A Markov Chain-Based SDDiP Method for Integrated Logistics and Hydrogen-Electric Energy Scheduling for Seaports
マルコフ連鎖に基づくSDDiP法による港湾の統合物流・水素電力スケジューリング (AI 翻訳)
Wentao Lv, Yujian Ye, Tianxiang Cui, Huayan Zhang, Cun Zhang, Dezhi Xu, Di Huang, Zhiyuan Liu, Tang Li, Goran Strbac
🤖 gxceed AI 要約
日本語
本論文は、港湾の水素・電力エネルギーシステムと物流を統合した多段階確率計画モデルを提案。マルコフ連鎖ベースの確率的双対動的整数計画法により、船舶到着や再エネ出力の不確実性に対処し、寧波・舟山港の実データで運用コスト33%削減、ピーク需要30%削減、排出量51%削減を実証。水素インフラを柔軟性資源として活用する意義を示した。
English
This paper proposes a multistage stochastic programming framework integrating hydrogen-electric energy and logistics scheduling for seaports. Using a Markov chain-based SDDiP algorithm, it handles uncertainties in ship arrivals and renewable generation. Case studies at Ningbo-Zhoushan Port show 33% lower operating costs, 30% peak demand reduction, and 51% emission cuts, highlighting hydrogen as a flexibility resource.
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
日本の港湾（例：横浜港、神戸港）でも水素サプライチェーン構築が進む中、本研究成果は港湾のエネルギー自律化と脱炭素化に向けた具体的な数値目標を示す。SSBJ対応やカーボンニュートラルポート政策と整合し、港湾運営者や関連企業の投資判断に有用。
In the global GX context
As global ports pursue decarbonization, this study provides a quantitative framework for integrating hydrogen infrastructure with port operations, aligning with ISSB disclosure and transition finance trends. The demonstrated cost and emission reductions offer a replicable model for energy-autonomous ports worldwide.
👥 読者別の含意
🔬研究者:Provides a novel stochastic optimization method for integrated energy-logistics systems with real-world validation.
🏢実務担当者:Offers actionable insights for port operators to reduce costs and emissions via hydrogen-electric coordination.
🏛政策担当者:Highlights the potential of hydrogen infrastructure in achieving port decarbonization targets.
📄 Abstract（原文）
This article proposes a multistage stochastic programming framework for the integrated day-ahead and intraday scheduling of hydrogen–electric energy and logistics systems. This framework establishes a deep cyber-physical coupling mechanism by unlocking the demand–response potential of reefer thermal dynamics and leveraging hydrogen infrastructure as a controllable industrial buffer to synchronize power supply with port operations. To address temporal uncertainties in ship arrivals and renewable generation, a Markov chain-based stochastic dual dynamic integer programming algorithm is developed. By leveraging finite-state Markovian transitions and cut families, the proposed algorithm overcomes the curse of dimensionality, ensuring computational tractability and high solution quality for large-scale problems. Case studies based on Ningbo–Zhoushan Port real-world data demonstrate significant techno-economic benefits. The proposed coordination achieves a 33% reduction in total operating costs, a 30% cut in peak demand, and a 51% abatement in emissions compared to uncoordinated operations. Comparative benchmarks validate the method's robustness in dampening disturbance propagation and reducing cost volatility. Sensitivity analysis confirms the structural necessity of the hybrid architecture, where an optimized automated guided vehicle ratio balances charging latency against energy costs. Furthermore, results demonstrate that sufficient hydrogen capacity transforms the system into an active flexibility resource, establishing the framework as a prerequisite for energy-autonomous seaports.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/tii.2026.3673736first seen 2026-08-07 05:32:27
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