Research on bidding equilibrium model of the joint electricity-carbon market based on carbon price discovery
炭素価格発見に基づく電力・炭素統合市場の入札均衡モデルに関する研究 (AI 翻訳)
Mengjie Zhao, Shijie Duan, Tianshuai Jiang, Xin Jiang, Qiangang Jia
🤖 gxceed AI 要約
日本語
本論文は、マルチエージェント強化学習を用いて電力・炭素統合市場の入札均衡モデルを構築。炭素排出基準を約14.21%引き締めると火力発電コストが約13.83%上昇し、再エネ比率が倍増すると火力の入札パラメータが最低1/3低下することを実証。低炭素ユニットは炭素価格優位を収益増に転換でき、「双炭」目標達成に寄与する。
English
This paper develops a bidding equilibrium model for the joint electricity-carbon market using multi-agent reinforcement learning. It finds that tightening carbon benchmarks by ~14.21% increases thermal power costs by ~13.83%, and doubling renewable penetration reduces thermal bidding parameters by at least one-third. Low-carbon units leverage carbon price advantages to increase revenue, promoting the dual carbon goal.
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
本研究は、日本のGX政策における電力市場と炭素市場の連携メカニズムの理解に貢献する。特にSSBJ開示基準やカーボンプライシング制度設計に示唆を与え、日本でも導入が進むカーボンプライシングと電力市場の統合に具体的なモデルを提供する。
In the global GX context
This paper models the interaction between electricity and carbon markets, providing insights for carbon pricing design and market coupling. It is relevant to global GX contexts like EU ETS and emerging carbon markets, and its use of multi-agent reinforcement learning offers a novel approach to bidding strategy analysis.
👥 読者別の含意
🔬研究者:Researchers can extend this model to study dynamic bidding strategies under different carbon pricing mechanisms and market structures.
🏢実務担当者:Corporate sustainability teams can use these findings to assess how carbon prices affect power procurement costs and optimize bidding in integrated markets.
🏛政策担当者:Policymakers can gain insights into the design of integrated electricity-carbon markets, including the impact of carbon benchmark tightening and renewable integration.
📄 Abstract（原文）
The electricity and carbon markets are naturally coupled, and the operation of the carbon trading market links carbon prices closely with power generation costs. Starting from the interactive coupling mechanism of the two markets, this paper first clarifies that the game in the joint electricity-carbon market is a non-cooperative game, and then constructs a bidding equilibrium model for the joint market based on carbon price discovery—with the upper layer being a power generator’s bidding strategy iterative model, which based on multi-agent reinforcement learning, and the lower layer being a joint market clearing model. Finally, using the Win or Learn Fast-Policy Hill Climbing (WOLF-PHC) algorithm, the paper gives the model solution process. Example analysis shows: 1) When the carbon emission benchmark is tightened by ∼14.21%, the carbon transaction cost of traditional thermal power units rises by ∼13.83%; 2) When the system’s new energy penetration doubles, thermal power units’ bidding parameters decrease by at least 1/3; 3) Considering carbon prices, low-carbon units in the joint market tend to increase their bidding parameters more (transforming their carbon emission advantages into higher revenue), which is more conducive to achieving the “dual carbon” goal.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1088/1742-6596/3214/1/012091first seen 2026-05-15 17:15:34
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