A robust demand response control of commercial buildings for smart grid under load prediction uncertainty

Various demand response control strategies have been developed for grid power balance and user cost saving. Few studies have systematically considered the impacts of load prediction uncertainty which can cause the strategies fail to achieve their objectives. This study, therefore, develops a robust demand response control of commercial buildings for smart grid under load prediction uncertainty. Based on the initial control signals from the conventional genetic algorithm method, the optimal control signals with improved robustness are obtained using the Monte Carlo method. Under dynamic pricing of smart grid, the study results show the impacts of load prediction uncertainty reduce the daily electricity cost saving from 8.5% to 4.1%. Such a significant cost saving reduction implies the necessity of taking account of the load prediction uncertainty in the development of a demand response control. Moreover, under the load prediction uncertainty, the proposed demand response control can still achieve 7.3% daily electricity cost saving, which demonstrates its robustness and effectiveness. The improved robustness of the proposed control has also been demonstrated by the statistics analysis results from the Monte Carlo studies. The proposed robust control is useful for commercial buildings to achieve significant cost savings in practice particularly as uncertainty exists.