In this paper, a batch-based industrial load model is used to model the energy system of a hydrogen-based steel plant. It is formulated in Gorubi as a profit-maximizing Mixed Integer Linear Programming (MILP) problem. The addition of H2 units to the steel plant introduces new operational aspects in steel production. This requires energy efficiency constraints to optimizematerial usage, consideringwarm-up time for specific units, and exploring the impact of a fuel cell system with the plant. The existing industrial load model is modified and new constraints are added to obtain flexible behavior, where units have the choice to consume electricity as part of their normal operation or sell the electricity back to the market. 40 scenarios are generated to optimally manage the energy consumption of the plant. Sensitivity analysis reveals that a fuel cell has a low impact on profit in low price periods, hydrogen storage is essential to overcome losses for the assumed average price , and flexible operation achieves the highest profit when encountered with a peak price. After obtaining the energy consumption of the units, they are modeled as loads in Pandapower assuming the worst-case scenario. Then, a time-series load flow analysis is carried out to validate the rating of the main transformer for the radial network. Finally, the peak active and reactive power of the plant is modeled as a static load in a representative European high voltage grid, where load flow reveals that bus voltages and line loadings depend on the location of the plant in the system.