Low‐Carbon Optimal Dispatch Strategy Incorporating Attention‐Based Dynamic Carbon Emission Factors of Thermal Power Units
注意機構に基づく火力発電ユニットの動的炭素排出係数を組み込んだ低炭素最適運用戦略 (AI 翻訳)
Xin Huang, Yixin Li, Keteng Jiang, Shucan Zhou, gaohong liu, Haibo Li
🤖 gxceed AI 要約
日本語
本論文は、火力発電ユニットの動的炭素排出係数を時空間注意機構と絶対位置符号化でモデル化し、電力・炭素連成スケジューリングに組み込む低炭素最適運用戦略を提案。中国の省級電力系統シミュレーションで、運用コスト2.69%削減、再生可能エネルギー利用率0.18%向上、炭素排出3.35%削減を実証。高再生可能エネルギー系統での低炭素運用とカーボンニュートラル目標に貢献する。
English
This paper proposes a low-carbon optimal dispatch strategy that models dynamic carbon emission factors of thermal power units using spatiotemporal attention and absolute position encoding, integrated into a power-carbon coupled scheduling framework. Simulations on a Chinese provincial grid show 2.69% lower operating costs, 0.18% higher renewable utilization, and 3.35% lower carbon emissions, supporting low-carbon operations in high-renewable systems.
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
日本の電力系統でも再生可能エネルギー導入拡大に伴い、火力発電の運用最適化と炭素排出削減の両立が課題。本手法はSSBJや有報での排出量算定精度向上に寄与し、系統運用者や電力会社の低炭素投資判断に示唆を与える。
In the global GX context
Globally, this work advances the integration of dynamic emission factors into power system dispatch, aligning with TCFD/ISSB expectations for accurate carbon accounting. It offers a scalable approach for grid operators to reduce emissions and costs, relevant to jurisdictions with high renewable penetration.
👥 読者別の含意
🔬研究者:Provides a novel AI-based method for dynamic carbon emission modeling and its integration into power dispatch optimization, useful for advancing low-carbon scheduling research.
🏢実務担当者:Offers a practical strategy for power utilities to reduce operating costs and emissions through dynamic emission factor-based dispatch, aiding in sustainability reporting and compliance.
🏛政策担当者:Demonstrates the potential of AI-driven dispatch to achieve carbon reduction targets, informing policy on grid modernization and renewable integration.
📄 Abstract（原文）
Traditional scheduling methods fail to accurately capture the dynamic carbon emission characteristics of thermal power units across different operating stages, leading to inefficient unit selection, increased emissions, and higher operating costs. To address these challenges under low‐carbon constraints, this paper proposes a low‐carbon optimal scheduling strategy that integrates a dynamic carbon emission model into the decision‐making process. First, a nonlinear model of the dynamic carbon emission factor of thermal power units is constructed by combining spatiotemporal attention mechanisms and absolute position encoding. This model captures the complex relationships between unit type, fuel characteristics, and load levels, achieving high‐precision estimation of the carbon emission factor. Second, the obtained dynamic carbon emission factor is embedded into a power‐carbon coupled scheduling framework for heterogeneous generating units, thereby achieving coordinated operation of units under low‐carbon conditions and improving system‐level decision‐making. Finally, simulation results based on a provincial power grid in China show that, compared with traditional scheduling methods using static emission factors, the proposed strategy can reduce total operating costs by 2.69%, increase renewable energy utilization by 0.18%, and reduce carbon emissions by 3.35%, providing strong technical support for low‐carbon scheduling and carbon neutrality goals in power systems with high renewable energy penetration. © 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
- openalex https://doi.org/10.1002/tee.70403first seen 2026-08-11 04:36:40
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