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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. Yin, Yue & Liu, Tianqi & He, Chuan, 2019. "Day-ahead stochastic coordinated scheduling for thermal-hydro-wind-photovoltaic systems," Energy, Elsevier, vol. 187(C).
    3. 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.
    4. 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.
    5. 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.
    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. 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).
    9. 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).
    10. 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.
    11. 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.
    12. 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.
    13. Reddy, S. Surender & Bijwe, P.R., 2015. "Real time economic dispatch considering renewable energy resources," Renewable Energy, Elsevier, vol. 83(C), pages 1215-1226.
    14. Ottesen, Stig Odegaard & Tomasgard, Asgeir, 2015. "A stochastic model for scheduling energy flexibility in buildings," Energy, Elsevier, vol. 88(C), pages 364-376.
    15. 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.
    16. 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.
    17. 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).
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