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A collaborative demand control of nearly zero energy buildings in response to dynamic pricing for performance improvements at cluster level

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  • Huang, Pei
  • Sun, Yongjun

Abstract

Collaborations (e.g. renewable energy sharing) among nearly zero energy buildings can improve performances at cluster level. Demand response control is helpful to enable such collaborations. Existing studies have developed some dynamic pricing demand response control methods to reduce the nearly zero energy building cluster’ electricity bills and eliminate the power grid's undesirable peaks. However, in these controls the collaborations among buildings are not allowed/enabled, since each building interacts with the grid and there is no direct interaction among buildings. Meanwhile, for performance optimizations at building cluster level, the computation costs of these non-collaborative controls are excessively high especially as a number of buildings considered. Therefore, this study proposes a collaborative demand response of nearly zero energy buildings in response to dynamic pricing for cluster-level performance improvements. Considering the building cluster as one ‘lumped’ building, in which the renewable generations, energy demands and battery capacities of individual buildings are aggregated, the collaborative control first identifies the optimal performance at cluster level in response to the dynamic pricing. Then, based on the identified optimal performance, the proposed control coordinates individual buildings' operations using non-linear programming, thereby realizing the collaborations. For validation, the proposed collaborative demand response control is compared with a game-theory based non-collaborative demand response control. The developed control effectively reduces the cluster-level peak energy exchanges and electricity bills by 18% and 45.2%, respectively, with significant computational load reduction. This study will provide the decision makers a computation-efficient demand response control of nearly zero energy buildings which enables full collaborations and thus helps improve the performances.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:174:y:2019:i:c:p:911-921
    DOI: 10.1016/j.energy.2019.02.192
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    References listed on IDEAS

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    4. 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).
    5. Wang, Huilong & Wang, Shengwei, 2021. "A disturbance compensation enhanced control strategy of HVAC systems for improved building indoor environment control when providing power grid frequency regulation," Renewable Energy, Elsevier, vol. 169(C), pages 1330-1342.
    6. Chen, Qi & Kuang, Zhonghong & Liu, Xiaohua & Zhang, Tao, 2022. "Energy storage to solve the diurnal, weekly, and seasonal mismatch and achieve zero-carbon electricity consumption in buildings," Applied Energy, Elsevier, vol. 312(C).
    7. Tushar, Wayes & Lan, Lan & Withanage, Chathura & Sng, Hui En Karen & Yuen, Chau & Wood, Kristin L. & Saha, Tapan Kumar, 2020. "Exploiting design thinking to improve energy efficiency of buildings," Energy, Elsevier, vol. 197(C).
    8. Wang, Huilong & Wang, Shengwei & Shan, Kui, 2020. "Experimental study on the dynamics, quality and impacts of using variable-speed pumps in buildings for frequency regulation of smart power grids," Energy, Elsevier, vol. 199(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. Zhang, Guiqing & Tian, Chenlu & Li, Chengdong & Zhang, Jun Jason & Zuo, Wangda, 2020. "Accurate forecasting of building energy consumption via a novel ensembled deep learning method considering the cyclic feature," Energy, Elsevier, vol. 201(C).
    11. 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).

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