Optimization of hydrogen supply operations for decarbonizing energy-intensive industries: A multi-timescale rolling horizon approach

The adoption of green hydrogen, produced via water electrolysis using renewable energy sources, is a promising
decarbonization strategy for energy-intensive industries. However, the feasibility of this transition depends on
the economic viability of hydrogen supply and the ability to ensure a stable hydrogen supply under fluctuating
and uncertain renewable energy availability. This study develops a Mixed-Integer Nonlinear Programming
(MINLP) model to optimize hydrogen supply operations. On-site hydrogen production is supported by grid
electricity purchases and complemented by external green hydrogen truck deliveries to ensure the continuous
fulfillment of hydrogen demand in industrial furnaces. The model captures key electrolyzer operational dynamics,
including variable loads, state transitions, and stack efficiency degradation. The formulation is
embedded in a multi-timescale framework that accounts for different decision frequencies and implementation
lead times of electrolyzer operations and external hydrogen delivery. The problem is solved using a rolling
horizon approach to reduce reliance on long-term forecasts and enable reactive scheduling of hydrogen supply
operations under renewable energy uncertainty. Results indicate that seasonal variations in renewable energy
availability and grid electricity prices can cause operating cost differences of up to 60%. In contrast, renewable
energy forecast inaccuracies result in cost variations limited to 2.47% under the rolling horizon approach, which
achieves operating cost reductions of up to 4.15% compared to static optimization, demonstrating the robustness
and relevance of the proposed framework. The use of real historical forecast datasets or the integration of
forecasting algorithms represents an interesting direction for future research.