to an island energy system," Renewable Energy, Elsevier, vol. 164(C), pages 1005-1016.
    9. Prehoda, Emily W. & Schelly, Chelsea & Pearce, Joshua M., 2017. "U.S. strategic solar photovoltaic-powered microgrid deployment for enhanced national security," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 167-175.
    10. Francesco Mancini & Benedetto Nastasi, 2019. "Energy Retrofitting Effects on the Energy Flexibility of Dwellings," Energies, MDPI, vol. 12(14), pages 1-19, July.
    11. Tsianikas, Stamatis & Zhou, Jian & Birnie, Dunbar P. & Coit, David W., 2019. "Economic trends and comparisons for optimizing grid-outage resilient photovoltaic and battery systems," Applied Energy, Elsevier, vol. 256(C).
    12. Sani Hassan, Abubakar & Cipcigan, Liana & Jenkins, Nick, 2017. "Optimal battery storage operation for PV systems with tariff incentives," Applied Energy, Elsevier, vol. 203(C), pages 422-441.
    13. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    14. Nezamoddini, Nasim & Wang, Yong, 2016. "Risk management and participation planning of electric vehicles in smart grids for demand response," Energy, Elsevier, vol. 116(P1), pages 836-850.
    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. Homaei, Shabnam & Hamdy, Mohamed, 2020. "A robustness-based decision making approach for multi-target high performance buildings under uncertain scenarios," Applied Energy, Elsevier, vol. 267(C).
    17. Wang, Andong & Li, Rongling & You, Shi, 2018. "Development of a data driven approach to explore the energy flexibility potential of building clusters," Applied Energy, Elsevier, vol. 232(C), pages 89-100.
    18. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    19. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
    20. Wolfgang Loibl & Romana Stollnberger & Doris Österreicher, 2017. "Residential Heat Supply by Waste-Heat Re-Use: Sources, Supply Potential and Demand Coverage—A Case Study," Sustainability, MDPI, vol. 9(2), pages 1-19, February.
    21. Vandermeulen, Annelies & van der Heijde, Bram & Helsen, Lieve, 2018. "Controlling district heating and cooling networks to unlock flexibility: A review," Energy, Elsevier, vol. 151(C), pages 103-115.
    22. Goutam Dutta & Krishnendranath Mitra, 2017. "A literature review on dynamic pricing of electricity," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(10), pages 1131-1145, October.
    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. Ali Saberi Derakhtenjani & Andreas K. Athienitis, 2021. "Model Predictive Control Strategies to Activate the Energy Flexibility for Zones with Hydronic Radiant Systems," Energies, MDPI, vol. 14(4), pages 1-19, February.
    2. John Clauß & Sebastian Stinner & Christian Solli & Karen Byskov Lindberg & Henrik Madsen & Laurent Georges, 2019. "Evaluation Method for the Hourly Average CO 2eq. Intensity of the Electricity Mix and Its Application to the Demand Response of Residential Heating," Energies, MDPI, vol. 12(7), pages 1-25, April.
    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. Song, Yuguang & Xia, Mingchao & Chen, Qifang & Chen, Fangjian, 2023. "A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin," Applied Energy, Elsevier, vol. 332(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. Francesco Mancini & Sabrina Romano & Gianluigi Lo Basso & Jacopo Cimaglia & Livio de Santoli, 2020. "How the Italian Residential Sector Could Contribute to Load Flexibility in Demand Response Activities: A Methodology for Residential Clustering and Developing a Flexibility Strategy," Energies, MDPI, vol. 13(13), pages 1-25, July.
    7. Maitanova, Nailya & Schlüters, Sunke & Hanke, Benedikt & von Maydell, Karsten, 2024. "An analytical method for quantifying the flexibility potential of decentralised energy systems," Applied Energy, Elsevier, vol. 364(C).
    8. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    9. Yin, Linfei & Qiu, Yao, 2022. "Long-term price guidance mechanism of flexible energy service providers based on stochastic differential methods," Energy, Elsevier, vol. 238(PB).
    10. Paterakis, Nikolaos G. & Erdinç, Ozan & Catalão, João P.S., 2017. "An overview of Demand Response: Key-elements and international experience," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 871-891.
    11. Lucas Roth & Jens Lowitzsch & Özgür Yildiz, 2021. "An Empirical Study of How Household Energy Consumption Is Affected by Co-Owning Different Technological Means to Produce Renewable Energy and the Production Purpose," Energies, MDPI, vol. 14(13), pages 1-38, July.
    12. Bertsch, Valentin & Harold, Jason & Fell, Harrison, 2019. "Consumer preferences for end-use specific curtailable electricity contracts on household appliances during peak load hours," Papers WP632, Economic and Social Research Institute (ESRI).
    13. Zahra Fallahi & Gregor P. Henze, 2019. "Interactive Buildings: A Review," Sustainability, MDPI, vol. 11(14), pages 1-26, July.
    14. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2018. "Large-scale demand response and its implications for spot prices, load and policies: Insights from the German-Austrian electricity market," Applied Energy, Elsevier, vol. 210(C), pages 1290-1298.
    15. Derakhtenjani, Ali Saberi & Athienitis, Andreas K., 2021. "A frequency domain transfer function methodology for thermal characterization and design for energy flexibility of zones with radiant systems," Renewable Energy, Elsevier, vol. 163(C), pages 1033-1045.
    16. Gallardo, Andres & Berardi, Umberto, 2022. "Evaluation of the energy flexibility potential of radiant ceiling panels with thermal energy storage," Energy, Elsevier, vol. 254(PC).
    17. Dominković, Dominik Franjo & Junker, Rune Grønborg & Lindberg, Karen Byskov & Madsen, Henrik, 2020. "Implementing flexibility into energy planning models: Soft-linking of a high-level energy planning model and a short-term operational model," Applied Energy, Elsevier, vol. 260(C).
    18. Guerrero, Jaysson & Gebbran, Daniel & Mhanna, Sleiman & Chapman, Archie C. & Verbič, Gregor, 2020. "Towards a transactive energy system for integration of distributed energy resources: Home energy management, distributed optimal power flow, and peer-to-peer energy trading," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    19. Coccia, Gianluca & Mugnini, Alice & Polonara, Fabio & Arteconi, Alessia, 2021. "Artificial-neural-network-based model predictive control to exploit energy flexibility in multi-energy systems comprising district cooling," Energy, Elsevier, vol. 222(C).
    20. Harder, Nick & Qussous, Ramiz & Weidlich, Anke, 2020. "The cost of providing operational flexibility from distributed energy resources," Applied Energy, Elsevier, vol. 279(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:gam:jeners:v:14:y:2021:i:10:p:2787-:d:553376. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.