Research ArticleAI 运维优化
Kevin D. Gauld、Daniel J. Varon、Nicholas Balasus、Daniel H. Cusworth
Published 2026-08-23 · arXiv · Credibility S
AI data center power demand is spurring rapid deployment of on- and near-site natural gas turbines. Nitrogen oxide (NO$_x$) pollution from this equipment is a growing concern but has not previously been quantified with atmospheric observations. Here we demonstrate space-based detection and quantification of NO$_x$ emissions from the SpaceXAI Colossus 2 power plant in Southaven, Mississippi. Using observations from t…
Abstract, interpretation and reference
Abstract
AI data center power demand is spurring rapid deployment of on- and near-site natural gas turbines. Nitrogen oxide (NO$_x$) pollution from this equipment is a growing concern but has not previously been quantified with atmospheric observations. Here we demonstrate space-based detection and quantification of NO$_x$ emissions from the SpaceXAI Colossus 2 power plant in Southaven, Mississippi. Using observations from the geostationary TEMPO satellite instrument, we detect a strong increase in local mean NO$_2$ column concentrations after the plant began operations in late 2025. We then use TEMPO to estimate two-week-average NO$_x$ source rates from August 2025 to mid-August 2026, calibrating against continuous emission monitoring system (CEMS) data from US power plants. TEMPO first detected NO$_x$ emissions in December 2025 at 460$\pm$180 kg h$^{-1}$. We find that emissions increased through August 2026, averaging 730$\pm$185 kg h$^{-1}$ after February 2026, roughly 16 times higher than expected from the facility's March 2026 permit for 41 turbines operating under best available control technology (BACT) requirements ($\sim$47 kg h$^{-1}$). Emissions at the expected level would be undetectable by our TEMPO analysis.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，AI 运维、负载预测和设施调优正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用仿真建模和情景分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断AI 工具是否能降低运维复杂度并提升可用性。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Kevin D. Gauld, Daniel J. Varon, Nicholas Balasus, 等. Quantifying AI data center nitrogen oxide (NO$_x$) emissions from space[J/OL]. (2026-08-23)[2026-08-29]. http://arxiv.org/abs/2608.22153v1.
Research Article算电协同
Chandan Chaudhary、Abanish Tiwari、Yansong Pei、Mohammed Ben-Idris、Joydeep Mitra
Published 2026-08-24 · arXiv · Credibility S
Artificial-intelligence data centers running bulk-synchronous training can impose sub-second power swings. When several facilities synchronize their training cycles, these load variations become spatially correlated and amplify the aggregate disturbance on the grid. A grid operator without access to data-center telemetry must infer this correlation from electrical measurements alone. However, the required observatio…
Abstract, interpretation and reference
Abstract
Artificial-intelligence data centers running bulk-synchronous training can impose sub-second power swings. When several facilities synchronize their training cycles, these load variations become spatially correlated and amplify the aggregate disturbance on the grid. A grid operator without access to data-center telemetry must infer this correlation from electrical measurements alone. However, the required observation time and the feasibility of detection on substation-deployable hardware remain uncharacterized. This paper develops a correlation-based detection method to classify the multi-facility operating regime from cross-facility power measurements. Analytical derivations and experimental validation show that the resulting detection confidence increases with the observation-window length at a rate governed by the load correlation time. The method is demonstrated in a real-time hardware-in-the-loop testbed, where load setpoints generated from a validated semi-Markov data-center load model are applied to an electromagnetic-transient grid simulation on a Real-Time Digital Simulator. A compact classifier built on pairwise power correlations runs on an edge device in this loop and determines whether the data-center load variations are independent or spatially correlated. The cross-facility correlation separates the independent and correlated cases across independent realizations. The held-out detection accuracy improves with the observation window, consistent with the predicted relation. A raw-waveform network fails to generalize, supporting pairwise correlation as the discriminative signal. The detector executes in real time on commodity edge hardware. A closed-loop demonstration against the running simulator tracks a regime change within one observation window.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用实验验证、原型测试或测量对比，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Chandan Chaudhary, Abanish Tiwari, Yansong Pei, 等. Real-Time Edge-based Detection of Correlated AI Data-Center Load Episodes[J/OL]. (2026-08-24)[2026-08-29]. http://arxiv.org/abs/2608.22719v1.
Research Article余热回收
Wenyu Liu、Enea Figini、Mario Paolone
Published 2026-08-17 · arXiv · Credibility S
This paper proposes a two-layer model predictive control (MPC) framework for the real-time operation of data centers integrated with on-site photovoltaic generation, battery energy storage, waste heat recovery, and district heating. The upper layer employs scenario-based stochastic optimization to jointly optimize intraday market participation, workload scheduling, and energy management under uncertainty. The lower …
Abstract, interpretation and reference
Abstract
This paper proposes a two-layer model predictive control (MPC) framework for the real-time operation of data centers integrated with on-site photovoltaic generation, battery energy storage, waste heat recovery, and district heating. The upper layer employs scenario-based stochastic optimization to jointly optimize intraday market participation, workload scheduling, and energy management under uncertainty. The lower layer adopts an adaptive tube-based MPC strategy that compensates short-term disturbances while tracking the dispatch references given by the upper layer. The framework further integrates multi-horizon forecasting to support real-time decision making. Microservice-based simulation studies under representative clear-sky and overcast operating conditions demonstrate that the proposed framework accurately tracks dispatch plans despite fast photovoltaic and workload fluctuations. Compared with single-layer control strategies, the adaptive lower-layer controller substantially reduces real-time dispatch deviations and the associated imbalance costs. In addition, the proposed framework naturally adapts to seasonal operating conditions and responds to carbon-aware operating signals, offering a practical approach for economically efficient, sustainable, and grid-supportive operation of future data centers.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，余热回收、热泵耦合和二次能源利用正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用建模优化、调度分析或算法评估，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断数据中心余热能否从成本项转化为能源资产。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Wenyu Liu, Enea Figini, Mario Paolone. Real-Time Control of Sustainable Data Centers: A Two-Layer Model Predictive Control Framework with Workload Flexibility and Heat Recovery[J/OL]. (2026-08-17)[2026-08-29]. http://arxiv.org/abs/2608.16432v1.
Research Article热管理与液冷
Cletus Ngwerume、Lang Tong、Chee-Wooi Ten、Yi Hu
Published 2026-07-30 · arXiv · Credibility S
Cooling
Abstract, interpretation and reference
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，液冷、热管理和数据中心能效正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用仿真建模和情景分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断液冷方案、热管理路线和高密度部署节奏。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Cletus Ngwerume, Lang Tong, Chee-Wooi Ten, 等. A Configurable Thermal-Dynamic Model for AI Data Center Cooling Load Simulation[J/OL]. (2026-07-30)[2026-08-29]. https://arxiv.org/abs/2607.28962.
Research Article算电协同
Eliseo Curcio
Published 2026-08-08 · arXiv · Credibility S
Interconnection queues, not electricity prices, now govern where data centers can be built, and the standard levelized-cost comparison answers a question no developer faces: it assumes a load profile, freezes the grid price while modeling the demand that moves it, and quotes busbar costs a facility cannot buy. This paper evaluates nine on-site supply technologies against a delivered grid whose price is endogenous to…
Abstract, interpretation and reference
Abstract
Interconnection queues, not electricity prices, now govern where data centers can be built, and the standard levelized-cost comparison answers a question no developer faces: it assumes a load profile, freezes the grid price while modeling the demand that moves it, and quotes busbar costs a facility cannot buy. This paper evaluates nine on-site supply technologies against a delivered grid whose price is endogenous to projected data-center demand, on a complete-site basis that retains standby charges, with measured GPU training load, delivered fuel prices, production-pathway carbon, and statutory 45V and 48E incentive mechanics. Nothing beats the wire: gas combined cycle produces at 47 USD/MWh but costs about 114 USD per megawatt-hour of complete site energy against a 92 USD grid; four-hour storage is physically capped near 18 percent of annual energy and, charged at the margin, dirtier than the grid; hydrogen from grid-priced power fails on cost and carbon together. An investment inversion converts these findings into capital terms: conversion-hardware learning buys nothing, because free hardware still exceeds the grid for every low-carbon arm, while global electrolyser deployment on sited sub-20 USD/MWh power brings PEM hydrogen power to about 2.2 times the grid at 300 billion USD and 1.9 times at 1 trillion USD (2.7 and 2.3 for the hydrogen engine), with a carbon reduction of roughly 85 percent (6.8-fold) against grid-power production. Grid parity is not purchasable at any budget. On-site supply is an access and depth product; most current investment targets the wrong term.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用仿真建模和情景分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Eliseo Curcio. Beyond the Grid: Cost, Carbon, and Capital Requirements of On-Site Power Technologies for AI Data Centers[J/OL]. (2026-08-08)[2026-08-29]. http://arxiv.org/abs/2608.08170v1.
Research Article热管理与液冷
Can Emre Koksal、Richard A. Barry、Artun Sel
Published 2026-08-13 · arXiv · Credibility S
The growing power demands and variability of AI
Abstract, interpretation and reference
Abstract
The growing power demands and variability of AI
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，液冷、热管理和数据中心能效正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用文献摘要中的模型、实验或案例分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向冷却效率、能源利用或运维策略的改进方向。意义：对日报读者而言，它可用于判断液冷方案、热管理路线和高密度部署节奏。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Can Emre Koksal, Richard A. Barry, Artun Sel. A Theory of Probabilistic Power Provisioning for Data Centers with Distributed Energy Storage[J/OL]. (2026-08-13)[2026-08-29]. https://arxiv.org/abs/2608.12993.
Research Article算电协同
Honglin Li、Buxin She、Jie Zhang
Published 2026-08-11 · arXiv · Credibility S
Hyperscale data centers are adding firm, high-utilization demand faster than grids can serve it, renewing interest in colocating them with small modular reactors. Such a plant could earn revenue in two ways, selling low-carbon power and diverting steam to absorption chillers that serve a cooling load accounting for 20-40% of facility electricity use, but neither revenue stream has been priced across the conditions t…
Abstract, interpretation and reference
Abstract
Hyperscale data centers are adding firm, high-utilization demand faster than grids can serve it, renewing interest in colocating them with small modular reactors. Such a plant could earn revenue in two ways, selling low-carbon power and diverting steam to absorption chillers that serve a cooling load accounting for 20-40% of facility electricity use, but neither revenue stream has been priced across the conditions that must coincide. Here we co-optimize reactor dispatch, steam extraction, absorption cooling and grid exchange hourly for a 200 MW$_\mathrm{e}$ data center in the Electric Reliability Council of Texas (ERCOT) region, across 109 runs spanning capital, market, policy, financing and cooling efficiency. At 2023 mid-range reactor capital, the nuclear configurations cost 49-62% more than grid supply even with the Section 45Y production tax credit. The viable region opens near \$5,000 kW$_\mathrm{e}^{-1}$, and nth-of-a-kind capital makes them 77-89% cheaper in 2023, though between parity and 34% more expensive in the low-price 2024 market. A carbon price of \$53-64 tCO$_2^{-1}$ closes the mid-range gap under hourly export crediting. Absorption cooling is dispatched in response to hourly electricity prices and supplies 38% of annual cooling, at an added cost of \$9.2 million yr$^{-1}$ relative to the reactor-only plant; that gap closes at an installed absorption cost of \$60 kW$_\mathrm{c}^{-1}$ at baseline efficiency and \$570 kW$_\mathrm{c}^{-1}$ on a legacy-efficiency campus, against surveyed commercial prices of \$450-1,200 kW$_\mathrm{c}^{-1}$. Together these results delineate the capital, market and policy conditions under which colocated reactor cogeneration is competitive with grid procurement, and the range over which each condition moves the outcome.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用综述归纳和指标比较，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Honglin Li, Buxin She, Jie Zhang. Techno-Economic Boundary Analysis of Small Modular Reactor Cogeneration for Hyperscale Data Center IT and Cooling Loads[J/OL]. (2026-08-11)[2026-08-29]. http://arxiv.org/abs/2608.10999v1.
Research Article算电协同
Saroj Khanal、Geon Roh、Boyu Yao、Abraham Silverman、Dennice Gayme、Charalambos Konstantinou、Jip Kim、Yury Dvorkin
Published 2026-08-20 · arXiv · Credibility S
Data-center growth risks overbuilding power grid infrastructure and stranding capital. Flexible data-center operation can defer infrastructure investments, but its value depends on the flexibility mechanism and the host power grid characteristics. We classify data-center load as firm, flexible or interruptible, and embed them in capacity expansion applied to market-organized, fossil-heavy PJM and carbon-capped, cent…
Abstract, interpretation and reference
Abstract
Data-center growth risks overbuilding power grid infrastructure and stranding capital. Flexible data-center operation can defer infrastructure investments, but its value depends on the flexibility mechanism and the host power grid characteristics. We classify data-center load as firm, flexible or interruptible, and embed them in capacity expansion applied to market-organized, fossil-heavy PJM and carbon-capped, centrally coordinated Korea. In PJM, the flexibility value is spatial: shifting workloads between zones reduces system cost by 6% in 2028 and 19% in 2038, avoiding 4.4 GW and 8.9 GW of gas and nuclear generation. In Korea, it is temporal: shifting load into midday solar hours makes 0.5 GW of additional solar worth building in 2028 and avoids 1.2 GW of gas and 0.3 GW of batteries in 2038. In both, realistic event-shape limits diminish the value of curtailment. The results show that flexibility procurement and its value are driven by grid characteristics and policy objectives.
中文解读
背景：AI 数据中心负载、功率密度和能源约束同步上升，算力负载与电网侧资源的协同调度正在成为智算中心设计的关键变量。问题：论文聚焦现有方案在效率、可靠性或工程协同上的瓶颈。方法：摘要显示作者采用文献摘要中的模型、实验或案例分析，把运行负载、冷却/能源系统和基础设施约束放在同一分析框架中。结果：研究重点指向AI 负载波动对电网设备寿命和调频边界的影响。意义：对日报读者而言，它可用于判断智算中心建设是否受电网容量、负载波动和调度机制约束。仍需结合全文实验条件、样本范围和成本假设核验。
参考文献
Saroj Khanal, Geon Roh, Boyu Yao, 等. Shift or curtail? How much data-center flexibility is worth depends on the host power grid[J/OL]. (2026-08-20)[2026-08-29]. http://arxiv.org/abs/2608.19622v1.
