Interpretable and Robust Day-Ahead Low-Carbon Scheduling of Integrated Multi-Energy Systems with Hourly Carbon-Intensity Tracing
時間単位の炭素強度追跡を用いた統合マルチエネルギーシステムの解釈可能でロバストな日前低炭素スケジューリング (AI 翻訳)
Jibin Zhang, Yuanyuan Song, Xiaohua Fan, Xuefei Liu, Congwei Bi
🤖 gxceed AI 要約
日本語
本論文は、水素ベースの統合マルチエネルギーシステムにおいて、マルチキャリア協調、需要応答、Wasserstein分布ロバスト最適化、時間単位の炭素強度追跡を統合したスケジューリングフレームワークを提案する。ケーススタディにより、総運用コストを23.6%、CO2関連コストを52.2%、風力出力抑制コストを74.1%削減できることを示した。
English
This paper proposes a scheduling framework for integrated hydrogen-based multi-energy systems, integrating multi-carrier coordination, demand response, Wasserstein distributionally robust optimization, and hourly carbon-intensity tracing. Case studies show total operating cost reduction of 23.6%, with CO2-related costs and wind curtailment costs reduced by 52.2% and 74.1%, respectively.
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
日本の水素社会実現やカーボンニュートラル目標に関連。マルチエネルギーシステムの統合運用最適化手法が、国内のエネルギーシステムに示唆を与える。
In the global GX context
Contributes to deep decarbonization of energy systems through integrated hydrogen-based systems and carbon tracing. Relevant for global energy transition and carbon accounting standards.
👥 読者別の含意
🔬研究者:Provides a robust optimization framework for multi-energy systems with carbon tracking, useful for further research in energy system integration.
🏢実務担当者:Offers an operational scheduling approach to reduce both costs and emissions in multi-energy systems, applicable to energy utilities and industrial users.
🏛政策担当者:Demonstrates the value of demand response and carbon pricing mechanisms in integrated energy systems for emission reduction.
📄 Abstract（原文）
Integrated hydrogen-based multi-energy systems play a key role in deep decarbonization, yet their day-ahead operation is challenged by cross-carrier coupling, uncertainty, and carbon accountability. This paper proposes a unified scheduling framework that integrates multi-carrier coordination, demand response (DR), Wasserstein distributionally robust optimization (DRO), and hourly carbon-intensity tracing. A mixed-integer linear programming model is developed to capture CHP, power-to-hydrogen, methanation, carbon capture and storage (CCS), and multi-carrier storage within a unified framework. A scenario-adaptive Wasserstein DRO approach is employed to handle correlated uncertainties, while a two-case design quantifies the value of DR. Case studies show that the proposed method reduces total operating cost by 23.6%, with CO 2 -related costs and wind curtailment costs reduced by 52.2% and 74.1%, respectively. The DRO framework improves robustness against uncertainty, while carbon-intensity tracing reveals distinct temporal emission patterns across energy carriers. The results demonstrate that the proposed approach enables cost-effective, robust, and carbon-aware operation of integrated multi-energy systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1016/j.rineng.2026.112158first seen 2026-07-30 05:27:51
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