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Adaptive Energy Stations for Sustainable Transport Infrastructure: Real-Time Dispatch Optimization Using Marginal Grid Emissions and Low-Carbon Fuel Pathways

持続可能な交通インフラのための適応型エネルギー・ステーション：限界系統排出量と低炭素燃料経路を用いたリアルタイム配電最適化 (AI 翻訳)

Marco Aurélio dos Santos Bernardes

Clean Technologies📚 査読済 / ジャーナル2026-07-29#AI×ESGOrigin: US経営インパクト: コスト削減対象セクター: transport
DOI: 10.3390/cleantechnol8040115
原典: https://doi.org/10.3390/cleantechnol8040115

🤖 gxceed AI 要約

日本語

本研究は、交通脱炭素化のための適応型エネルギー・ステーション（AES）を提案し、限界系統排出量、燃料ライフサイクル炭素強度、卸電力価格、車両制約を統合したステーション配電最適化を実装した。CAISOの1週間の冬季実証では、静的ベースライン比で32.12%のCO2排出削減を達成。ただし、結果は特定週の運用シナリオに限定され、バッテリー生産排出の扱いによりBEVとセルロース系E85の等価性が変わることを明示している。

English

This study introduces Adaptive Energy Stations (AESs) that integrate marginal grid emissions, fuel life-cycle carbon intensities, wholesale prices, and vehicle constraints into station-level dispatch optimization. A one-week winter CAISO proof-of-method shows a 32.12% CO2e/km reduction over a static baseline, but results are scenario-specific and sensitive to battery-production emissions treatment.

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

日本の交通・エネルギーインフラの脱炭素化に示唆を与える。SSBJ開示やカーボンプライシング政策と連動し、時間分解排出係数を用いた運用最適化は、日本の電力系統（地域別排出係数）にも応用可能。ただし、日本のデータでの検証が今後の課題。

In the global GX context

This work contributes to global transport decarbonization by demonstrating real-time dispatch optimization using marginal emissions, aligning with TCFD/ISSB disclosure trends that demand time-resolved carbon accounting. It highlights the importance of transparent boundaries and sensitivity analysis, relevant for CSRD and SEC climate disclosure.

👥 読者別の含意

🔬研究者:Provides a framework for integrating marginal emissions and fuel pathways into dispatch optimization, with clear caveats on boundary conditions.

🏢実務担当者:Offers a method for transport-energy operators to reduce carbon intensity in real-time, but requires validated data and cybersecurity measures.

🏛政策担当者:Highlights the need for standardized marginal-emission data access and transparent dispatch auditing to support low-carbon transport infrastructure.

📄 Abstract（原文）

Transport decarbonization requires infrastructure that can use time-resolved carbon information without overstating the representativeness of short proof-of-method runs. This study introduces Adaptive Energy Stations (AESs), multi-fuel transport-energy nodes that integrate marginal grid-emission signals, fuel life-cycle carbon intensities, wholesale electricity prices, and vehicle operating constraints into a station-level dispatch optimization. The implemented case is a one-week winter proof-of-method for CAISO/CAISO_NORTH using 168 hourly service events over 1–8 January 2026 Pacific time, archived WattTime marginal operating emissions, CAISO locational marginal prices, eGRID CAMX annual-average factors, and declared vehicle and fuel-pathway parameters. In the audited CAISO scenario, the attached dispatch outputs report a reduction from 181.76 to 123.38 g CO2e/km relative to the specified static baseline, corresponding to a 32.12% reduction for the one-week winter service-event stream. The populated dispatch trace shows that the carbon-priority AES plug-in hybrid electric vehicle (PHEV) run selected cellulosic E85 for all 168 events and selected no electric events; this result is interpreted as an operational scenario result for the archived week, not as an annual fleet-average, smart-charging benefit, or deployment forecast. The revised analysis explicitly separates implemented CAISO evidence from ERCOT, MISO-MROW, and ISO–NE extension sensitivities, which remain hypothetical until equivalent marginal-emissions, price, and service-event data are supplied. Battery-production amortization is treated as a separate sensitivity because it can change battery electric vehicle (BEV)–cellulosic E85 equivalence conclusions: at 50–100 kg CO2e/kWh over 240,000 km, a 75 kWh BEV pack contributes 15.6–31.3 g CO2e/km and a 14 kWh PHEV pack contributes 2.9–5.8 g CO2e/km. Practical-equivalence claims are therefore conditional on the declared boundary, equivalence margin, and production-emissions treatment. Full deployment requires validated marginal-emission access, transparent dispatch-audit outputs, supply-chain verification, user-behavior characterization, cost sensitivity analysis, and cybersecurity safeguards.

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