Adaptive robust scheduling optimization of a smart commercial building considering joint energy and reserve markets

With the increase in renewable energy penetration and the widespread use of electric vehicles, the uncertainty of controllable resources adversely affects the economic scheduling of smart buildings. Meanwhile, the smart building can take advantage of its flexibility through multiple markets mechanism. Therefore, this paper proposes an optimal scheduling model for the smart commercial building, including electric vehicle charging, which partakes in the day-ahead energy reserve markets. Firstly, the behavior of electric vehicles is obtained through Monte Carlo simulation. This paper categorizes electric vehicles according to their behavioral characteristics and proposes corresponding scheduling strategies. Then, the joint energy and reserve markets mechanism is introduced, and the robust adaptive optimization (ARO) method is employed to deal with uncertainties. The proposed model is a three-level and two-stage model, which can be solved by the column and constraints generation algorithm. Finally, the economy and feasibility of the proposed model are validated through a case study. Results show that the ARO method saves 3%-4% of expenses more than the pure robust optimization method, and the proposed joint markets mechanism can increase profits by 1%-4%.

Automated summary

This paper proposes an optimal scheduling model for smart commercial buildings, considering electric vehicle charging and multiple market mechanisms. Uncertainties are dealt with using Monte Carlo simulation and robust adaptive optimization method.