スタックルバーグゲーム理論に基づくマルチエージェント炭素価格多段階ロバスト最適化手法
A multi-agent carbon price multi-stage robust optimization method based on Stackelberg game theory (原題)
Ziniu Li, Chen Anlan, Yingxiang Wang, Siwu Li, Haoqin Zhang, Peng Xia, Bo Yuan
🤖 gxceed AI 要約
日本語
スタックルバーグゲーム理論を用い、炭素価格を短期（リアルタイム市場連動）・中期（配额配分と期間間取引）・長期（低炭素技術投資）の三段階に分けたマルチエージェント最適化モデルを構築。動的炭素価格不確実性集合と適応分解型二層反復アルゴリズムで炭素価格リスクを定量化する。実験では再生可能エネルギー消費率が86.3%に達し、需給弾力性に基づく炭素価格が顕著な優位性を示した。
English
A Stackelberg-game multi-agent model splits carbon pricing into short-, medium-, and long-term stages with a dynamic uncertainty set and adaptive two-layer decomposition algorithm. Experiments show an 86.3% daily renewable consumption rate (12.5pp above baseline) and a supply-demand-elasticity carbon price ranging 140-275 yuan/ton, improving renewable absorption and emission reduction.
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
中国の電力市場と炭素市場の連動設計は、日本でのGX推進における炭素価格制度や容量市場・需給調整市場との統合設計を検討する際の参考事例となる。特に多段階の時間軸で炭素価格リスクを扱う枠組みは、SSBJ開示におけるシナリオ分析や内部炭素価格設定の高度化に示唆を与える。
In the global GX context
This work links carbon-market pricing with power-market dispatch through a multi-stage robust framework, offering a modeling template for jurisdictions designing carbon-price-linked electricity markets. It contributes to global disclosure scholarship by demonstrating how dynamic carbon-price uncertainty can be quantified for scenario analysis and internal carbon pricing under TCFD/ISSB.
👥 読者別の含意
🔬研究者:炭素価格と電力市場の連動を多段階ロバスト最適化で扱う定式化とアルゴリズム設計の参考になる。
🏢実務担当者:内部炭素価格の設定や再生可能エネルギー調達・投資計画のシナリオ設計に応用可能な考え方を提供する。
🏛政策担当者:炭素市場と電力市場の制度連動設計や価格安定化メカニズムの検討材料となる。
📄 Abstract（原文）
In a multi-agent carbon price multi-stage robust optimization method based on Stackelberg game theory, the multi-agent carbon price is first divided into stages. The short-term focus is on real-time intraday market linkage; the medium-term focus is on carbon quota allocation and inter-period trading; and the long-term focus is on low-carbon technology investment planning, forming a closed-loop regulation. A multi-agent carbon price multi-stage robust optimization model is constructed, introducing a dynamic carbon price uncertainty set. The outer layer coordinates overall planning, and the inner layer performs robust optimization to characterize carbon price risk and provide relevant constraints. A two-layer iterative algorithm based on adaptive decomposition is proposed to solve the model. The carbon price uncertainty quantification layer uses scenario tree generation technology, and the agent strategy collaborative optimization layer constructs an ATC two-layer decomposition framework. Convergence is determined by dynamic compression based on upper and lower bounds, and the optimal strategy set is output to achieve multi-agent carbon price multi-stage robust optimization. Experimental results show that the average daily renewable energy consumption rate in scenario D reaches 86.3%, which is 12.5 percentage points higher than that in scenario A. Significant power increases are observed at noon and evening peak hours at 20:00, with a large advantage in average daily power. In a comparison of carbon pricing models, the carbon pricing mechanism based on supply and demand elasticity shows significant advantages, with prices at 140 yuan/ton at 4:00 AM and 275 yuan/ton at 12:00 PM. This method improves the efficiency of new energy consumption and power allocation, resulting in remarkable carbon emission reduction.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1049/icp.2026.2860first seen 2026-10-25 04:57:37 · last seen 2026-11-06 05:03:24
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