IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i9p2357-d355542.html
   My bibliography  Save this article

Flattening the Electricity Demand Profile of Office Buildings for Future-Proof Smart Grids

Author

Listed:
  • Rick Cox

    (Department of the Built Environment, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands)

  • Shalika Walker

    (Department of the Built Environment, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands)

  • Joep van der Velden

    (Kropman Installatietechniek, Lagelandseweg 84, 6545 CG Nijmegen, The Netherlands)

  • Phuong Nguyen

    (Department of the Built Environment, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands)

  • Wim Zeiler

    (Department of the Built Environment, Eindhoven University of Technology, PO Box 513, 5600 MB Eindhoven, The Netherlands)

Abstract

The built environment has the potential to contribute to maintaining a reliable grid at the demand side by offering flexibility services to a future Smart Grid. In this study, an office building is used to demonstrate forecast-driven building energy flexibility by operating a Battery Electric Storage System (BESS). The objective of this study is, therefore, to stabilize/flatten a building energy demand profile with the operation of a BESS. First, electricity demand forecasting models are developed and assessed for each individual load group of the building based on their characteristics. For each load group, the prediction models show Coefficient of Variation of the Root Mean Square Error (CVRMSE) values below 30%, which indicates that the prediction models are suitable for use in engineering applications. An operational strategy is developed aiming at meeting the flattened electricity load shape objective. Both the simulation and experimental results show that the flattened load shape objective can be met more than 95% of the time for the evaluation period without compromising the thermal comfort of users. Accurate energy demand forecasting is shown to be pivotal for meeting load shape objectives.

Suggested Citation

  • Rick Cox & Shalika Walker & Joep van der Velden & Phuong Nguyen & Wim Zeiler, 2020. "Flattening the Electricity Demand Profile of Office Buildings for Future-Proof Smart Grids," Energies, MDPI, vol. 13(9), pages 1-27, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2357-:d:355542
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/9/2357/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/9/2357/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    2. Francesco Mancini & Benedetto Nastasi, 2019. "Energy Retrofitting Effects on the Energy Flexibility of Dwellings," Energies, MDPI, vol. 12(14), pages 1-19, July.
    3. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    4. Protopapadaki, Christina & Saelens, Dirk, 2017. "Heat pump and PV impact on residential low-voltage distribution grids as a function of building and district properties," Applied Energy, Elsevier, vol. 192(C), pages 268-281.
    5. Feng Qi & Fushuan Wen & Xunyuan Liu & Md. Abdus Salam, 2017. "A Residential Energy Hub Model with a Concentrating Solar Power Plant and Electric Vehicles," Energies, MDPI, vol. 10(8), pages 1-17, August.
    6. Louhichi, Kamel & Jacquet, Florence & Butault, Jean Pierre, 2012. "Estimating input allocation from heterogeneous data sources: A comparison of alternative estimation approaches," Agricultural Economics Review, Greek Association of Agricultural Economists, vol. 13(2), pages 1-20.
    7. Dirk Kuiken & Heyd F. Más & Maryam Haji Ghasemi & Niels Blaauwbroek & Thai H. Vo & Thijs Van der Klauw & Phuong H. Nguyen, 2018. "Energy Flexibility from Large Prosumers to Support Distribution System Operation—A Technical and Legal Case Study on the Amsterdam ArenA Stadium," Energies, MDPI, vol. 11(1), pages 1-29, January.
    8. Fischer, David & Madani, Hatef, 2017. "On heat pumps in smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 342-357.
    9. Walker, Shalika & Katic, Katarina & Maassen, Wim & Zeiler, Wim, 2019. "Multi-criteria feasibility assessment of cost-optimized alternatives to comply with heating demand of existing office buildings – A case study," Energy, Elsevier, vol. 187(C).
    10. Binod Prasad Koirala & José Pablo Chaves Ávila & Tomás Gómez & Rudi A. Hakvoort & Paulien M. Herder, 2016. "Local Alternative for Energy Supply: Performance Assessment of Integrated Community Energy Systems," Energies, MDPI, vol. 9(12), pages 1-24, November.
    11. Luthander, Rasmus & Widén, Joakim & Nilsson, Daniel & Palm, Jenny, 2015. "Photovoltaic self-consumption in buildings: A review," Applied Energy, Elsevier, vol. 142(C), pages 80-94.
    12. Nadeem Javaid & Sardar Mehboob Hussain & Ibrar Ullah & Muhammad Asim Noor & Wadood Abdul & Ahmad Almogren & Atif Alamri, 2017. "Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations," Energies, MDPI, vol. 10(8), pages 1-29, August.
    13. Salpakari, Jyri & Lund, Peter, 2016. "Optimal and rule-based control strategies for energy flexibility in buildings with PV," Applied Energy, Elsevier, vol. 161(C), pages 425-436.
    14. 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.
    15. Walker, Shalika & Labeodan, Timilehin & Boxem, Gert & Maassen, Wim & Zeiler, Wim, 2018. "An assessment methodology of sustainable energy transition scenarios for realizing energy neutral neighborhoods," Applied Energy, Elsevier, vol. 228(C), pages 2346-2360.
    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. Vladimir Z. Gjorgievski & Nikolas G. Chatzigeorgiou & Venizelos Venizelou & Georgios C. Christoforidis & George E. Georghiou & Grigoris K. Papagiannis, 2020. "Evaluation of Load Matching Indicators in Residential PV Systems-the Case of Cyprus," Energies, MDPI, vol. 13(8), pages 1-18, April.
    2. Joshua M. Pearce & Nelson Sommerfeldt, 2021. "Economics of Grid-Tied Solar Photovoltaic Systems Coupled to Heat Pumps: The Case of Northern Climates of the U.S. and Canada," Energies, MDPI, vol. 14(4), pages 1-17, February.
    3. Maria Pinamonti & Alessandro Prada & Paolo Baggio, 2020. "Rule-Based Control Strategy to Increase Photovoltaic Self-Consumption of a Modulating Heat Pump Using Water Storages and Building Mass Activation," Energies, MDPI, vol. 13(23), pages 1-21, November.
    4. Langer, Lissy & Volling, Thomas, 2020. "An optimal home energy management system for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 278(C).
    5. 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.
    6. Walker, Shalika & Katic, Katarina & Maassen, Wim & Zeiler, Wim, 2019. "Multi-criteria feasibility assessment of cost-optimized alternatives to comply with heating demand of existing office buildings – A case study," Energy, Elsevier, vol. 187(C).
    7. 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.
    8. Edmunds, Calum & Galloway, Stuart & Dixon, James & Bukhsh, Waqquas & Elders, Ian, 2021. "Hosting capacity assessment of heat pumps and optimised electric vehicle charging on low voltage networks," Applied Energy, Elsevier, vol. 298(C).
    9. Walker, Shalika & Bergkamp, Vince & Yang, Dujuan & van Goch, T.A.J. & Katic, Katarina & Zeiler, Wim, 2021. "Improving energy self-sufficiency of a renovated residential neighborhood with heat pumps by analyzing smart meter data," Energy, Elsevier, vol. 229(C).
    10. Wakui, Tetsuya & Sawada, Kento & Yokoyama, Ryohei & Aki, Hirohisa, 2019. "Predictive management for energy supply networks using photovoltaics, heat pumps, and battery by two-stage stochastic programming and rule-based control," Energy, Elsevier, vol. 179(C), pages 1302-1319.
    11. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    12. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Optimal energy management in all-electric residential energy systems with heat and electricity storage," Applied Energy, Elsevier, vol. 254(C).
    13. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    14. Wilke, Christoph & Bensmann, Astrid & Martin, Stefan & Utz, Annika & Hanke-Rauschenbach, Richard, 2018. "Optimal design of a district energy system including supply for fuel cell electric vehicles," Applied Energy, Elsevier, vol. 226(C), pages 129-144.
    15. Jolando M. Kisse & Martin Braun & Simon Letzgus & Tanja M. Kneiske, 2020. "A GIS-Based Planning Approach for Urban Power and Natural Gas Distribution Grids with Different Heat Pump Scenarios," Energies, MDPI, vol. 13(16), pages 1-31, August.
    16. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    17. Li, Sihui & Gong, Guangcai & Peng, Jinqing, 2019. "Dynamic coupling method between air-source heat pumps and buildings in China’s hot-summer/cold-winter zone," Applied Energy, Elsevier, vol. 254(C).
    18. 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.
    19. Nordgård-Hansen, Ellen & Kishor, Nand & Midttømme, Kirsti & Risinggård, Vetle Kjær & Kocbach, Jan, 2022. "Case study on optimal design and operation of detached house energy system: Solar, battery, and ground source heat pump," Applied Energy, Elsevier, vol. 308(C).
    20. Kuboth, Sebastian & Heberle, Florian & König-Haagen, Andreas & Brüggemann, Dieter, 2019. "Economic model predictive control of combined thermal and electric residential building energy systems," Applied Energy, Elsevier, vol. 240(C), pages 372-385.

    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:13:y:2020:i:9:p:2357-:d:355542. 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.