Optimal risk-constrained stochastic scheduling of microgrids with hydrogen vehicles in real-time and day-ahead markets

Abstract

By growing interest in hydrogen vehicles (HVs), hydrogen fueling stations (HFSs) that convert electric power to hydrogen to supply HVs have emerged as a new asset for power grids. To safely and consistently supply HFSs with power, the use of microgrids (MGs), including various flexible generation units, is considered to be a reliable choice. This paper proposes a competence MG scheduling model. In this model, an optimal coordination of HFSs with demand response (DR), energy storage systems (ESS), and appropriate multi-market mechanisms is addressed. Also, a reformulated version of a risk-constrained stochastic scheduling (RSS) model is used to minimize the MG operation cost. The uncertainties associated with the real-time market price, renewables, electrical loads, and HVs are handled by considering conservativeness parameters. In this model, linearized AC optimal power flow (ACOPF) equations are included in the mixed-integer linear programming (MILP) problem to satisfy the security of MG operation. The proposed model is examined on a 21-bus MG while considering various case studies. The results show that the operation of HFSs provides a profit of 254.5$/day by selling hydrogen for MG operator. In addition, it is found that developing the HFS technology can reduce the total daily operation costs of MG by up to 9.6%. It is also shown that participation of MG in both day-ahead and real-time markets leads to a reduction of 11.7% in the operating costs. Moreover, we show that employing DR programs leads to operation cost reduction and load flattening during high-demand hours. The security constraints keep the voltage between 0.97 p.u. and 1 p.u and the loss of lines within a reasonable range. Finally, a comprehensive comparison between our perceptive RSS with traditional stochastic scheduling and conservative RSS is carried out to show the effectiveness of the proposed method.