電化とセンサー劣化下におけるレジリエント低炭素港湾インフラのための不確実性対応デジタルツインフレームワーク
Uncertainty-Aware Digital Twin Framework for Resilient Low-Carbon Port Infrastructure under Electrification and Sensor Degradation (原題)
Seyed Reza Samaei, K.S. Reddy, James Riffat
🤖 gxceed AI 要約
日本語
本研究は、電化が進む港湾インフラを対象に、センサー劣化やデータ同期遅延といった不確実性を明示的に扱うデジタルツインフレームワークを開発した。合成コンテナターミナルを1年間・5分間隔でシミュレーションし、複合ストレス下でも従来型デジタルツイン比で待機遅延18%減、エネルギー不足32%減、復旧時間26%減を達成。低炭素港湾運用には設備容量だけでなく、時間整合性・確率的状態推定・エネルギー柔軟性・情報品質の明示的表現が重要と示す。
English
This study develops an uncertainty-aware digital twin framework for electrified low-carbon ports that explicitly handles sensor degradation, noise and synchronisation delay. Simulating a synthetic container terminal over one year at 5-minute intervals under compound stress, it cuts completed-vessel waiting delay by 18.0%, energy shortfall by 32.1% and recovery time by 26.3% versus a conventional digital twin. Resilient electrified-port operation depends not only on installed electrical capacity but on temporal alignment, probabilistic state estimation, energy flexibility and explicit representation of degraded information quality.
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
港湾の電化・脱炭素は日本のGX政策（グリーン成長戦略、カーボンニュートラルポート）の重点領域であり、本稿のレジリエンス評価手法は港湾管理者・物流事業者の設備投資判断やBCP策定に示唆を与える。SSBJ開示における物理的リスク・適応策の記述にも接続しうる。
In the global GX context
Port electrification and resilience sit at the intersection of TCFD/ISSB physical-risk disclosure and transition planning for hard-to-abate logistics infrastructure. The framework's explicit treatment of degraded data quality and recovery time offers a template for quantifying adaptation capacity in climate-risk reporting, relevant to CSRD and ISSB preparers with port or logistics exposure.
👥 読者別の含意
🔬研究者:不確実性下のデジタルツイン設計とレジリエンス指標の定量化手法を、港湾・エネルギーシステム研究に応用できる。
🏢実務担当者:港湾・物流事業者は、センサー劣化やデータ遅延を前提とした運用設計と電化投資のリスク評価に活用できる。
🏛政策担当者:港湾脱炭素政策において、設備容量だけでなく情報品質・復旧時間を含むレジリエンス基準の設計に示唆を与える。
📄 Abstract（原文）
The electrification of port operations is reshaping the technical relationship between logistics performance, energy demand, infrastructure condition and data reliability. Although digital twins are increasingly used for port monitoring and decision support, many frameworks continue to assume clean sensing, synchronised data streams and stable observability. This study develops an uncertainty-aware digital twin framework for resilient low-carbon port infrastructure under electrification and sensor degradation. A synthetic reference container terminal with 4.5 million TEU/year throughput, six berths, twenty-two quay cranes, 58,000 TEU yard capacity, 120 monitoring channels, 55 MW grid-import capacity, 28 MW renewable capacity and 60 MWh battery storage is simulated for one year at a 5 min update interval. Fifty paired stochastic replications are analysed after a 14-day warm-up period. Under compound stress comprising 35% sensor loss, 5% measurement noise, 10 min synchronisation delay, 90% electrification, 25% higher latent vessel-arrival demand intensity before application of the approach-channel cap, 25% lower renewable availability and 12% lower equipment productivity, the framework yields 10.5 h mean completed-vessel waiting delay, 5.7% energy shortfall, 0.27 infrastructure-health risk, 0.31 uncertainty index, 14.6 h recovery time and a resilience index of 0.64. Relative to a conventional digital twin, completed-vessel waiting delay decreases by 18.0%, energy shortfall by 32.1%, uncertainty index by 29.5% and recovery time by 26.3%. Paired replication-level tests remain significant after Holm adjustment, with large paired effect sizes for the principal operational, energy, uncertainty and resilience outcomes. The results show that resilient electrified-port operation depends not only on installed electrical capacity, but also on temporal alignment, probabilistic state estimation, energy flexibility, health-aware operation and explicit representation of degraded information quality.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.65582/ec.2026.009first seen 2026-10-16 04:55:26
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