Low-Carbon Economic Dispatch of Data Center Microgrids via Heat-Determined Computing and Tiered Carbon Trading
データセンターマイクログリッドの熱決定コンピューティングと段階的炭素取引による低炭素経済的運用 (AI 翻訳)
Lijun Ma, Hongru Shi, Guohai Liu, Weiping Lu, Na Gu
🤖 gxceed AI 要約
日本語
本研究は、データセンターマイクログリッドの低炭素経済スケジューリング戦略を提案する。キューイング理論に基づく遅延許容型ワークロードシフトと熱決定コンピューティングによる廃熱回収を統合し、さらに線形化された段階的炭素取引メカニズムを組み込んだ混合整数線形計画問題として定式化。シミュレーションの結果、総運用コストを11.7%削減し、炭素排出量を6879kgに最小化、日常のPUEを1.2607に最適化した。
English
This study proposes a collaborative low-carbon economic scheduling strategy for data center microgrids, integrating delay-tolerant workload shifting based on queuing theory and waste heat recovery through heat-determined computing, along with a linearized tiered carbon trading mechanism. Simulation results show a dual optimization: total operating costs reduced by 11.7%, carbon emissions minimized to 6879 kg, and daily PUE optimized to 1.2607, quantifying the marginal benefits of load flexibility under tiered pricing.
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電力消費増大とカーボンニュートラル目標が課題となる中、本論文は廃熱回収と段階的炭素取引を組み合わせた運用最適化手法を提示。日本のデータセンター事業者やエネルギー政策にとって、コストと排出の同時削減に向けた具体的な方策を示す点で有用。
In the global GX context
This paper provides a novel integration of carbon trading mechanisms into data center operations, demonstrating how tiered carbon pricing can incentivize load flexibility and waste heat recovery. It offers a practical model for global data centers seeking to align operational efficiency with decarbonization goals under emerging carbon pricing regimes.
👥 読者別の含意
🔬研究者:This paper presents a novel MILP formulation combining queuing theory, thermal dynamics, and tiered carbon trading, valuable for research on data center energy optimization and carbon-aware computing.
🏢実務担当者:Data center operators can adopt the proposed strategy to reduce operating costs and carbon emissions by leveraging workload flexibility and waste heat recovery.
🏛政策担当者:Policymakers can gain insights into how tiered carbon trading mechanisms can drive operational changes in energy-intensive sectors like data centers.
📄 Abstract（原文）
The exponential growth of the digital economy has transformed data centers into major energy consumers, yet their inflexible power consumption patterns and substantial waste heat generation pose significant challenges to grid stability and carbon neutrality targets. Existing energy management strategies often overlook the deep coupling potential between computing workload flexibility, thermal dynamics, and carbon trading mechanisms, leading to suboptimal resource utilization. To address these issues, this study proposes a collaborative low-carbon economic scheduling strategy for data center microgrids. A multiple-dimensional coupling framework is established, integrating a queuing theory-based model for delay-tolerant workload shifting and a heat-determined computing mechanism for active waste heat recovery (WHR). Furthermore, a mixed-integer linear programming (MILP) model is formulated, incorporating a linearized tiered carbon trading mechanism to facilitate source–load coordination. Simulation results demonstrate that the proposed strategy achieves a dual optimization of economic and environmental benefits, reducing total operating costs by 11.7% while minimizing carbon emissions to 6879 kg compared to baseline scenarios. Additionally, by leveraging temperature aware load migration, the daily weighted power usage effectiveness (PUE) is optimized to 1.2607. These findings quantify the marginal benefits of load flexibility under tiered pricing, providing insights for operators to balance service timeliness and energy efficiency in next generation green computing infrastructure.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/en19030699first seen 2026-05-05 22:57:47
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。