Cooperative scheduling of multi-data-center systems under an electricity–carbon coupling framework
マルチデータセンターシステムの電力・炭素結合フレームワーク下での協調スケジューリング (AI 翻訳)
Xuefeng Ma, S. Fan
🤖 gxceed AI 要約
日本語
データセンター(DC)が電力・炭素結合(ECC)取引に積極参加するための協調ゲームに基づく最適スケジューリング手法を提案。DC間のワークロード移行やエネルギー貯蔵、炭素枠取引を統合し、コスト削減と排出削減を両立。Nash交渉理論とADMMアルゴリズムにより各DCのプライバシーを保護。シミュレーションで有効性を確認。
English
This study proposes a cooperative game-based scheduling method for multiple data centers under an electricity-carbon coupling trading framework. It integrates workload migration, energy storage, and carbon allowance trading to minimize costs and emissions while preserving data center privacy via Nash bargaining and ADMM. Simulation results show significant reductions in both operational costs and carbon emissions, supporting low-carbon DC operations.
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
日本でもデータセンターのエネルギー消費増加とカーボンプライシング導入（東京都排出量取引など）が進む中、複数DC間の協調による効率的な炭素管理の枠組みは実践的示唆を与える。特に、電力市場と炭素市場の連携を扱う点が日本企業のGX戦略にも参考となる。
In the global GX context
This paper offers a novel framework for integrating electricity and carbon markets in multi-data-center operations, relevant globally as data center energy demand surges. The cooperative game approach balances efficiency and privacy, providing insights for carbon market design and industrial decarbonization under evolving climate policies.
👥 読者別の含意
🔬研究者:Novel combination of cooperative game theory and carbon-electricity coupling for data center scheduling, with algorithmic contributions.
🏢実務担当者:Operational framework for reducing carbon footprint and energy costs across multiple data centers via coordinated trading and workload migration.
🏛政策担当者:Demonstrates practical integration of carbon pricing in a high-growth sector, informing policy design for electricity-carbon market coupling.
📄 Abstract（原文）
To promote the active participation of data centers (DCs) in electricity–carbon coupling (ECC) trading and to achieve coordinated multi-entity emission reduction and cost optimization, this study proposes a cooperative game (CG)-based optimal scheduling method for multiple DCs under an ECC trading framework. First, a comprehensive trading framework is developed, in which DCs participate simultaneously in electricity and carbon markets, capturing the coupling relationship between energy prices and carbon quotas while establishing a low-carbon operational model for each DC. Second, based on CG theory, a multi-DC alliance optimization model is formulated, enabling coordinated management of multiple resources and fair benefit sharing through workload migration, energy storage and gas turbine management, and carbon allowance trading. Finally, leveraging Nash bargaining theory, the CG model is decomposed into a cost minimization problem and a benefit allocation problem, which are solved using an alternating direction method of multipliers algorithm, thereby preserving the privacy of each DC. Simulation results demonstrate that the proposed approach effectively reduces both operational costs and carbon emissions, providing theoretical and technical support for low-carbon and economically efficient operation of multi-DC systems.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1063/5.0316369first seen 2026-05-15 17:16:20
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