Multi-agent Reinforcement Learning for Low-Carbon P2P Energy Trading among Self-Interested Microgrids
自己利益追求型マイクログリッド間の低炭素P2Pエネルギー取引のためのマルチエージェント強化学習 (AI 翻訳)
Junhao Ren, Honglin Gao, Lan Zhao, Qiyu Kang, Gaoxi Xiao, Yajuan Sun
🤖 gxceed AI 要約
日本語
本研究は、不確かな再生可能エネルギー発電と需要変動に対応するため、自己利益追求型マイクログリッド間のP2P電力取引にマルチエージェント強化学習を適用。各マイクログリッドが価格と数量を独立に入札し、蓄電池運用で利益を最大化。市場清算メカニズムにより再生可能エネルギーの利用を促進し、炭素排出を削減しつつコミュニティ全体の経済的厚生を向上させる。
English
This study develops a multi-agent reinforcement learning framework for self-interested microgrids in P2P electricity trading, addressing renewable generation and demand uncertainties. Each microgrid independently bids price and quantity, optimizing profit via storage arbitrage. A market-clearing mechanism improves renewable utilization and reduces carbon emissions while enhancing community economic welfare.
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
日本では分散型エネルギーリソースの活用が進み、マイクログリッド間のP2P取引は再生可能エネルギー普及と地域活性化に寄与する。本研究の強化学習アプローチは、日本の電力市場設計や地域エネルギー管理に示唆を与える。
In the global GX context
This paper contributes to global GX by demonstrating how multi-agent RL can enable decentralized, low-carbon energy trading among prosumers. Its incentive-compatible market design is relevant for scaling renewable energy communities and peer-to-peer platforms worldwide.
👥 読者別の含意
🔬研究者:Provides a novel multi-agent RL framework for P2P energy trading that balances self-interest and community welfare.
🏢実務担当者:Offers a bidding strategy and market mechanism that can be implemented by microgrid operators to increase renewable utilization and profits.
🏛政策担当者:Highlights the potential of RL-based market designs for promoting low-carbon energy trading and local economic benefits.
📄 Abstract（原文）
Uncertainties in renewable generation and demand dynamics challenge day-ahead scheduling. To enhance renewable penetration and maintain intra-day balance, we develop a multi-agent reinforcement learning framework for self-interested microgrids participating in peer-to-peer (P2P) electricity trading. Each microgrid independently bids both price and quantity while optimizing its own profit via storage arbitrage under time-varying main-grid prices. A market-clearing mechanism coordinating trades and promoting incentive compatibility is proposed. Simulation results show that the learned bidding policy improves renewable utilization and reduces reliance on high-carbon electricity, while increasing community-level economic welfare, delivering a win-win situation in emission reduction and local prosperity.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.semanticscholar.org/paper/1ad955176f8c4a5740e85ca018b3ddd07925371afirst seen 2026-05-05 22:56:46
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。