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

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:The rolling horizon optimization framework can be extended with real forecast data or integrated with forecasting algorithms for further validation.

🏢実務担当者:Companies in energy-intensive industries can apply this model to plan hydrogen supply operations and evaluate trade-offs between on-site production and external delivery under uncertainty.

🏛政策担当者:The study highlights the importance of supporting green hydrogen infrastructure and flexible electricity pricing to mitigate supply cost fluctuations.

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.

• Zenodo https://zenodo.org/records/20159745first seen 2026-05-14 21:30:50 · last seen 2026-05-14 21:39:03