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Modelling and metrics for optimal sizing of renewable power plants supplying green hydrogen generation systems

再生可能エネルギー発電所から供給されるグリーン水素生成システムの最適規模設定のためのモデリングと指標 (AI 翻訳)

Mobin Naderi, David A. Stone, E. E. Ballantyne

Scientific Reports📚 査読済 / ジャーナル2026-01-26#水素
DOI: 10.1038/s41598-026-36987-0
原典: https://doi.org/10.1038/s41598-026-36987-0

🤖 gxceed AI 要約

日本語

本論文は、再生可能エネルギー駆動の水素生成・貯蔵施設の長期分析と設計のためのモジュール型モデリング手法を提案する。太陽光・風力、バッテリー、バックアップ電源、水素システムモジュールを統合し、汎用的なフレームワークを提供する。また、多目的最適化のための包括的な性能指標セットを提案し、英国の1MW電解槽を対象としたケーススタディで検証した。

English

This paper presents a modular modelling approach for long-term analysis and design of renewable-powered hydrogen generation and storage facilities, integrating solar, wind, battery, backup power, and hydrogen system modules. It proposes a comprehensive set of performance metrics for multi-objective optimization and validates the model through a UK case study with a 1 MW electrolyser.

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

日本は水素をGX戦略の柱と位置づけており、本論文の最適規模設定手法は国内の水素サプライチェーン設計に応用可能。特に、再生可能エネルギーと水素の統合設計は、日本の再エネ導入拡大と水素社会実現に貢献する。

In the global GX context

As global interest in green hydrogen grows, this paper provides a flexible modelling framework and metrics for optimizing renewable-hydrogen systems. The modular design allows adaptation to different regional contexts, supporting the scale-up of hydrogen infrastructure for the energy transition.

👥 読者別の含意

🔬研究者:Offers a modular, case-study-free modelling framework and a comprehensive set of performance metrics that can be extended for further research on integrated renewable-hydrogen systems.

🏢実務担当者:Provides plant designers with a tool to evaluate different system configurations and make informed decisions based on weighted performance criteria for green hydrogen projects.

🏛政策担当者:Supports policymakers in assessing the technical and economic feasibility of green hydrogen systems, aiding in the design of incentives and infrastructure planning.

📄 Abstract(原文)

This paper presents a modular modelling approach for long-term analysis and design of renewable-powered hydrogen generation and storage facilities, encompassing both power generation and hydrogen system components. The proposed model can be used to integrate different sizes of solar and wind energy resources, different battery energy storage systems, a backup power source (if required), and main hydrogen system modules in power demand calculations. As a part of the paper’s novelty, the proposed modelling approach is modular and case study-free, which allows for generalisation to a variety of case studies. The expandability of the modelling method is strengthened by presenting a unified modelling framework for all modules required in modelling the system. As the second main paper’s contribution, a comprehensive set of performance metrics is proposed to support a multi-objective optimisation framework for optimal sizing of system components. Although the metrics focus on different technical and economic aspects, environmental issues can be covered using some metrics, like the grid share of total energy requirements for the hydrogen system. Both proposed modelling and sizing methods enable renewable power plant designers to evaluate different configurations and make informed decisions based on weighted performance criteria. The proposed model and sizing problem are implemented in a combined Editor and Simulink environment in MATLAB for a case study as a real feasibility study in the UK to operate a renewable-supplied hydrogen system, including a 1 MW electrolyser. Simulation results for the representative case study validate the model’s behaviour and its reliability through various primary output profiles, e.g., power profiles, and secondary outputs, e.g., met hydrogen demand and levelised cost of hydrogen. The proposed modelling and optimisation methods can easily be expanded for case studies with more technical data or different load demands, e.g., combined hydrogen, heat, and power.

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