Hydrogen energy storage train scheduling with renewable generation and demand response

Abstract

Large-scale penetration of renewable and hydrogen energy sources represents promising trends toward carbon emission reductions in the power sector. The storage systems such as the hydrogen energy storage (HES) Train will be crucial in responding to extreme grid events due to their agility and flexibility. This manuscript proposes a stochastic network constrained unit commitment (NCUC) considering HES Train with solar PV generation and demand response program (DRP). The DRP is introduced as a flexible option for dealing with energy market prices, providing sustainable options, and modifying the load profile for peak load shaving. The proposed model is applied to manage an HES Train that provides hydrogen energy services with low electricity prices. Moreover, the vector autoregressive moving average (VARMA) model is used in the stochastic optimization strategy to handle uncertain solar PV power. The model is formulated as a mixed integer linear programming (MILP) problem along with a generalized bender decomposition technique (GBD) to obtain the global optimal solution. A sensitivity analysis is presented to analyze solar power's ability to handle the high demand of the system operation. The proposed NCUC problem is simulated using GAMS software on an IEEE 24-bus system. HES Train scheduling with DRP and uncertain solar PV reduces the overall cost by 8.71 % as compared to the thermal-HES Train system.