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Building-group-level performance evaluations of net zero energy buildings with non-collaborative controls

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  • Sun, Yongjun
  • Huang, Gongsheng
  • Xu, Xinhua
  • Lai, Alvin Chi-Keung

Abstract

Net zero energy building (NZEB) is widely considered an effective solution to the increasing energy and environmental problems. Focusing on performance optimizations at single-building-level, conventional control strategies have not considered collaborations among NZEBs (such as renewable energy sharing) and thus optimal results may not be able to be achieved at a higher level, i.e. building-group-level. Few studies have evaluated the building-group-level performance of NZEBs using non-collaborative controls and researchers are still unclear about the performance improvement potentials if collaborative controls are implemented. Considering economic cost, load matching and grid interaction, this study aims to evaluate the performance of a group of NZEBs in which conventional controls are used. Meanwhile, associated performance improvement potentials are analyzed at building-group-level. The study results show that significant performance improvement potentials exist if NZEB collaborations are enabled. The study results also analyze the benefits of the NZEB collaborations in the three different aspects. To improve NZEB performance at building-group-level, new collaborative controls need to be developed to replace the conventional ones.

Suggested Citation

  • Sun, Yongjun & Huang, Gongsheng & Xu, Xinhua & Lai, Alvin Chi-Keung, 2018. "Building-group-level performance evaluations of net zero energy buildings with non-collaborative controls," Applied Energy, Elsevier, vol. 212(C), pages 565-576.
    • Handle: RePEc:eee:appene:v:212:y:2018:i:c:p:565-576
    DOI: 10.1016/j.apenergy.2017.11.076
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    Cited by:

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    7. Lu, Yuehong & Zhang, Xiao-Ping & Huang, Zhijia & Lu, Jinli & Wang, Dong, 2019. "Impact of introducing penalty-cost on optimal design of renewable energy systems for net zero energy buildings," Applied Energy, Elsevier, vol. 235(C), pages 106-116.
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    9. Ran, Fengming & Gao, Dian-ce & Zhang, Xu & Chen, Shuyue, 2020. "A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications," Applied Energy, Elsevier, vol. 269(C).
    10. Fan, Cheng & Huang, Gongsheng & Sun, Yongjun, 2018. "A collaborative control optimization of grid-connected net zero energy buildings for performance improvements at building group level," Energy, Elsevier, vol. 164(C), pages 536-549.
    11. Lu, Yuehong & Zhang, Xiao-Ping & Li, Jianing & Huang, Zhijia & Wang, Changlong & Luo, Liang, 2019. "Design of a reward-penalty cost for the promotion of net-zero energy buildings," Energy, Elsevier, vol. 180(C), pages 36-49.
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    14. Huang, Pei & Sun, Yongjun, 2019. "A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level," Energy, Elsevier, vol. 174(C), pages 911-921.
    15. May, Ross & Huang, Pei, 2023. "A multi-agent reinforcement learning approach for investigating and optimising peer-to-peer prosumer energy markets," Applied Energy, Elsevier, vol. 334(C).
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    18. Huang, Pei & Han, Mengjie & Zhang, Xingxing & Hussain, Syed Asad & Jayprakash Bhagat, Rohit & Hogarehalli Kumar, Deepu, 2022. "Characterization and optimization of energy sharing performances in energy-sharing communities in Sweden, Canada and Germany," Applied Energy, Elsevier, vol. 326(C).
    19. Zheng, Siqian & Huang, Gongsheng & Lai, Alvin CK., 2021. "Techno-economic performance analysis of synergistic energy sharing strategies for grid-connected prosumers with distributed battery storages," Renewable Energy, Elsevier, vol. 178(C), pages 1261-1278.
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