IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v253y2022ics0360544222011070.html
   My bibliography  Save this article

Two-time-scale coordinated optimal control of building energy systems for demand response considering forecast uncertainties

Author

Listed:
  • Li, Hangxin
  • Wang, Shengwei

Abstract

Predictive scheduling is essentially needed to optimize energy dispatch of building energy systems for demand response, as it can maximize the benefits considering future conditions. However, the optimized demand response probably cannot be achieved and energy demands probably cannot be fully satisfied in operation due to the large discrepancy between the predicted and actual conditions. Generic and comprehensive optimal control methods of building energy systems for demand response, which can achieve maximized benefits while ensuring satisfaction of actual energy demands, are still absent. In this study, a two-time-scale coordinated optimal control strategy is proposed to optimize energy dispatch between building energy systems for demand response considering forecast uncertainties. The control strategy includes two coordinated schemes: a stochastic scheduling scheme and a real-time optimal control scheme. Forecast uncertainties are quantified based on real meteorological data. The optimal start time of scheduling optimization horizon is investigated. The control strategy was tested on a platform with PV and battery, established based on the Zero Carbon Building in Hong Kong. Results show that the optimal scheduling optimization horizon start time is the initial of the off-peak period. The proposed strategy reduces up to 14.7% cost compared with existing strategies while satisfying actual energy demands.

Suggested Citation

  • Li, Hangxin & Wang, Shengwei, 2022. "Two-time-scale coordinated optimal control of building energy systems for demand response considering forecast uncertainties," Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:energy:v:253:y:2022:i:c:s0360544222011070
    DOI: 10.1016/j.energy.2022.124204
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222011070
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124204?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bornapour, Mosayeb & Hooshmand, Rahmat-Allah & Parastegari, Moein, 2019. "An efficient scenario-based stochastic programming method for optimal scheduling of CHP-PEMFC, WT, PV and hydrogen storage units in micro grids," Renewable Energy, Elsevier, vol. 130(C), pages 1049-1066.
    2. K. Gnana Sheela & S. N. Deepa, 2013. "Review on Methods to Fix Number of Hidden Neurons in Neural Networks," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-11, June.
    3. Gang, Wenjie & Wang, Shengwei & Xiao, Fu & Gao, Dian-ce, 2015. "Robust optimal design of building cooling systems considering cooling load uncertainty and equipment reliability," Applied Energy, Elsevier, vol. 159(C), pages 265-275.
    4. Reddy, S. Surender & Bijwe, P.R., 2015. "Real time economic dispatch considering renewable energy resources," Renewable Energy, Elsevier, vol. 83(C), pages 1215-1226.
    5. Lu, Yuehong & Wang, Shengwei & Sun, Yongjun & Yan, Chengchu, 2015. "Optimal scheduling of buildings with energy generation and thermal energy storage under dynamic electricity pricing using mixed-integer nonlinear programming," Applied Energy, Elsevier, vol. 147(C), pages 49-58.
    6. 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.
    7. Li, Xiaozhu & Wang, Weiqing & Wang, Haiyun, 2021. "Hybrid time-scale energy optimal scheduling strategy for integrated energy system with bilateral interaction with supply and demand," Applied Energy, Elsevier, vol. 285(C).
    8. Ottesen, Stig Odegaard & Tomasgard, Asgeir, 2015. "A stochastic model for scheduling energy flexibility in buildings," Energy, Elsevier, vol. 88(C), pages 364-376.
    9. Nikzad, Mehdi & Samimi, Abouzar, 2021. "Integration of designing price-based demand response models into a stochastic bi-level scheduling of multiple energy carrier microgrids considering energy storage systems," Applied Energy, Elsevier, vol. 282(PA).
    10. Turk, Ana & Wu, Qiuwei & Zhang, Menglin & Østergaard, Jacob, 2020. "Day-ahead stochastic scheduling of integrated multi-energy system for flexibility synergy and uncertainty balancing," Energy, Elsevier, vol. 196(C).
    11. Li, Shuangqi & Gu, Chenghong & Zeng, Xianwu & Zhao, Pengfei & Pei, Xiaoze & Cheng, Shuang, 2021. "Vehicle-to-grid management for multi-time scale grid power balancing," Energy, Elsevier, vol. 234(C).
    12. 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.
    13. Li, Weilin & Xu, Peng & Lu, Xing & Wang, Huilong & Pang, Zhihong, 2016. "Electricity demand response in China: Status, feasible market schemes and pilots," Energy, Elsevier, vol. 114(C), pages 981-994.
    14. Yin, Yue & Liu, Tianqi & He, Chuan, 2019. "Day-ahead stochastic coordinated scheduling for thermal-hydro-wind-photovoltaic systems," Energy, Elsevier, vol. 187(C).
    15. Nottrott, A. & Kleissl, J. & Washom, B., 2013. "Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems," Renewable Energy, Elsevier, vol. 55(C), pages 230-240.
    16. 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.
    17. Daud, Abdel-Karim & Ismail, Mahmoud S., 2012. "Design of isolated hybrid systems minimizing costs and pollutant emissions," Renewable Energy, Elsevier, vol. 44(C), pages 215-224.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Li, Jiale & Yang, Bo & Huang, Jianxiang & Guo, Zhengxun & Wang, Jingbo & Zhang, Rui & Hu, Yuanweiji & Shu, Hongchun & Chen, Yixuan & Yan, Yunfeng, 2023. "Optimal planning of Electricity–Hydrogen hybrid energy storage system considering demand response in active distribution network," Energy, Elsevier, vol. 273(C).

    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. Li, Hangxin & Wang, Shengwei, 2022. "Comparative assessment of alternative MPC strategies using real meteorological data and their enhancement for optimal utilization of flexibility-resources in buildings," Energy, Elsevier, vol. 244(PA).
    2. 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).
    3. 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.
    4. Li, Hangxin & Wang, Shengwei, 2019. "Coordinated optimal design of zero/low energy buildings and their energy systems based on multi-stage design optimization," Energy, Elsevier, vol. 189(C).
    5. Ding, Yihong & Tan, Qinliang & Shan, Zijing & Han, Jian & Zhang, Yimei, 2023. "A two-stage dispatching optimization strategy for hybrid renewable energy system with low-carbon and sustainability in ancillary service market," Renewable Energy, Elsevier, vol. 207(C), pages 647-659.
    6. Luo, Jianing & Li, Hangxin & Wang, Shengwei, 2022. "A quantitative reliability assessment and risk quantification method for microgrids considering supply and demand uncertainties," Applied Energy, Elsevier, vol. 328(C).
    7. 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).
    8. 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.
    9. Li, Hangxin & Wang, Shengwei, 2020. "Coordinated robust optimal design of building envelope and energy systems for zero/low energy buildings considering uncertainties," Applied Energy, Elsevier, vol. 265(C).
    10. Morteza Zare Oskouei & Ayşe Aybike Şeker & Süleyman Tunçel & Emin Demirbaş & Tuba Gözel & Mehmet Hakan Hocaoğlu & Mehdi Abapour & Behnam Mohammadi-Ivatloo, 2022. "A Critical Review on the Impacts of Energy Storage Systems and Demand-Side Management Strategies in the Economic Operation of Renewable-Based Distribution Network," Sustainability, MDPI, vol. 14(4), pages 1-34, February.
    11. 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).
    12. Zhuang, Chaoqun & Wang, Shengwei & Shan, Kui, 2019. "Probabilistic optimal design of cleanroom air-conditioning systems facilitating optimal ventilation control under uncertainties," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    13. Lu, Qing & Guo, Qisheng & Zeng, Wei, 2022. "Optimization scheduling of integrated energy service system in community: A bi-layer optimization model considering multi-energy demand response and user satisfaction," Energy, Elsevier, vol. 252(C).
    14. Liu, Fang & Mo, Qiu & Yang, Yongwen & Li, Pai & Wang, Shuai & Xu, Yanping, 2022. "A nonlinear model-based dynamic optimal scheduling of a grid-connected integrated energy system," Energy, Elsevier, vol. 243(C).
    15. 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.
    16. Gong, Yu & Liu, Pan & Ming, Bo & Feng, Maoyuan & Huang, Kangdi & Wang, Yibo, 2022. "Identifying the functional form of operating rules for hydro–photovoltaic hybrid power systems," Energy, Elsevier, vol. 243(C).
    17. 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).
    18. 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.
    19. Fan, Wei & Tan, Zhongfu & Li, Fanqi & Zhang, Amin & Ju, Liwei & Wang, Yuwei & De, Gejirifu, 2023. "A two-stage optimal scheduling model of integrated energy system based on CVaR theory implementing integrated demand response," Energy, Elsevier, vol. 263(PC).
    20. Adrian Grimm & Patrik Schönfeldt & Herena Torio & Peter Klement & Benedikt Hanke & Karsten von Maydell & Carsten Agert, 2021. "Deduction of Optimal Control Strategies for a Sector-Coupled District Energy System," Energies, MDPI, vol. 14(21), pages 1-13, November.

    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:eee:energy:v:253:y:2022:i:c:s0360544222011070. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.