風力・太陽光不確実性を考慮した仮想発電所のための適応的動的更新炭素排出係数に基づく分布ロバスト最適化スケジューリング手法
An Adaptive Dynamically Updated Carbon Emission Factor Based Distributionally Robust Optimization Scheduling Method for Virtual Power Plant Considering Wind-Photovoltaic Uncertainty (原題)
Xiao-Lan Li, Jin-Huang Liang, Tian-Qi Xu, Ling-Ling Kong, Fang-Fang Chen
🤖 gxceed AI 要約
日本語
風力・太陽光の不確実性を混合コピュラとK-meansでシナリオ化し、動的更新炭素排出係数を組み込んだグリーン証書・炭素排出枠取引メカニズムを設計。二段階分布ロバスト最適化により、固定係数比で算出炭素排出量を6.9%削減し、取引収入を129.32元増加。負荷損失率ゼロを経済性を損なわず達成した。
English
This paper integrates mixed-copula scenario generation, K-means clustering, and a dynamically updated carbon emission factor into a green certificate–carbon allowance trading mechanism for virtual power plant scheduling. A two-stage distributionally robust optimization solved via column-and-constraint generation reduces calculated carbon emissions by 6.9% and increases trading revenue by 129.32 CNY versus fixed factors, while achieving zero load loss without sacrificing economics.
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
日本ではVPP実証や需給調整市場の議論が進むが、炭素排出係数の動的更新やグリーン証書と炭素市場の統合設計は今後の制度設計課題であり、SSBJやScope2算定の精緻化にも示唆を与える。
In the global GX context
Globally, this work speaks to the need for more accurate carbon accounting under renewable uncertainty and to the integration of certificate markets with carbon pricing—relevant to ISSB/Scope 2 guidance and transition finance mechanisms that rely on robust emission factors.
👥 読者別の含意
🔬研究者:動的排出係数と分布ロバスト最適化を組み合わせたVPPスケジューリングの定量的ベンチマークを提供する。
🏢実務担当者:再生可能エネルギー調達や炭素取引を行う企業にとって、排出係数の動的更新が炭素コストと収益に与える影響を評価する参考になる。
🏛政策担当者:炭素排出係数の動的更新とグリーン証書制度の設計が、排出量算定の精度と市場効率に与える影響を示唆する。
📄 Abstract（原文）
: Under the carbon peaking and carbon neutrality goals, the uncertainty of distributed energy output complicates virtual power plant (VPP) scheduling, while fixed carbon emission factors distort carbon accounting, green certificate emission reductions are quantified inaccurately, and scenario modeling remains insufficiently precise. To tackle these issues, an integrated framework encompassing scenario generation, mechanism design, and optimization-based solution is constructed. First, raw wind power and photovoltaic output scenarios are produced with a mixed copula function, from which typical scenarios are then extracted via the K-means algorithm. Second, a green certificate-carbon emission allowance trading mechanism that incorporates dynamically updated carbon emission factors is designed, together with a green certificate allocation strategy that seeks to maximize the revenue of green certificate-carbon emission allowance trading. Building on these components, a two-stage distributionally robust optimization model is established and handled through a column-and-constraint generation algorithm. Case study results indicate that, relative to the fixed carbon emission factor scheme, the dynamic scheme lowers the calculated carbon emissions by 6.9%, and raises the green certificate-total carbon trading income by 129.32 CNY. The mixed copula method decreases the prediction errors of wind power and photovoltaic by 4.2658% and 2.9225%, respectively, while the distributionally robust optimization model attains a zero load loss rate without compromising economic benefits.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.techscience.com/energy/online/detail/28860/TSP_EE_88202.pdffirst seen 2026-11-05 05:47:25 · last seen 2026-11-06 05:21:31
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