Optimal Scheduling of Data Center Clusters with Distributed and Shared Energy Storage Under a Carbon Trading Mechanism
カーボン取引メカニズムにおける分散型および共有型エネルギーストレージを統合したデータセンタークラスタの最適スケジューリング (AI 翻訳)
Xiaolin Chu, Peng Wang, Ruijuan Zhao
🤖 gxceed AI 要約
日本語
本論文は、カーボン取引メカニズムの下で、分散型蓄電システム（DES）と共有型蓄電システム（SES）を統合したデータセンタークラスタ（DCC）の最適なエネルギー・ワークロード配分モデルを提案。混合整数非線形計画（MINLP）モデルを用いて、電気料金と炭素取引コストを最小化。ケーススタディでは、DESのみの構成と比較して、DESとSESの併用によりコスト46.19％削減、排出量54.02％削減を達成。需要応答（DR）参加により更なる削減効果を示した。
English
This paper proposes an optimal energy and workload dispatch model for data center clusters (DCC) integrating distributed energy storage (DES) and shared energy storage (SES) under a carbon trading mechanism. A Mixed-Integer Nonlinear Programming (MINLP) model minimizes electricity and carbon trading costs. Case studies show a 46.19% cost reduction and 54.02% carbon emission reduction compared to DES-only configuration. Demand response further improves reductions. Sensitivity analyses on carbon prices and storage capacities are provided.
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、日本のカーボンプライシング（J-クレジットなど）やデータセンターのエネルギー効率化政策に示唆を与える。特に、共有型蓄電システムの導入がコストと排出量の両面で有効である点は、日本のデータセンター事業者やエネルギー管理に関連する。
In the global GX context
This research offers practical insights for data center operators and policymakers under global carbon pricing mechanisms (e.g., EU ETS, China ETS). The model demonstrates how integrating shared energy storage with demand response can significantly cut costs and emissions, relevant to the ongoing digital infrastructure expansion and climate goals.
👥 読者別の含意
🔬研究者:Provides a MINLP model for multi-energy coordination in data center clusters, with sensitivity analysis on carbon prices and storage capacities.
🏢実務担当者:Data center operators can apply the co-optimization of DES, SES, and DR to reduce energy costs and carbon exposure under carbon trading.
🏛政策担当者:Demonstrates that higher carbon prices reduce emissions but increase costs, while larger storage capacities mitigate both, informing carbon pricing and storage subsidy design.
📄 Abstract（原文）
The rapid growth in data storage and computing demand has substantially increased the energy consumption and carbon emissions of the data center (DC). Energy storage systems facilitate multi-source energy coordination, while DCs are playing an expanding role in the demand response (DR) program. This paper proposes an optimal energy and workload dispatch model for a DC cluster (DCC) integrating distributed energy storage (DES) and shared energy storage (SES) under the carbon trading mechanism. A Mixed-Integer Nonlinear Programming (MINLP) model is formulated to minimize the economic objective, including the electricity-related cost and carbon trading cost. Case study results show that, compared with a DES-only configuration, the DCC incorporating both DES and SES can achieve a 46.19% reduction in cost and a 54.02% reduction in carbon emission. DR participation yields further reductions in both cost and carbon emission. The scalability of the proposed model is validated by evaluating its computational performance across DCC instances with varying numbers of constituent DCs. The study also examines the effects of carbon prices and energy storage capacities on DCC performance. A higher carbon price reduces carbon emissions but increases costs, whereas larger DES and SES capacities reduce both. The proposed model proves solvable and well-behaved across a wide range of renewable energy generation conditions and computing workloads. This research provides practical implications for the sustainable development of DCs under the carbon trading mechanism.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18147359first seen 2026-07-22 04:57:18
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