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可逆固体酸化物セル向け経済的NMPC

Sakshi S Naik1, Yufei Zhao2, Douglas Allan3,4

  • 1Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States.

Industrial & engineering chemistry research
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PubMed
まとめ
この要約は機械生成です。

本研究では、可逆固体酸化物燃料電池（rSOC）の経済的非線形モデル予測制御（E-NMPC）を導入する。E-NMPCはrSOCの運用を最適化し、燃料電池モードでの水素使用量を削減し、電気分解モードでの生産を向上させる。

キーワード:
可逆固体酸化物燃料電池非線形モデル予測制御エネルギー貯蔵経済的最適化電力系統統合

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科学分野:

  • エネルギー貯蔵
  • 電気化学工学
  • 制御システム

背景:

  • 可逆固体酸化物燃料電池（rSOC）は、燃料電池モードと電気分解モードを切り替えることができ、電力系統の柔軟性を提供します。
  • 複雑で連成したダイナミクスにより、rSOC運用の最適化は困難です。
  • リアルタイムの電力価格は、経済的実行可能性のための高度な制御戦略を必要とします。

研究 の 目的:

  • rSOC運用の最適化のための経済的非線形モデル予測制御（E-NMPC）フレームワークの開発と評価。
  • 従来の制御方法と比較したE-NMPCの経済的利点の評価。
  • 運用柔軟性を高めるためのバッテリー貯蔵とrSOCシステムの統合の調査。

主な方法:

  • プラント設備を含む詳細なrSOCフローシートモデルの開発。
  • 運用最適化のためのrSOCモデルへのE-NMPC戦略の適用。
  • 燃料電池モードと電気分解モードの両方でのE-NMPC性能のシミュレーションと分析。

主要な成果:

  • E-NMPCは、発電量を維持しながら燃料電池モードでの水素消費量を削減しました。
  • E-NMPCは、電気分解モードでの水素生産においてわずかな改善を示しました。
  • バッテリーシステムとの統合により、発電および消費目標の柔軟性が向上しました。

結論:

  • E-NMPCは、rSOCの経済的運用を最適化するための効果的な戦略を提供します。
  • 提案されたフレームワークは、電力系統に接続されたrSOCアプリケーションにおける効率と経済的パフォーマンスの向上に貢献する可能性を示しています。
  • バッテリー統合は、rSOCシステムの動的な電力系統需要への適応性をさらに高めます。