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Energy flexibility of a nearly zero-energy building with weather predictive control on a convective building energy system and evaluated with different metrics

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  • Liu, Mingzhe
  • Heiselberg, Per

Abstract

The implementation of energy flexibility using thermal storage of building structure is one of the key solutions for buildings in contributing to a stable exploitation and distribution of renewable energy. The objective of this study is to investigate the performance of energy flexibility of a nearly zero-energy building with weather predictive control of a convective building energy system. Analysis and comparisons have been conducted on different control strategies to evaluate the influence of the strategies on the energy flexibility. The investigated control strategies of a heating and cooling system include: normal control (reference case), adjustment of set-points for heating and cooling and adjustment of set-points together with a rule-based weather predictive control strategy.

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  • 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.
    • Handle: RePEc:eee:appene:v:233-234:y:2019:i::p:764-775
    DOI: 10.1016/j.apenergy.2018.10.070
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    Cited by:

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    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. João Tabanêz Patrício & Rui Amaral Lopes & Naim Majdalani & Daniel Aelenei & João Martins, 2023. "Aggregated Use of Energy Flexibility in Office Buildings," Energies, MDPI, vol. 16(2), pages 1-17, January.
    4. 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).
    5. Walker, Linus & Hischier, Illias & Schlueter, Arno, 2022. "Scenario-based robustness assessment of building system life cycle performance," Applied Energy, Elsevier, vol. 311(C).
    6. O'Connell, Sarah & Reynders, Glenn & Keane, Marcus M., 2021. "Impact of source variability on flexibility for demand response," Energy, Elsevier, vol. 237(C).
    7. 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.
    8. 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.
    9. Pengying Wang & Shuo Zhang, 2022. "Retrofitting Strategies Based on Orthogonal Array Testing to Develop Nearly Zero Energy Buildings," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    10. Khalilnejad, Arash & French, Roger H. & Abramson, Alexis R., 2020. "Data-driven evaluation of HVAC operation and savings in commercial buildings," Applied Energy, Elsevier, vol. 278(C).
    11. Karol Bot & Laura Aelenei & Maria da Glória Gomes & Carlos Santos Silva, 2020. "Performance Assessment of a Building Integrated Photovoltaic Thermal System in Mediterranean Climate—A Numerical Simulation Approach," Energies, MDPI, vol. 13(11), pages 1-25, June.
    12. 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).
    13. Wang, Chuyao & Ji, Jie & Yang, Hongxing, 2024. "Day-ahead schedule optimization of household appliances for demand flexibility: Case study on PV/T powered buildings," Energy, Elsevier, vol. 289(C).
    14. Pallonetto, Fabiano & De Rosa, Mattia & Milano, Federico & Finn, Donal P., 2019. "Demand response algorithms for smart-grid ready residential buildings using machine learning models," Applied Energy, Elsevier, vol. 239(C), pages 1265-1282.

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