A Cooperative Game-Based Low-Carbon Optimal Operation Strategy for Multi-Microgrids Based on Multi-Agent Deep Reinforcement Learning
マルチエージェント深層強化学習に基づく協力ゲームを用いたマルチマイクログリッドの低炭素最適運用戦略 (AI 翻訳)
Pengfei Zhang, Pan Liu, Li Jiang, Dong Han
🤖 gxceed AI 要約
日本語
本論文は、マルチマイクログリッドの低炭素運用を最適化するため、協力ゲーム理論とマルチエージェント深層強化学習を組み合わせた戦略を提案する。エネルギー・炭素・グリーン証明書のP2P取引メカニズムとNash交渉に基づく公平な利益配分を導入し、集中訓練・分散実行の枠組みで運用コストを削減する。中国長江デルタの事例で、集中型MILPと比較して最適性ギャップ1.25%、独立運用と比較して連合運用コスト8.19%削減、炭素取引コストを33〜40%削減することを実証した。
English
This paper proposes a cooperative game-based low-carbon operation strategy for multi-microgrids using multi-agent deep reinforcement learning. It introduces an energy-carbon-green certificate P2P trading mechanism and Nash bargaining for fair benefit allocation, achieving near-optimal performance (1.25% gap vs. MILP) and reducing coalition operating costs by 8.19% and carbon trading costs by 33-40% in a Yangtze River Delta case study.
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、地域エネルギーシステムの脱炭素化が進む中、マイクログリッド間の連携やP2P取引は今後の重要課題。本手法は、カーボンプライシングやグリーン証明書の活用を通じて、エネルギー取引と環境価値の統合的な最適化を可能にし、日本の地域エネルギー管理や需給調整市場への示唆を与える。
In the global GX context
Globally, this research contributes to the growing field of AI-driven energy management and carbon trading. It demonstrates how multi-agent reinforcement learning can handle complex multi-commodity trading and cooperative game dynamics, offering a scalable approach for low-carbon microgrid operation that aligns with international carbon pricing and renewable certificate mechanisms.
👥 読者別の含意
🔬研究者:Provides a novel integration of cooperative game theory and MARL for multi-microgrid low-carbon operation, with quantitative results on cost and carbon reductions.
🏢実務担当者:Offers a practical framework for optimizing energy and carbon trading among microgrids, potentially reducing operational costs and carbon compliance expenses.
🏛政策担当者:Highlights the potential of AI-based coordination mechanisms to enhance the efficiency of carbon markets and renewable certificate systems at the regional level.
📄 Abstract（原文）
Distributed integrated energy microgrids support the low-carbon transition of regional energy systems. However, multi-agent trading among microgrids still faces insufficient cross-market coordination, weak low-carbon incentives, and difficulties in fair benefit allocation. To address these issues, this paper proposes a cooperative-game-based low-carbon optimal operation strategy for multi-microgrids using multi-agent deep reinforcement learning. First, an energy-carbon-green certificate peer-to-peer coordinated trading mechanism and a green-carbon offsetting-based dual-incentive model are developed to link energy exchange, carbon quota adjustment, and green certificate circulation. Second, a Nash bargaining-based cooperative game model is formulated for multi-commodity P2P trading to maximize coalition benefits and ensure a fair allocation of surplus. Finally, the cooperative game is transformed into a Markov decision process, and a centralized training and decentralized execution framework with homogeneous agents is constructed based on the multi-agent soft actor-critic algorithm. Case studies using data from the Yangtze River Delta region of China show that the proposed method achieves a 1.25% optimality gap compared with the centralized MILP benchmark and reduces the coalition operating cost by 8.19% relative to independent operation. The carbon trading costs of the three microgrids are reduced by 37.27%, 40.13%, and 33.82%, respectively, verifying the economic applicability of the proposed method.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/en19153683first seen 2026-08-07 05:01:25
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