Distributionally Robust Optimization for Integrated Energy System with Tiered Carbon Trading: Synergizing CCUS with Hydrogen Blending Combustion
段階的炭素取引を組み込んだ統合エネルギーシステムにおける分布ロバスト最適化：CCUSと水素混焼の相乗効果 (AI 翻訳)
Mingyao Huang, Meiheriayi Mutailipu, Peng Wang, Jun Huang, Fusheng Xue, Xiaofeng Li
🤖 gxceed AI 要約
日本語
本研究は、水素精製と段階的炭素取引を統合したエネルギーシステム（IES）を提案し、不確実性に対処するためカーネル密度推定に基づく分布ロバスト最適化を導入。CCUSと水素混焼の相乗効果により、コストと炭素排出を大幅に削減できることを示した。カーボンプライシングの設計がシステム性能に与える影響も分析している。
English
This study presents an integrated energy system (IES) with hydrogen refinement and tiered carbon trading, using distributionally robust optimization with kernel density estimation to handle uncertainties. Results show that combining CCUS with hydrogen blending combustion reduces costs by up to 7.3% and carbon emissions by up to 6% compared to baseline scenarios. The tiered carbon trading mechanism outperforms fixed-price trading, achieving additional 3.5% emission reductions.
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
日本は水素基本戦略やCCS長期ロードマップを推進しており、本論文の水素混焼とCCUSの統合アプローチは日本のGX政策と親和性が高い。また、段階的炭素取引の実証的知見は、国内排出量取引制度の設計に示唆を与える。
In the global GX context
This paper bridges carbon pricing, hydrogen blending, and CCUS within an optimization framework, offering practical insights for designing tiered carbon trading mechanisms. The results highlight the synergy between hydrogen utilization and carbon capture, which is highly relevant for global energy transition strategies under the Paris Agreement.
👥 読者別の含意
🔬研究者:The distributionally robust optimization approach with KDE uncertainty modeling is a methodological contribution for integrated energy system studies.
🏢実務担当者:The tiered carbon trading and hydrogen blending model provides a benchmark for cost and emission reduction potentials in industrial energy systems.
🏛政策担当者:The sensitivity analysis on carbon trading parameters offers evidence for designing effective carbon pricing policies that incentivize CCUS and hydrogen adoption.
📄 Abstract（原文）
In this study, an Integrated Energy System (IES) with hydrogen refinement within a tiered carbon trading mechanism (TCTM) is presented to improve energy efficiency and support decarbonization. To address uncertainties in the IES, a distributionally robust optimization (DRO) approach, employing a fuzzy set framework with Kernel Density Estimation (KDE) to construct error distributions and specify output ranges for renewable energy (RE), is proposed. Latin hypercube sampling (LHS) and K-means clustering are, respectively, applied to generate original and representative scenarios. Subsequently, case studies are performed to evaluate advantages of the presented model. The results indicate that hydrogen refinement within the TCTM framework has substantial benefits for the IES. Specifically, the proposed scenario integrates hydrogen blending combustion (HBC) with synthetic methane, demonstrating significant economic and carbon benefits, with cost reductions of 7.3%, 7.1%, and 4.3% and carbon emission reductions of 6%, 3%, and 2.4% compared to scenarios with no hydrogen utilization, HBC only, and synthetic methane only, respectively. In contrast, to exclude carbon trading and include fixed-price trading, the TCTM achieves a 3.5% and 1.1% reduction in carbon emissions, respectively. Finally, a comprehensive sensitivity analysis is performed, examining factors such as the ratio of hydrogen blending, price, and growth rate of carbon trading.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/pr14020328first seen 2026-05-05 22:56:32
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