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人工知能に基づくダイナミックな価格設定による再生可能水素インフラの技術的経済的な最適化
Paul C Okonkwo1, Samuel Chukwujindu Nwokolo2, Edson L Meyer3
1Mechanical & Mechatronics Engineering Department, College of Engineering, Dhofar University, Salalah, Oman.
Scientific reports
|August 27, 2025
まとめ
オーストラリア
科学分野:
- 再生可能エネルギーシステム
- テクノ経済分析
- 水素の生産
背景:
- オーストラリアは緑の水素の潜在能力を有している.
- 脱炭素化水素の生産は エネルギー移行に不可欠です
- ハイブリッド再生可能エネルギーシステムは 実現可能な解決策です
研究 の 目的:
- オーストラリアの6つの都市でハイブリッドの太陽風発電システムを用いて,水素生産の技術的経済的な最適化を実施する.
- 費用対効果の高いグリーン水素発電のための最適なシステム構成と場所を特定する.
- システムの性能に対する高度なアルゴリズムと需要側管理の影響を評価する.
主な方法:
- ハイブリッド光伏-風力-電解機-水素タンクシステムの技術経済モデル化
- メタヒューリスティック・アルゴリズムによる最適化 (メイフライ,グレイ・ウルフ,クジラ最適化).
- ブロックチェインと強化学習戦略を組み込んだ感受性分析
主要な成果:
- オーストラリアのパースは,最も低いレベル化水素コスト (LCH) を0.582ドル/kgで示した.
- 試験されたすべての場所では,水素の生産に100%の再生可能分子が使用されました.
- メタヒューリスティックアルゴリズムは,標準モデルと比較して,技術経済指標を3~8%改善しました.
- ブロックチェーンとAIによる需要対応は 8~10%のコスト削減をもたらしました
結論:
- パースはオーストラリアのグリーン水素生産の最適なハブとして認識されています.
- 地域の資源利用と都市化が水素生産コストに大きく影響している.
- 輸出市場では先進的な材料とオフショアプラットフォームへのさらなる投資が推奨されています.
- AIで強化された地域特有のシステムは オーストラリアの水素経済目標の達成の鍵です
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