Economic Value of Hydrogen Demand Forecasting for Rule-Based Operation of Green Hydrogen Hubs
グリーン水素ハブのルールベース運転における水素需要予測の経済的価値 (AI 翻訳)
Pedro Félix, F. Soares
🤖 gxceed AI 要約
日本語
再生可能エネルギー水素ハブの運用において、日先の水素需要予測をルールベース制御に組み込むことで、年間利益の増加、再生可能エネルギー出力抑制の解消、電解槽稼働率の向上が達成されることを示した。XGBoostと季節性ナイーブベンチマークの差は限定的であり、需要予測情報自体の導入が最大の効果をもたらす。
English
This study evaluates the economic value of day-ahead hydrogen demand forecasting for rule-based operation of green hydrogen hubs. Using a case study with PV, battery, electrolyzer, and hydrogen storage, it shows that integrating forecasts (seasonal naive or XGBoost) into a rule-based controller significantly improves annual profit, eliminates renewable curtailment, and raises electrolyzer utilization close to an optimization-based benchmark. The results indicate that the largest gain comes from equipping heuristic controllers with reliable demand information, rather than the specific forecasting model.
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
日本では水素基本戦略に基づきグリーン水素ハブの実証が進んでいるが、運用最適化の知見は限られる。本論文は、簡便なルールベース制御でも需要予測を導入することで経済性が大幅に改善できることを示しており、日本の水素サプライチェーン実装における実用的な制御戦略の設計に直接的な示唆を与える。
In the global GX context
Globally, green hydrogen hubs are a key component of decarbonization strategies, but their operational optimization remains a challenge. This paper demonstrates that even simple rule-based controllers can achieve near-optimal performance when equipped with day-ahead demand forecasts, offering a practical pathway for hub operators to improve economic viability without complex optimization solvers. The findings are relevant for the scale-up of hydrogen infrastructure worldwide.
👥 読者別の含意
🔬研究者:Provides evidence that demand forecasting, rather than sophisticated optimization, can unlock most of the economic value in hydrogen hub operation.
🏢実務担当者:Offers a practical approach to improve hydrogen hub profitability using accessible forecasting and rule-based control.
🏛政策担当者:Highlights the importance of supporting demand forecasting infrastructure for hydrogen markets to enhance project bankability.
📄 Abstract（原文）
The operation of renewable hydrogen hubs is often based on simple rule-based strategies that prioritise immediate renewable utilisation and corrective use of storage. While easy to implement, such approaches may underuse system flexibility and reduce the economic value extracted from available assets. This paper assesses the operational value of day-ahead hydrogen demand forecasting in the supervisory control of a renewable hydrogen hub composed of photovoltaic generation, a battery energy storage system, a proton exchange membrane electrolyser, and a hydrogen storage tank. Two forecasting approaches are considered, namely a seasonal naïve benchmark and an XGBoost model, and their predictions are embedded into a rule-based dayahead controller. The resulting operating strategies are compared against a rule-based controller without forecast and against an optimisation-based benchmark with perfect foresight. The assessment is carried out using annual economic and operational indicators, complemented by daily operating profiles on a representative high-PV day. Results show that introducing dayahead demand forecasts into the rule-based controller substantially improves hub operation, increasing annual profit, eliminating renewable curtailment, and raising electrolyser utilisation to values close to the optimisation benchmark. For the considered case study, the additional operational benefit of XGBoost over the seasonal naïve approach is limited, as both forecast-based strategies lead to very similar annual performance and dispatch patterns. Overall, the results indicate that the largest gain arises from equipping a practical heuristic controller with reliable day-ahead demand information, allowing it to recover most of the value associated with optimisation-based scheduling.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/eem68581.2026.11589850first seen 2026-07-13 06:57:45
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