Temporally Coordinated Operation of Green Multi-Energy Airport Microgrids With Climatic Correlations and Flexible Loads via Decomposed Stochastic Programming
気候相関と柔軟負荷を考慮した分解確率計画法によるグリーン・マルチエネルギー空港マイクログリッドの時間的協調運用 (AI 翻訳)
Zhongtian Li, Patrik Hilber, Zhengmao Li, Tor Laneryd, Stefan Ivanell
🤖 gxceed AI 要約
日本語
本論文は、電動・水素航空機の普及を見据え、空港マイクログリッド（MEAM）の最適運用を目的とした2段階確率計画モデルを提案する。気候条件と負荷の相関をコピュラ法で捉え、水素製造効率への影響を考慮。ケーススタディにより、コスト最小化とシステム安定性の両立が可能であることを示した。
English
This paper proposes a two-stage stochastic programming model for optimizing green multi-energy airport microgrids (MEAM) that integrate electricity, hydrogen, and thermal energy. It uses copula to capture climatic correlations and an adapted Progressive Hedging algorithm to reduce computational burden. Case studies demonstrate effective coordination for cost minimization and security.
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
本研究成果は、日本が進める空港の脱炭素化（例：成田・関西での水素導入検討）に直接応用可能な技術基盤を提供する。特に、水素と再エネの統合運用モデルは、日本のGX実現に向けたエネルギーシステム設計に示唆を与える。
In the global GX context
This work contributes to the global discourse on decarbonizing aviation infrastructure by providing a stochastic optimization framework for airport microgrids that incorporate hydrogen and electric aircraft charging. The methodological advances in scenario generation and decomposition are relevant for any multi-energy system facing renewable uncertainty.
👥 読者別の含意
🔬研究者:Provides a stochastic programming framework for coordinating green hydrogen and renewable energy in airport microgrids.
🏢実務担当者:Offers a method to optimize multi-energy airport microgrids integrating hydrogen production and electric vehicle charging.
📄 Abstract（原文）
To cater to the advancement of electric and hydrogen-powered aircraft, airports are increasingly motivated to transition to green multi-energy airport microgrids (MEAM) for efficient operation and integration of stochastic renewable energy sources (RES). This paper presents a temporally coordinated (two-stage) stochastic programming (SP) model to minimize the energy supply cost of MEAMs while enabling efficient operations with electricity, green hydrogen and thermal energy. From the MEAM perspective, first, the multi-energy loads of MEAMs are considered flexible and modeled in details; second, the electricity-to-hydrogen-and-heat (E2HH) model is applied considering the influence of climatic conditions on the electrolyzers' efficiencies. With regard to the SP model, for one thing, the copula method is employed to capture correlations between climatic parameters related to RES generation and loads to enhance the accuracy and fidelity of the generated scenarios; for another, the Jensen wake model is applied to improve the estimation accuracy of available wind power in the generated scenarios. Furthermore, an adapted-penalty Progressive Hedging (PH) model is proposed to decompose the SP model, reducing the computational burden. Finally, case studies indicate that the proposed approach can effectively coordinate the operation of MEAM to balance system security and cost minimization.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/tste.2025.3639360first seen 2026-05-15 20:00:51
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