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Robust scheduling of building energy system under uncertainty

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  • Wang, Chengshan
  • Jiao, Bingqi
  • Guo, Li
  • Tian, Zhe
  • Niu, Jide
  • Li, Siwei

Abstract

This paper proposes a robust scheduling strategy to manage a building energy system with solar power generation system, multi-chiller system and ice thermal energy storage under prediction uncertainty. The strategy employs a two-stage adjustable robust formulation to minimize the system operation cost, wherein a parameter is introduced to adjust the level of conservatism of the robust solution against the modeled uncertainty. Then a column and constraint generation algorithm with modified initialization strategy is adopted to solve this optimization model along with mixed-integer linear programming. Further, we evaluate the performance of the proposed strategy by hourly simulating the system operation of a practical project with Monte Carlo simulation. Numerical results show that the robust scheduling with a proper parameter can be superior to the deterministic strategy in all the studied cases. Additionally, the proposed strategy has similar results with the model-based predictive control strategy while the former only needs to be implemented once. Even in the highest load case, the relative deviation between the two strategies is less than 2%.

Suggested Citation

  • Wang, Chengshan & Jiao, Bingqi & Guo, Li & Tian, Zhe & Niu, Jide & Li, Siwei, 2016. "Robust scheduling of building energy system under uncertainty," Applied Energy, Elsevier, vol. 167(C), pages 366-376.
    • Handle: RePEc:eee:appene:v:167:y:2016:i:c:p:366-376
    DOI: 10.1016/j.apenergy.2015.09.070
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    1. Zachariadis, Theodoros & Hadjinicolaou, Panos, 2014. "The effect of climate change on electricity needs – A case study from Mediterranean Europe," Energy, Elsevier, vol. 76(C), pages 899-910.
    2. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    3. Candanedo, J.A. & Dehkordi, V.R. & Stylianou, M., 2013. "Model-based predictive control of an ice storage device in a building cooling system," Applied Energy, Elsevier, vol. 111(C), pages 1032-1045.
    4. 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.
    5. Mendes, Gonçalo & Ioakimidis, Christos & Ferrão, Paulo, 2011. "On the planning and analysis of Integrated Community Energy Systems: A review and survey of available tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(9), pages 4836-4854.
    6. Dimitris Bertsimas & Melvyn Sim, 2004. "The Price of Robustness," Operations Research, INFORMS, vol. 52(1), pages 35-53, February.
    7. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    8. Parisio, Alessandra & Rikos, Evangelos & Tzamalis, George & Glielmo, Luigi, 2014. "Use of model predictive control for experimental microgrid optimization," Applied Energy, Elsevier, vol. 115(C), pages 37-46.
    9. Ma, Zhenjun & Wang, Shengwei, 2011. "Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm," Applied Energy, Elsevier, vol. 88(1), pages 198-211, January.
    10. Powell, Kody M. & Sriprasad, Akshay & Cole, Wesley J. & Edgar, Thomas F., 2014. "Heating, cooling, and electrical load forecasting for a large-scale district energy system," Energy, Elsevier, vol. 74(C), pages 877-885.
    11. Vaghefi, A. & Jafari, M.A. & Bisse, Emmanuel & Lu, Y. & Brouwer, J., 2014. "Modeling and forecasting of cooling and electricity load demand," Applied Energy, Elsevier, vol. 136(C), pages 186-196.
    12. Murphy, M.D. & O’Mahony, M.J. & Upton, J., 2015. "Comparison of control systems for the optimisation of ice storage in a dynamic real time electricity pricing environment," Applied Energy, Elsevier, vol. 149(C), pages 392-403.
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