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Energies 2018, 11(11), 3075; https://doi.org/10.3390/en11113075

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

1
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
2
State Grid Jiangsu Electric Power Company, Nanjing 210096, China
*
Author to whom correspondence should be addressed.
Received: 11 October 2018 / Revised: 29 October 2018 / Accepted: 31 October 2018 / Published: 8 November 2018
(This article belongs to the Special Issue Smart Management Energy Systems in Industry 4.0)
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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. View Full-Text
Keywords: demand response; public buildings; robust-index method; load scheduling demand response; public buildings; robust-index method; load scheduling
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Xiao, J.; Xie, J.; Chen, X.; Yu, K.; Chen, Z.; Luan, K. Robust Optimization of Power Consumption for Public Buildings Considering Forecasting Uncertainty of Environmental Factors. Energies 2018, 11, 3075.

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