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Robust Optimization of Power Consumption for Public Buildings Considering Forecasting Uncertainty of Environmental Factors

Author

Listed:
  • Jingshu Xiao

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Jun Xie

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Xingying Chen

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Kun Yu

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Zhenyu Chen

    (State Grid Jiangsu Electric Power Company, Nanjing 210096, China)

  • Kaining Luan

    (State Grid Jiangsu Electric Power Company, Nanjing 210096, China)

Abstract

In recent years, with the advancement of urban construction in China, the optimization of power consumption in public buildings has been focused on. The optimization of power consumption in public buildings is based on the prediction of natural illuminance, outdoor air temperature and flow of people in public building. Therefore, it is worthwhile to study how to formulate a power consumption strategy with consideration of forecasting uncertainty of environmental factors. The robust-index method is proposed to deal with the problem of forecasting uncertainty. Firstly, this paper establishes power consumption models for lighting systems, air-conditioning systems, and elevator systems in public buildings. Secondly, the robust indexes for each system and the synthetic robust index are established. Thirdly, the objective function is formulated to reduce the total electricity cost with the robust indexes applied as additional constraints to the optimization problem, therefore the obtained power consumption schedules are able to reach the expected robust level. Finally, simulation results show attributes of the proposed method.

Suggested Citation

  • Jingshu Xiao & Jun Xie & Xingying Chen & Kun Yu & Zhenyu Chen & Kaining Luan, 2018. "Robust Optimization of Power Consumption for Public Buildings Considering Forecasting Uncertainty of Environmental Factors," Energies, MDPI, vol. 11(11), pages 1-13, November.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:3075-:d:181331
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    References listed on IDEAS

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    1. Hernández, J.C. & Ruiz-Rodriguez, F.J. & Jurado, F., 2017. "Modelling and assessment of the combined technical impact of electric vehicles and photovoltaic generation in radial distribution systems," Energy, Elsevier, vol. 141(C), pages 316-332.
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    Cited by:

    1. Renato Ferrero & Mario Collotta & Maria Victoria Bueno-Delgado & Hsing-Chung Chen, 2020. "Smart Management Energy Systems in Industry 4.0," Energies, MDPI, vol. 13(2), pages 1-3, January.
    2. Lemiao Qiu & Huifang Zhou & Zili Wang & Wenqian Lou & Shuyou Zhang & Lichun Zhang, 2020. "A Stepped-Segmentation Method for the High-Speed Theoretical Elevator Car Air Pressure Curve Adjustment," Energies, MDPI, vol. 13(10), pages 1-21, May.

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