Optimized Scheduling of Electricity‐Hydrogen‐Ammonia‐Carbon Integrated Energy System Based on Distributionally Robust Optimization
分布ロバスト最適化に基づく電力‐水素‐アンモニア‐炭素統合エネルギーネットワークの最適スケジューリング (AI 翻訳)
Wang Zhibo, Haijun Xing, Yang Kun, Shijie Zhuang, Y. Xiao, Wang Qiwei
🤖 gxceed AI 要約
日本語
本論文は、風力・太陽光出力の不確実性と多エネルギー連携を考慮し、電力‐水素‐アンモニア‐炭素統合エネルギーネットワークの分布ロバスト最適化手法を提案。ガウス混合モデルで不確実性をモデル化し、アンモニア混焼・酸素燃焼による石炭火力の協調脱炭素モデルと段階的炭素価格メカニズムを組み込んだ。事例検証により、経済性・低炭素性・運用頑健性の向上を確認。
English
This paper proposes a distributionally robust optimization scheduling method for an electricity-hydrogen-ammonia-carbon integrated energy system, addressing wind-solar uncertainties and multi-energy collaboration. It models renewable uncertainty via Gaussian Mixture Models, integrates ammonia co-firing and oxy-fuel combustion for coal-fired units with a stepped carbon pricing mechanism. Case studies confirm enhanced economy, low-carbon performance, and operational robustness.
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
本論文は、日本が進めるGX（グリーントランスフォーメーション）の一環である水素・アンモニアサプライチェーン構築や炭素価格導入の政策文脈に直接貢献する。特に、アンモニア混焼と酸素燃焼を組み合わせた石炭火力の脱炭素モデルは、日本の既存火力発電所の転換策として示唆に富む。
In the global GX context
This paper contributes to global GX efforts by presenting a scheduling framework that integrates hydrogen, ammonia, and carbon capture with a stepped carbon pricing mechanism. It is relevant for regions exploring fossil fuel plant decarbonization via co-firing and carbon pricing, and offers a robust optimization approach for high-renewable systems.
👥 読者別の含意
🔬研究者:Provides a novel distributionally robust optimization model for multi-energy systems with coupled decarbonization and carbon pricing, applicable to further studies on uncertainty handling.
🏢実務担当者:Offers a concrete scheduling method for integrated energy systems involving hydrogen/ammonia and carbon capture, which can inform operational strategies for utilities.
🏛政策担当者:Illustrates how carbon pricing and technology mandates (ammonia co-firing) can be incorporated into an optimization framework, supporting policy design for power sector decarbonization.
📄 Abstract（原文）
To address the uncertainties of wind‐solar power outputs and multi‐energy collaborative optimization in integrated energy systems with high renewable penetration, this paper proposes a distributionally robust optimization (DRO) scheduling method for an electricity‐hydrogen‐ammonia‐carbon system. First, a Gaussian Mixture Model (GMM) is used to model the probability distribution of wind‐solar outputs, generating initial scenarios that cover diverse spatiotemporal fluctuation characteristics, which are then reduced to typical scenarios via improved K‐medoids clustering. Second, a hydrogen‐ammonia‐carbon multi‐energy coupling framework is designed, incorporating a collaborative decarbonization model for coal‐fired units via ammonia co‐firing and oxy‐fuel combustion, with a stepped carbon pricing mechanism and a power‐to‐hydrogen‐to‐ammonia conversion scheduling strategy embedded. A DRO approach is introduced to balance robustness and economy under extreme scenarios, constructing a wind‐solar output uncertainty set via combined 1‐norm/∞‐norm constraints and solving the model using the Column and Constraint Generation (C&CG) algorithm. Case studies validate the method's effectiveness in enhancing system economy, low‐carbon performance, and operational robustness. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1002/tee.70249first seen 2026-05-15 19:58:06
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