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A top-down control method of nZEBs for performance optimization at nZEB-cluster-level

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  • Huang, Pei
  • Wu, Hunjun
  • Huang, Gongsheng
  • Sun, Yongjun

Abstract

Nearly zero energy buildings (NZEBs) are considered as a promising solution to the mitigation of the energy problems. A proper control of the energy system operation of the nZEB cluster is essential for improving load matching, reducing grid interaction and reducing energy bills. Existing studies have developed many demand response control methods to adjust the operation of energy systems to improve performances. Most of these studies focus on optimizing performances at individual-nZEB-level while neglecting collaborations (e.g. energy sharing and battery sharing) between nZEBs. Only a few studies consider the collaborations and optimize the system operation at nZEB-cluster-level, yet they cannot take full advantage of nZEB collaborations as optimization is conducted in a bottom-up manner lacking global coordination. This paper, therefore, proposes a top-down control method of nZEBs for optimizing performances at the cluster level. The top-down control method first considers the nZEB cluster as ‘one’ and optimizes its energy system operation using the genetic algorithm (GA), and then it coordinates the operation of every single nZEB inside the cluster using non-linear programming (NLP). The top-down control enables collaborations among nZEBs by coordinating single nZEB's operations. Such collaborations can bring significant performance improvements in different aspects. For instance, in aspect of economic cost, the collaborations can reduce the high-priced energy imports from the grid by sharing the surplus renewable energy with nZEBs which have insufficient energy generations. The proposed top-down control has been compared with a traditional non-collaborative control. The study results show that the top-down control is effective in improving performances at cluster level.

Suggested Citation

  • Huang, Pei & Wu, Hunjun & Huang, Gongsheng & Sun, Yongjun, 2018. "A top-down control method of nZEBs for performance optimization at nZEB-cluster-level," Energy, Elsevier, vol. 159(C), pages 891-904.
    • Handle: RePEc:eee:energy:v:159:y:2018:i:c:p:891-904
    DOI: 10.1016/j.energy.2018.06.199
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    References listed on IDEAS

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    Cited by:

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    3. Huang, Pei & Sun, Yongjun, 2019. "A robust control of nZEBs for performance optimization at cluster level under demand prediction uncertainty," Renewable Energy, Elsevier, vol. 134(C), pages 215-227.
    4. Huang, Pei & Sun, Yongjun, 2019. "A clustering based grouping method of nearly zero energy buildings for performance improvements," Applied Energy, Elsevier, vol. 235(C), pages 43-55.
    5. Francesca Ceglia & Elisa Marrasso & Carlo Roselli & Maurizio Sasso, 2021. "Small Renewable Energy Community: The Role of Energy and Environmental Indicators for Power Grid," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    6. Huang, Pei & Lovati, Marco & Zhang, Xingxing & Bales, Chris, 2020. "A coordinated control to improve performance for a building cluster with energy storage, electric vehicles, and energy sharing considered," Applied Energy, Elsevier, vol. 268(C).
    7. 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.
    8. Zhang, Shicong & Wang, Ke & Xu, Wei & Iyer-Raniga, Usha & Athienitis, Andreas & Ge, Hua & Cho, Dong woo & Feng, Wei & Okumiya, Masaya & Yoon, Gyuyoung & Mazria, Edward & Lyu, Yanjie, 2021. "Policy recommendations for the zero energy building promotion towards carbon neutral in Asia-Pacific Region," Energy Policy, Elsevier, vol. 159(C).
    9. Huang, Pei & Fan, Cheng & Zhang, Xingxing & Wang, Jiayuan, 2019. "A hierarchical coordinated demand response control for buildings with improved performances at building group," Applied Energy, Elsevier, vol. 242(C), pages 684-694.
    10. 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).
    11. Zhang, Yelin & Zhang, Xingxing & Huang, Pei & Sun, Yongjun, 2020. "Global sensitivity analysis for key parameters identification of net-zero energy buildings for grid interaction optimization," Applied Energy, Elsevier, vol. 279(C).
    12. Cai, Qiran & Xu, Qingyang & Qing, Jing & Shi, Gang & Liang, Qiao-Mei, 2022. "Promoting wind and photovoltaics renewable energy integration through demand response: Dynamic pricing mechanism design and economic analysis for smart residential communities," Energy, Elsevier, vol. 261(PB).
    13. Chi, Fang'ai & Xu, Liming & Pan, Jiajie & Wang, Ruonan & Tao, Yekang & Guo, Yuang & Peng, Changhai, 2020. "Prediction of the total day-round thermal load for residential buildings at various scales based on weather forecast data," Applied Energy, Elsevier, vol. 280(C).
    14. Mottaghizadeh, Pegah & Jabbari, Faryar & Brouwer, Jack, 2022. "Integrated solid oxide fuel cell, solar PV, and battery storage system to achieve zero net energy residential nanogrid in California," Applied Energy, Elsevier, vol. 323(C).

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