Managing Renewable Energy Uncertainty inGreen Hydrogen Production Systems
Matteo Lea Casagrande, Andrea Isella, Davide Manca
🤖 gxceed AI 要約
日本語
本論文は、再生可能エネルギーの不確実性下でのグリーン水素生産システム向けに、実時間動的最適化アプローチを提案する。不完全な気象予測を利用し、水素目標生産量を動的に調整することで、年間目標を達成しつつ電力輸入を最小化し、機器負荷を制限する。CAISOデータに基づく1年間のシミュレーションにより、水素製造コスト3.31USD/kgH2、CO2排出90%超削減（1kgCO2/kgH2未満）を実証した。既存設備への小規模改修で導入可能。
English
This paper proposes a real-time dynamic optimization approach for green hydrogen production systems under renewable energy uncertainty. Using imperfect weather forecasts and adaptive hydrogen production reference, the method minimizes grid electricity imports and equipment stress while meeting annual production targets. A year-long simulation with CAISO data achieves a levelized cost of 3.31 USD/kgH2 and over 90% CO2 reduction (<1 kgCO2/kgH2) compared to steam methane reforming. The strategy can be implemented with limited modifications to existing facilities.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本はグリーン水素の大量導入を目指しており、本手法は変動性再エネ下での水素製造コスト低減と安定供給に直接貢献する。既存の化学プラントへの適用可能性が示され、SSBJ関連のScope 1排出削減にもつながる。
In the global GX context
As global green hydrogen deployment accelerates, this optimization framework addresses a key operational challenge: managing renewable uncertainty while meeting production targets. The demonstrated LCOH of 3.31 USD/kgH2 and 90%+ emission reduction provide a benchmark for cost-competitive green hydrogen, relevant to TCFD/ISSB-aligned transition planning and renewable hydrogen certification schemes like CertifHy.
👥 読者別の含意
🔬研究者:The receding-horizon optimization with imperfect forecast adaptation offers a practical methodology for green hydrogen system control under uncertainty, supported by year-long CAISO data.
🏢実務担当者:Industrial operators can adopt this strategy with limited modifications to existing electrolysis plants to reduce grid dependence, lower LCOH, and meet emission targets.
🏛政策担当者:The results validate that green hydrogen can achieve cost and emission parity with fossil-based alternatives, supporting policies for hydrogen subsidies, carbon pricing, and renewable portfolio standards.
📄 Abstract（原文）
The extensive use of renewable energy to supply hydrogen production for chemical processes is hindered by the uncertainty in power generation and by strict operational limits.These challenges are addressed through a real-time dynamic optimization approach based on a receding-horizon strategy that provides optimal decision variables. The framework explicitly relies on imperfect weather forecasts and dynamically adapts the hydrogen reference production to guarantee the final productivity target. The optimization methodology focuses on minimizing grid electricity imports, limiting excessive equipment stress, and preventing constraint violations, while ensuring that the hydrogen production target is satisfied.The proposed approach yields competitive economic and environmental performance, with a levelized cost of hydrogen of 3.31 USD/kgH2, well within literature values (1.50–7.50 USD/kgH2) and below typical industrial costs (4–12 USD/kgH2). At the same time, carbon dioxide emissions are reduced by more than 90% compared to steam methane reforming, resulting in specific emissions below 1 kgCO2/kgH2. These results are supported by a year-long simulation based on CAISO renewable energy data. The optimized system achieves the annual hydrogen production target while respecting all operational constraints, whereas the absence of operational strategy and buffering systems leads to systematic constraint violations. The strategy can be implemented in existing industrial facilities with limited modifications.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.69997/sct.174564first seen 2026-06-23 05:58:17
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