智算中心论文专站

A Review on Source-Grid-Load-Storage Coordinated Planning and Operation Optimization for Carbon-Neutral Data Centers

Yiming Lu

Published 2026-08-12 · Semantic Scholar · Credibility S

The energy consumption of data centers continues to rise, currently accounting for around 1% of global electricity use, with the majority of carbon emissions originating from server operations and cooling systems. This paper reviews mechanisms and technologies for carbon-neutral data centers, with a particular focus on the integrated architecture of "Source-Grid-Load-Storage." Through a synthesis of relevant literat…

Abstract, interpretation and reference

Abstract

The energy consumption of data centers continues to rise, currently accounting for around 1% of global electricity use, with the majority of carbon emissions originating from server operations and cooling systems. This paper reviews mechanisms and technologies for carbon-neutral data centers, with a particular focus on the integrated architecture of "Source-Grid-Load-Storage." Through a synthesis of relevant literature, it formulates carbon emission limits and evaluation metrics, including Scope 1-3 emissions and Carbon Usage Effectiveness (CUE). On this basis, a unified analysis framework is built that links low-carbon planning, such as site selection optimization, multi-site integration, efficient designs, liquid cooling technologies, and renewable-energy-adaptive capacity setups, with operational optimization, including mixed clean energy scheduling, electricity-carbon coordination, cascaded waste-heat recovery, carbon-aware workload shifting, AI-driven energy management, and multi-type energy storage coordination. The results indicate that synergy between geographically distributed computing flexibility and dynamic carbon signals can greatly reduce emissions and costs; however, full-life-cycle carbon management and privacy-preserving distributed optimization still lack sufficiently in-depth treatment.

The Artificial Intelligence–Power Grid Nexus for Sustainability: AI–Power Grid Sustainability Nexus: Challenges and Path Forward

C. I. Nwakanma, A. Srivastava

Published 2026-08-01 · Semantic Scholar · Credibility S

Artificial intelligence (AI) has shown significant promise in improving power grid sustainability; however, a co-evolutionary framework is needed for sustainable AI and sustainable grid operation. There is a need for “Grid friendly AI', that guarantees a sustainable power grid. AI is expected to increasingly rely on resilient and reliable energy infrastructure, while simultaneously serving as a critical enabler for …

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Abstract

Artificial intelligence (AI) has shown significant promise in improving power grid sustainability; however, a co-evolutionary framework is needed for sustainable AI and sustainable grid operation. There is a need for “Grid friendly AI', that guarantees a sustainable power grid. AI is expected to increasingly rely on resilient and reliable energy infrastructure, while simultaneously serving as a critical enabler for the optimization, predictive control, and decarbonization of sustainable power grids. AI technologies are being leveraged to improve grid flexibility, enhance forecasting accuracy, enable real-time decision-making for resiliency, and support greater integration of distributed energy resources. Conversely, the exponential growth of AI workloads, particularly large-scale training and inference, poses escalating energy and carbon demands, necessitating the development of cleaner, smarter, and more adaptive power infrastructures to sustain digital systems. This article explores the nexus between AI and the power grid through the lens of sustainability. We first examine how AI can potentially help accelerate the transition to clean energy, enabling greater penetration of renewable and sustainable energy, improved demand-side flexibility, and more efficient grid operations. We then investigate the increasing energy footprint of AI itself and propose strategies to power data centers and compute-intensive workloads with sustainable electricity. We asked the question- “What can the AI-power grid community do at the intersection of AI and power to make things more sustainable?". These include geographically aligning AI infrastructure with renewable generation, leveraging edge and federated AI to reduce energy consumption, and employing carbon-sensitive workload scheduling. In addition, we discuss pressing challenges at this nexus, including limited data accessibility, lack of interoperability standards, model explainability requirements, and evolving regulatory frameworks. To address these, we present a forward-looking architecture for aligning AI development with power grid evolution, grounded in co-design principles, energy-aware AI practices, sustainable digital infrastructure, and collaborative policy making. Achieving a sustainable, intelligent future will depend on viewing AI and power systems not as isolated domains, but as dynamically co-evolving systems, wherein AI contributes to grid decarbonization, and the grid, in turn, enables environmentally responsible AI. However, AI needs to become more efficient - it is not just about allowing unbridled growth of AI, but of Green AI.

A Theory of Probabilistic Power Provisioning for Data Centers with Distributed Energy Storage

Can Emre Koksal, Richard A. Barry, Artun Sel

Published 2026-08-13 · arXiv · Credibility S

The growing power demands and variability of AI workloads make electrical power delivery a critical constraint in data-center operation. Distributed energy storage can reduce the power capacity required to support stochastic loads, but its benefits depend fundamentally on the statistics and time scales of demand. This paper develops a probabilistic framework that jointly characterizes provisioned power, energy-stora…

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Abstract

The growing power demands and variability of AI workloads make electrical power delivery a critical constraint in data-center operation. Distributed energy storage can reduce the power capacity required to support stochastic loads, but its benefits depend fundamentally on the statistics and time scales of demand. This paper develops a probabilistic framework that jointly characterizes provisioned power, energy-storage capacity, and the probability of overdraw. We show that storage-assisted provisioning separates into two operating regimes. In the Small Battery Region, overdraw is dominated by short-lived demand excursions and storage provides nearly linear reductions in the required power margin. In the Large Battery Region, overdraw results from sustained demand fluctuations over longer spans of time, and the required margin exhibits diminishing returns with storage. For this regime we introduce effective power, an analogue of effective bandwidth that captures the temporal statistics of the demand and gives an asymptotically tight characterization of the required power. We further quantify how temporal correlation and spatial aggregation affect storage requirements and statistical multiplexing gains, and extend the analysis to loads with multiple demand time scales. Finally, we evaluate the framework using power-demand traces from three production data centers spanning HPC, GPU-training, and cloud-service workloads. Despite their heterogeneous, cyclo-stationary and multi-modal behavior, the measured workloads exhibit the predicted regimes, and a simple four-parameter two-state model captures the dynamics governing their storage-power tradeoffs. The resulting framework provides both a probabilistic foundation and practical dimensioning principles for storage-assisted power provisioning in next-generation AI data centers.

InFactPlanner: Planning Sustainable Geo-Distributed LLM Data Centers

Nicoletta Tsiopani, Moysis Symeonides, George Pallis, Marios D. Dikaiakos

Published 2026-08-13 · arXiv · Credibility S

The rapid growth of LLM inference is shifting sustainability concerns from one-time training to continuous serving, where infrastructure decisions shape energy use, carbon emissions, water consumption, and service quality. Yet operators often need to compare deployment alternatives before large-scale infrastructure is built, making direct measurement costly, slow, and sometimes infeasible. We present InFactPlanner, …

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Abstract

The rapid growth of LLM inference is shifting sustainability concerns from one-time training to continuous serving, where infrastructure decisions shape energy use, carbon emissions, water consumption, and service quality. Yet operators often need to compare deployment alternatives before large-scale infrastructure is built, making direct measurement costly, slow, and sometimes infeasible. We present InFactPlanner, a trace-driven decision-support framework for what-if analysis of sustainable AI data center deployment for LLM inference across single and geo-distributed sites. InFactPlanner combines query traces, hardware-model profiles, candidate site configurations, PUE/WUE parameters, renewable generation models, and time-varying grid carbon intensity to estimate power, energy, carbon emissions, water use, latency, and server utilization. The framework abstracts low-level serving effects into configurable hardware-model profiles, enabling rapid comparison of site selection, capacity placement, hardware, model, renewable integration, and routing choices. We validate the energy accounting pipeline by reproducing reference LLM inference energy estimates with less than 10% deviation, evaluate scalability across multiple data centers and server counts, and demonstrate scenario-driven decision analyses for hardware selection, renewable placement, geographic deployment, and carbon-aware routing. Our results show that sustainability-optimal choices can differ from latency-optimal ones, and that the carbon value of deployment depends strongly on the local grid mix.

Environmental and Economic Implications of Artificial Intelligence Data Centers in the United States

Johanna Bolaños-Zuñiga, Alberto J. Lamadrid

Published 2026-08-10 · arXiv · Credibility S

In this study, we use electricity demand growth, cooling requirements, and backup system operation to evaluate the environmental and economic implications of artificial intelligence data centers in the United States. Our results indicate that impacts are not determined solely by facility design, but by the broader electricity, water, and land-use systems in which these facilities operate. Emissions are primarily dri…

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Abstract

In this study, we use electricity demand growth, cooling requirements, and backup system operation to evaluate the environmental and economic implications of artificial intelligence data centers in the United States. Our results indicate that impacts are not determined solely by facility design, but by the broader electricity, water, and land-use systems in which these facilities operate. Emissions are primarily driven by electricity consumption and therefore depend on marginal generation mixes, transmission constraints, and the spatial and temporal distribution of demand. Analysis further shows that local effects include pressures on water resources, increased noise exposure, and land-use changes, with outcomes varying across regions and infrastructure conditions. The assessment of technological and operational measures shows that improvements in energy efficiency, cooling configurations, and operational strategies can reduce these impacts, although their effectiveness depends on system-level conditions. Evaluation of regulatory and market structures suggests that existing frameworks may not fully account for location- and time-specific externalities. These findings support the need for integrated policy approaches that align data center deployment and operation with electricity system characteristics, water availability, and land-use planning to improve overall environmental and economic performance.

Techno-Economic Boundary Analysis of Small Modular Reactor Cogeneration for Hyperscale Data Center IT and Cooling Loads

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…

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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.

AIDC Microgrid Vulnerability Assessment Under Computing-Power Coordinated Attacks

Ze Yu, Hongwei Zhen, Chao Shen, Mingyang Sun

Published 2026-08-11 · arXiv · Credibility S

The rapid growth of large language model (LLM) services is accelerating the expansion of AI data centers (AIDCs), intensifying concerns over power system resource adequacy and rising carbon emissions. The integration of renewable energy provides a pathway toward addressing these pressures, but it also introduces new cross-domain stability challenges to low-carbon AIDCs. For example, variability in renewable generati…

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Abstract

The rapid growth of large language model (LLM) services is accelerating the expansion of AI data centers (AIDCs), intensifying concerns over power system resource adequacy and rising carbon emissions. The integration of renewable energy provides a pathway toward addressing these pressures, but it also introduces new cross-domain stability challenges to low-carbon AIDCs. For example, variability in renewable generation affects reliability on the supply side, whereas fluctuations in AIDC workloads affect reliability on the demand side, jointly creating interconnected stability risks in AIDC microgrids. To address this problem, this paper is the first to explore computing-power coordinated attacks against low-carbon AIDCs. First, we propose an uncertainty-aware AIDC microgrid vulnerability assessment framework to capture two interacting attack surfaces: inverter control parameter tampering attacks, and AI-induced demand manipulation attacks. Then, accounting for renewable-side forecast uncertainty and AIDC-side demand response uncertainty, we introduce confidence-weighted realizations and construct a long-term attack reachable domain analysis. Furthermore, an impedance-based screening method is utilised to map generation and load variations to erosion of stability margin, thereby identifying vulnerable attack time windows and attack vectors. In addition, case studies show that computing-power coordinated attacks induce sustained inverter frequency excursions exceeding 20% of the nominal value and reach instability conditions unattainable by single attacks. The results also demonstrate that the proposed framework can extract sparse, high-confidence vulnerable periods from long-term operating trajectories.

Towards Terabit/$λ$/s Multidimensional Silicon Photonic Engine

Hao Chen, Zengqi Chen, Wu Zhou, Kaihang Lu, Mingyuan Zhang, Yuxiang Yin, Yiou Cui, Chaoran Huang, Pui-In Mak, Yeyu Tong

Published 2026-08-12 · arXiv · Credibility S

Increasing artificial intelligence (AI) workloads drive co-packaged optics (CPO), which integrates optical engines with electronic components. Optical interconnects can extend transmission distances and reduce latency, allowing distributed clusters in AI factories to operate as a unified computational unit. However, escalating data throughput necessitates greater parallelization of light within ultracompact form fac…

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Abstract

Increasing artificial intelligence (AI) workloads drive co-packaged optics (CPO), which integrates optical engines with electronic components. Optical interconnects can extend transmission distances and reduce latency, allowing distributed clusters in AI factories to operate as a unified computational unit. However, escalating data throughput necessitates greater parallelization of light within ultracompact form factors while maintaining stringent energy efficiency and latency constraints. Here, we present a multidimensional silicon photonic engine that achieves a communication capacity exceeding 1.8 terabit/s/lambda/s. By monolithically integrating transceivers, spatial and polarization (de)multiplexers, and optical signal processors on a single chip, we eliminate bulky discrete (de)multiplexers and power-hungry digital signal processing (DSP). In experiments, the photonic engine can be self-configured to identify two, four, or six concurrent spatial and polarization channels per fiber while mitigating dynamic channel crosstalk. Compared with the state-of-art DSP, our approach achieves >5,000-fold reductions in both power consumption and processing latency at a MIMO processing order of six. Furthermore, we demonstrate full-duplex, modulation-format-transparent inter-chip communication over 300-meter fiber. These results represent a paradigm shift for optical engines in future high-performance computing and AI-driven data centers.

Multi-Objective Optimization for Data Center HVAC Systems Based on Edge–Cloud Collaborative Deep Reinforcement Learning

Shichao Huang, Yibing Zhou, Yuan Liu

Published 2026-08-07 · Semantic Scholar · Credibility S

The sustained growth of cloud computing and AI training workloads drives data center expansion. Optimizing their control is therefore critical for reducing operational costs. Edge real-time control is indispensable for guaranteeing thermal safety, data sovereignty, and offline availability. Yet deploying Deep Reinforcement Learning (DRL) in production Heating, Ventilation, and Air Conditioning (HVAC) environments co…

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Abstract

The sustained growth of cloud computing and AI training workloads drives data center expansion. Optimizing their control is therefore critical for reducing operational costs. Edge real-time control is indispensable for guaranteeing thermal safety, data sovereignty, and offline availability. Yet deploying Deep Reinforcement Learning (DRL) in production Heating, Ventilation, and Air Conditioning (HVAC) environments confronts cold-start risks, edge–cloud computational asymmetry, and multi-objective conflicts spanning energy efficiency, electricity cost, and thermal safety. To address these challenges, this paper proposes an edge-cloud collaborative physics-informed reinforcement learning framework for production data center HVAC control. The framework integrates a physics-informed cold-start solution using Adaptive Particle Swarm Optimization (APSO) to generate physically constrained initial policies on a gray-box digital twin without expert demonstration data, a three-time-scale edge–cloud architecture coordinating minute-level edge Soft Actor-Critic (SAC) real-time inference, weekly edge APSO online model identification, daily cloud Non-dominated Sorting Genetic Algorithm III (NSGA-III) thermal storage scheduling, and a constraint-aware safe projection layer that embeds thermal safety hard constraints directly into the neural network policy. The framework is validated through a seven-month production deployment spanning the complete summer-to-winter transition, comprising approximately 3.2 million sensor records and evaluated with rigorous statistical methods.

Beyond the Grid: Cost, Carbon, and Capital Requirements of On-Site Power Technologies for AI Data Centers

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…

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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.

ClusterBench: A Framework for Cluster-Wide Continuous Benchmarking and Regression Testing

Aditya Ujeniya, Jan Eitzinger, Thomas Gruber, Georg Hager, Gerhard Wellein

Published 2026-08-11 · arXiv · Credibility S

Data centers need tooling that validates an entire installation rather than individual nodes, at acceptance and at regular intervals thereafter. This requires dispatching identical benchmarks to every node in a single submission, and therefore cluster-aware scheduling. This paper presents ClusterBench, a framework for cluster-wide continuous benchmarking. It ships with a benchmark collection targeting each component…

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Abstract

Data centers need tooling that validates an entire installation rather than individual nodes, at acceptance and at regular intervals thereafter. This requires dispatching identical benchmarks to every node in a single submission, and therefore cluster-aware scheduling. This paper presents ClusterBench, a framework for cluster-wide continuous benchmarking. It ships with a benchmark collection targeting each component: CPU, GPU, memory, interconnect, and I/O. Because measurements are repeated throughout the cluster's lifetime, ClusterBench collects data across space and time. Comparison against earlier runs detects performance regressions introduced by software changes, such as kernel updates or new library versions. The measurements also form a dataset for research on hardware variability. On the NHR@FAU clusters Helma, Alex, and Fritz, variation within a single component stays within 1%. Variation across specimens reaches 5%, despite nodes identical by specification. Correlating performance with power draw, frequency, and temperature shows that this relationship differs between air- and liquid-cooled nodes.

Advanced AI Chip Deployment Strategies for Scalable Qualcomm-Powered Telecom and Hyperscale Data Center Ecosystems

Joshua Asogwa

Published 2026-07-28 · Semantic Scholar · Credibility S

The rapid advancement of digital transformation is reshaping global industries through the convergence of artificial intelligence (AI), cloud computing, high-performance computing, the Internet of Things (IoT), and next-generation communication networks. These technological developments are driving unprecedented growth in data generation, computational demand, and intelligent service delivery, placing increasing pre…

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Abstract

The rapid advancement of digital transformation is reshaping global industries through the convergence of artificial intelligence (AI), cloud computing, high-performance computing, the Internet of Things (IoT), and next-generation communication networks. These technological developments are driving unprecedented growth in data generation, computational demand, and intelligent service delivery, placing increasing pressure on telecommunications infrastructures and hyperscale data centres to provide scalable, energy-efficient, secure, and low-latency computing environments. As AI-driven applications, including generative AI, autonomous network management, digital twins, and real-time analytics, become integral to modern digital ecosystems, conventional computing architectures are increasingly unable to meet the performance, scalability, and sustainability requirements of these workloads. Consequently, specialised AI chip technologies have emerged as critical enablers of intelligent infrastructure capable of accelerating AI processing while optimising energy consumption and operational efficiency. Among these technologies, Qualcomm-powered AI platforms offer significant advantages through heterogeneous computing architectures, integrated AI accelerators, advanced connectivity, and edge-to-cloud processing capabilities. Nevertheless, the large-scale deployment of Qualcomm AI chips across telecom and hyperscale data centre ecosystems remains constrained by challenges relating to workload orchestration, infrastructure interoperability, cybersecurity, thermal management, resource allocation, and energy optimisation. This study proposes advanced AI chip deployment strategies that integrate intelligent workload scheduling, AI-driven resource orchestration, software-defined infrastructure, predictive infrastructure management, and security-by-design principles into a unified deployment framework. The proposed framework enhances computational efficiency, infrastructure resilience, service scalability, and operational sustainability while supporting next-generation telecommunications and hyperscale computing ecosystems capable of meeting the evolving demands of an increasingly AI-driven digital economy.

AI-driven optimization of thermoelectric waste heat recovery systems for sustainable and secure data center operations

Fan Liao

Published 2026-07-31 · Semantic Scholar · Credibility S

暂无可展示摘要。

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Abstract

暂无可展示摘要。

GREEN COMPUTING: ENERGY-EFFICIENT ALGORITHMS AND DATA CENTERS

Shankar Kumar

Published 2026-07-16 · Semantic Scholar · Credibility S

The exponential growth of data center operations and cloud computing infrastructure has resulted in unprecedented energy consumption, contributing significantly to global carbon emissions and environmental degradation. This paper presents a comprehensive investigation into energy-efficient algorithms and sustainable data center architectures as critical components of green computing. Existing energy optimization app…

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Abstract

The exponential growth of data center operations and cloud computing infrastructure has resulted in unprecedented energy consumption, contributing significantly to global carbon emissions and environmental degradation. This paper presents a comprehensive investigation into energy-efficient algorithms and sustainable data center architectures as critical components of green computing. Existing energy optimization approaches including Dynamic Voltage and Frequency Scaling (DVFS), virtualization technologies, AI-driven workload distribution, and advanced cooling systems are analyzed in relation to their role in reducing data center power demand. A conceptual Energy-Aware Data Processing (EADP) algorithm is presented by integrating data management, task scheduling, and hardware optimization techniques derived from current literature. Simulated comparative results indicate meaningful reductions in energy consumption, improvements in processing time, and better Power Usage Effectiveness (PUE) and Carbon Usage Effectiveness (CUE) values under an energy-aware operating model. The study argues that energy-efficient algorithms combined with sustainable infrastructure practices provide a viable pathway toward environmentally responsible digital transformation.[1][2][3][4][5][6][7][8] Keywords: green computing; energy-efficient algorithms; data centers; DVFS; PUE; sustainable computing; renewable energy

Predictive Failure Detection in Network Hardware Using Thermal Imaging and Deep Learning with Sensor Fusion

Ashly Joseph

Published 2026-08-05 · arXiv · Credibility S

Unplanned network hardware malfunctions can interrupt services and result in expensive downtime in data centers. A deep learning-based predictive maintenance strategy is presented that utilizes thermal imaging and power sensor data to detect early indicators of equipment breakdown in routers, switches, and servers. A simulated dataset was generated comprising annotated thermal pictures and power readings indicative …

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Abstract

Unplanned network hardware malfunctions can interrupt services and result in expensive downtime in data centers. A deep learning-based predictive maintenance strategy is presented that utilizes thermal imaging and power sensor data to detect early indicators of equipment breakdown in routers, switches, and servers. A simulated dataset was generated comprising annotated thermal pictures and power readings indicative of three operating states: Normal, Warning, and Critical. Three ImageNet-pretrained convolutional neural network (CNN) models ResNet-50, InceptionV3, and VGG16 were assessed together with a multi-modal CNN-LSTM fusion model that integrates visual and sensor time-series information. Experiments were performed with and without pre-processing procedures, including region-of-interest (ROI) extraction and normalization. In the absence of pre-processing, CNNs attained moderate accuracy (e.g., ResNet-50 at 52%), but ROI-based pre-processing significantly enhanced performance (ResNet-50 accuracy reaching 91%). The CNN-LSTM model attained the greatest accuracy of 94%, with precision and recall approaching 95%, illustrating the effectiveness of multi-modal fusion. The results validate that domain-specific pre-processing and sensor fusion substantially improve early failure prediction, providing a potential foundation for proactive maintenance of network hardware through non-intrusive monitoring.

A Phased Development Framework Enabling Islanded Operation of Sustainable AI Data Centers With Onsite Grid-Following and Grid-Forming Energy Architectures

Soham Ghosh, Nabil Mohammed, Mohammad Ashraf Hossain Sadi

Published 2026-07-19 · arXiv · Credibility S

As hyperscale and colocation AI data centers continue to expand, the electric grid is increasingly required to support large, concentrated loads, with individual facilities ranging from 500 MW to 2 GW. Current projections estimate that approximately 50 GW of AI data center capacity will require grid connectivity in the United States by 2030. While prior research has extensively examined the environmental and operati…

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Abstract

As hyperscale and colocation AI data centers continue to expand, the electric grid is increasingly required to support large, concentrated loads, with individual facilities ranging from 500 MW to 2 GW. Current projections estimate that approximately 50 GW of AI data center capacity will require grid connectivity in the United States by 2030. While prior research has extensively examined the environmental and operational impacts of AI data centers, as well as their potential role as grid-interactive assets, limited attention has been given to the challenges associated with their scalable deployment through engineering, procurement, and construction (EPC) processes. This manuscript addresses this gap by proposing a phased development framework for AI data center expansion. The approach is designed to enable developers to meet aggressive time-to-market objectives while navigating multi-year constraints associated with interconnection approvals and lead times associated with the procurement of component equipment. A modular construction architecture is presented, along with a detailed analysis of integrated energy systems and the role of hybrid on-site generation in supporting incremental capacity growth. Electromagnetic transient simulations (EMT) are used to evaluate system performance, demonstrating that a combination of on-site natural gas generation and grid-forming energy storage can reliably support data center operations during early and intermediate deployment phases. The study further examines the transition to full grid interconnection, including the capability of the data center to operate in islanded mode during grid disturbances. Finally, the manuscript compares grid-forming control strategies for system reconnection and restoration under varying conditions.

Zero-change foundry compatible silicon photonics MEMS optical switch

Arkadev Roy, Daniel Klawson, Jianheng Luo, Yiyang Zhi, Sirui Tang, Ming Wu

Published 2026-08-04 · arXiv · Credibility S

Large-scale photonic switches are emerging as essential devices for energy-efficient optical interconnect in data centers and AI/ML clusters as a key enabler for high-bandwidth and low-latency connectivity. Combining micro-electro-mechanical (MEMS) based mechanical reconfigurability with silicon photonic integrated circuits can enable a large-scale, low-loss, programmable platform required for large-scale optical ci…

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Abstract

Large-scale photonic switches are emerging as essential devices for energy-efficient optical interconnect in data centers and AI/ML clusters as a key enabler for high-bandwidth and low-latency connectivity. Combining micro-electro-mechanical (MEMS) based mechanical reconfigurability with silicon photonic integrated circuits can enable a large-scale, low-loss, programmable platform required for large-scale optical circuit switches. We demonstrate a broadband silicon photonics MEMS switch with more than 30 dB extinction ratio operating in C-band using a zero-change foundry-compatible process and Back-end-of-Line (BEOL) post-processing. The optical switch element exhibits an insertion loss of less than 1.5 dB with a low static power consumption of approx 20 nW at maximum actuation voltage. Our results illustrate that MEMS-based silicon photonics modulators and phase shifters can be used alongside standard silicon photonics components seamlessly in scenarios where performance in terms of footprint, extinction ratio, broad bandwidth, and low-loss operation is of paramount importance.

Assessing Risks of Hydro-Generator Shaft Fatigue from Data Center Load Oscillations

Kaustav Chatterjee, Meghana Ramesh, Shuchismita Biswas, Brett A. Ross, Antos C. Varghese, Sameer Nekkalapu, Slaven Kincic

Published 2026-07-15 · arXiv · Credibility S

Large AI data center loads can introduce persistent sub-synchronous active-power oscillations that may impact nearby generators by exciting torsional modes and increasing shaft stress. This paper presents a model-based framework for evaluating hydro-generator shaft fatigue risk under oscillatory loading. An electromagnetic transient simulation model is developed using a two-mass turbine-generator shaft representatio…

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Abstract

Large AI data center loads can introduce persistent sub-synchronous active-power oscillations that may impact nearby generators by exciting torsional modes and increasing shaft stress. This paper presents a model-based framework for evaluating hydro-generator shaft fatigue risk under oscillatory loading. An electromagnetic transient simulation model is developed using a two-mass turbine-generator shaft representation with parameters from real-world generation units and a configurable AI data center load. The risk assessment is performed in two stages. First, a network transfer function quantifies the propagation of load oscillations from the data center point of interconnection to the hydro-generator terminal. A plant transfer function then characterizes the resulting shaft torque amplification. A frequency-scan approach identifies resonance regions and evaluates torque amplification at individual forcing frequencies. Parametric studies show that amplification is strongly affected by generator-to-turbine inertia ratio and torsional damping. Lower inertia ratios shift torsional modes to lower frequencies and increase amplification, indicating that some Kaplan-type units may be more susceptible than comparable Francis or Pelton units. Reduced damping further increases resonant response and fatigue exposure. A simplified fatigue assessment based on S--N curves and the Goodman diagram relates simulated torque response to mechanical integrity. The resulting Goodman safety factor provides a practical metric for evaluating the impact of persistent AI data center oscillations on hydro-generator service life and supports interconnection studies, oscillation limits, and plant-level monitoring strategies.

A Stackelberg-Bayesian Capacity-Market Game of Carbon Regulation and Second-Life Battery Investment under AI Data-Center Load Growth

Rouzbeh Haghighi, Ali Hassan, Sina Mohammadi, Marcus Chen I Wada, Wencong Su

Published 2026-08-04 · arXiv · Credibility S

Artificial intelligence (AI) data centers are driving rapid electricity load growth across all U.S. ISO/RTO regions, raising both system costs and carbon exposure. This study develops a three-level Stackelberg--Bayesian game in which a regulator (leader) sets carbon penalties and subsidies, a single ISO capacity market clears against an energy balance modeled as a classical generation-expansion problem, and technolo…

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Abstract

Artificial intelligence (AI) data centers are driving rapid electricity load growth across all U.S. ISO/RTO regions, raising both system costs and carbon exposure. This study develops a three-level Stackelberg--Bayesian game in which a regulator (leader) sets carbon penalties and subsidies, a single ISO capacity market clears against an energy balance modeled as a classical generation-expansion problem, and technology-specific investors (followers) decide capacity and operation under incomplete information, yielding a Bayesian Nash equilibrium. The AI impact is captured parsimoniously as an additional load-growth factor on a greenfield-incremental expansion, isolating how much new capacity the growth pulls in and which technology fills it. Within this framework, we consider second-life battery (SLB) storage competing against new/first-life storage for capacity-market revenue. We quantify how a carbon tax, a renewable subsidy, and an SLB subsidy reshape the equilibrium investment mix, carbon emissions, and profit. Different scenarios are compared at the end based on cost-effectiveness and reduced carbon emissions.

The Cost and Network Limits of Space-Based AI Compute

Kees van Berkel

Published 2026-07-15 · arXiv · Credibility S

This paper evaluates whether large-scale AI data centers deployed in low-Earth orbit (LEO) could become a cost-effective alternative to terrestrial facilities. The analysis compares orbital and ground-based systems across launch cost, power generation, cooling, radiation exposure, and atmospheric reentry, as well as compute-network performance. A key distinction is the shift from terrestrial Clos networks to space-b…

Abstract, interpretation and reference

Abstract

This paper evaluates whether large-scale AI data centers deployed in low-Earth orbit (LEO) could become a cost-effective alternative to terrestrial facilities. The analysis compares orbital and ground-based systems across launch cost, power generation, cooling, radiation exposure, and atmospheric reentry, as well as compute-network performance. A key distinction is the shift from terrestrial Clos networks to space-based mesh networks using laser inter-satellite links. Using bisection bandwidth, bisection intensity, and roofline-style models, we show that while LEO-based inference may be feasible, training frontier-scale LLMs in orbit is unlikely to be competitive with terrestrial data centers.

The Environmental Cost of Digital Sovereignty: Water, Energy, and Emissions Impacts of Sovereign AI Infrastructure in the Global South

Muntaser Syed, Marius C. Silaghi, Sheikh Abujar, Sharun Akter Khushbu, Amal El Ahmad

Published 2026-07-15 · arXiv · Credibility S

Sovereign AI has become a strategic priority across the Global South, with over \$200 billion in state-led commitments announced between 2024 and 2026. Yet the physical infrastructure that compute sovereignty demands, above all data centers, imposes water, energy, and carbon costs that fall hardest on countries least equipped to absorb them. This paper presents a comparative environmental stress analysis across four…

Abstract, interpretation and reference

Abstract

Sovereign AI has become a strategic priority across the Global South, with over \$200 billion in state-led commitments announced between 2024 and 2026. Yet the physical infrastructure that compute sovereignty demands, above all data centers, imposes water, energy, and carbon costs that fall hardest on countries least equipped to absorb them. This paper presents a comparative environmental stress analysis across four cases: the United Arab Emirates, Bangladesh, India, and Africa (with a focus on Kenya). Using publicly available water stress data, grid carbon intensity factors, and GPU power specifications, we model the water consumption, energy demand, and carbon emissions of hypothetical sovereign AI deployments under multiple cooling technology scenarios. We find that a 1,024-GPU cluster using evaporative cooling in the UAE would consume over 30 million liters of water annually in a country classified as ``extremely high'' water stress. In Bangladesh, sovereign AI policy documents call for centralized GPU procurement but do not address where to site data centers in a country where more than a fifth of the land floods in an average year and the power grid struggles to deliver reliable supply. We identify a sovereignty-sustainability trilemma in which no country can simultaneously maximize AI sovereignty, minimize environmental impact, and maintain affordable resource access for citizens. We propose design principles for environmentally responsible sovereign AI, including mandatory water usage effectiveness reporting, climate-vulnerability siting assessments, and a preference for frugal small language models over frontier pre-training in resource-constrained settings.

Emission-Forecasting-Based Spatial-Temporal Carbon Response: A Multi-Agent Attention-Enhanced Deep Learning Framework

Feiyu Cai, Jing Qiu, Yi Yang, Chenxi Zhang, Xinlei Wang, Baichuan Liu, Junhua Zhao

Published 2026-07-29 · arXiv · Credibility S

As a major contributor to carbon emissions, the decarbonization of power systems has garnered significant societal attention. Nodal carbon intensity (NCI), a critical factor in carbon-oriented demand response, has traditionally been determined through ex-post calculations. However, this ex-post approach introduces latency in low-carbon dispatch. To address this, this paper presents a proactive ex-ante spatial-tempor…

Abstract, interpretation and reference

Abstract

As a major contributor to carbon emissions, the decarbonization of power systems has garnered significant societal attention. Nodal carbon intensity (NCI), a critical factor in carbon-oriented demand response, has traditionally been determined through ex-post calculations. However, this ex-post approach introduces latency in low-carbon dispatch. To address this, this paper presents a proactive ex-ante spatial-temporal carbon response framework. At its core, we develop a novel deep learning-based hierarchical design, enhanced by a dual-stage attention mechanism and a large language model (LLM)-based multi-agent cooperation system, to accurately forecast day-ahead NCI. This design effectively mitigates the impact of renewable energy uncertainty and enhances predictive resilience. On the demand side, the framework proposes a spatial-temporal carbon scheduling model that integrates geographically dispatchable loads (GDLs), including mobile energy storage systems (MESSs) and distributed data centers (DDCs). Leveraging high-accuracy day-ahead NCI predictions, the framework can effectively reduce system emissions by quickly responding to carbon intensity fluctuations. The proposed framework is tested on the modified IEEE 33-bus system. According to the simulation results, the impacts of proposed framework on dispatching latency and emission outcomes are analyzed. The results demonstrate that under a one-hour reduction in carbon scheduling latency, the proposed model and methodology can achieve over 30% emission reduction. This research breaks through the limitations of passive carbon accounting, advancing toward proactive carbon management. It offers an intelligent solution that accelerates the transition to cleaner power systems while directly supporting sustainable production goals.

Inter-Area Oscillation Damping in Data-Center-Integrated Power Systems

Ahmed Alfatlawi, Masoud H. Nazari

Published 2026-07-30 · arXiv · Credibility S

This paper develops explicit dynamic models of a hyperscale data center, including its heating, ventilation, and air conditioning (HVAC) and uninterruptible power supply (UPS) subsystems, and integrates them into a small-signal stability framework to investigate the impact of data center demand response on power system inter-area oscillations. Through eigenvalue analysis and time-domain simulations, the results demo…

Abstract, interpretation and reference

Abstract

This paper develops explicit dynamic models of a hyperscale data center, including its heating, ventilation, and air conditioning (HVAC) and uninterruptible power supply (UPS) subsystems, and integrates them into a small-signal stability framework to investigate the impact of data center demand response on power system inter-area oscillations. Through eigenvalue analysis and time-domain simulations, the results demonstrate that UPS-based demand response can enhance inter-area oscillation damping. In contrast, the HVAC subsystem is shown to be inherently incapable of providing effective oscillation damping due to its limited thermal response bandwidth. A gradient-based optimization algorithm is used to tune the UPS controller gain to maximize the damping ratio of the critical inter-area mode. The effectiveness of the proposed approach is validated using the IEEE 39-bus test system.

A Configurable Thermal-Dynamic Model for AI Data Center Cooling Load Simulation

Cletus Ngwerume, Lang Tong, Chee-Wooi Ten, Yi Hu

Published 2026-07-31 · arXiv · Credibility S

Cooling demand constitutes a significant and flexible component of AI data center electricity consumption, but time-synchronized measurements are scarce and constant coefficient-of-performance models cannot represent thermal dynamics. This letter proposes a configurable thermal dynamic simulation model for hybrid air- and liquid-cooled data centers. Unlike existing models centered on temperature prediction or equipm…

Abstract, interpretation and reference

Abstract

Cooling demand constitutes a significant and flexible component of AI data center electricity consumption, but time-synchronized measurements are scarce and constant coefficient-of-performance models cannot represent thermal dynamics. This letter proposes a configurable thermal dynamic simulation model for hybrid air- and liquid-cooled data centers. Unlike existing models centered on temperature prediction or equipment-level cooling analysis, the proposed model is designed to generate dynamic cooling electricity profiles for long-duration power system studies. The model is validated using operational telemetry from the Marconi100 supercomputer. Compared with the baseline, the proposed model reduces the mean absolute error from 95.80 to 20.88~kW and the root-mean-square error from 109.79 to 27.27~kW. Evaluation over approximately 520 daily profiles further shows improved reproduction of daily peak demand and intraday variability. The proposed model provides a computationally tractable means of generating physically interpretable cooling load profiles for power system studies.

From Individual to Shared Ownership: A Coalitional Game Approach to Sustainable Co-investment

Published 2026-07-29 · arXiv · Credibility S

This paper proposes a cooperative game-theoretic framework for sustainable co-investment in shared infrastructure under regulatory incentives. Multiple heterogeneous operators co-invest in a common infrastructure whose production capability evolves over time and is subject to operational variability. A regulator supports the deployment through incentive mechanisms designed to align individual economic investment obj…

Abstract, interpretation and reference

Abstract

This paper proposes a cooperative game-theoretic framework for sustainable co-investment in shared infrastructure under regulatory incentives. Multiple heterogeneous operators co-invest in a common infrastructure whose production capability evolves over time and is subject to operational variability. A regulator supports the deployment through incentive mechanisms designed to align individual economic investment objectives with the coalitional one. We formulate the co-investment problem as a transferable-utility (TU) coalitional game in which the value generated by cooperation depends on heterogeneous operational profiles, dynamic resource availability, investment costs, and regulatory incentive level. We show that the proposed coalitional game can be reformulated as a linear production game (LPG), whose dual prices yield a constructive and stable allocation of the cooperative surplus. Finally, we illustrate the proposed framework through a case study on co-investment among data center operators in shared renewable energy infrastructure, supported by government subsidies promoting renewable energy consumption.

Planning Waste-to-Energy-Coupled AI Data Centers Through Grade-Matched Cooling and Corridor Screening

Qi He, Chunyu Qu, Wenjie Zuo

Published 2026-07-28 · arXiv · Credibility S

AI data-center growth is increasingly constrained by limited deliverable electricity, interconnection capacity, and cooling demand. This study develops a boundary-consistent screening framework for waste-to-energy (WtE)-coupled AI data-center cooling. It treats cooling as an energy service that can be supplied through grade matching rather than only through electricity-driven mechanical chilling. The framework trans…

Abstract, interpretation and reference

Abstract

AI data-center growth is increasingly constrained by limited deliverable electricity, interconnection capacity, and cooling demand. This study develops a boundary-consistent screening framework for waste-to-energy (WtE)-coupled AI data-center cooling. It treats cooling as an energy service that can be supplied through grade matching rather than only through electricity-driven mechanical chilling. The framework translates plant-side exportable heat into corridor-level planning metrics by accounting for thermal attenuation, absorption conversion, and parasitic electricity for delivery and auxiliaries. In a reference case, a regulated WtE plant processing 1500 t/day of municipal solid waste at 10 MJ/kg provides about 78.1 MWth of exportable heat. At a 20 km corridor, this yields about 53.0 MW of delivered cooling and 8.0 MWe of net avoided cooling electricity after parasitic loads. The coupled system is governed by operating regimes rather than a single efficiency score. Under baseline assumptions, full thermal coverage extends to about 20.9 km, the quality-adjusted criterion remains positive to about 22.9 km, and net electricity relief remains positive to about 44.7 km. For a 1 GW IT campus at 70 percent utilization and a 5 km corridor, net grid relief ranges from about 116.9 to 264.4 MW across scenarios. The required WtE footprint ranges from about 3 to 148 representative plants, or 0.6 to 40 full-load-equivalent plants at a 25 percent displacement target. The framework identifies when WtE-coupled cooling is corridor-feasible, when hybrid operation is required, and when infrastructure scale becomes the binding constraint. It is intended for screening and comparison, not project-specific hydraulic or plant-cycle design.

A Predict-then-Schedule framework for Power Distribution Networks with AI Data Centers

Siqi Yan, Jiebao Zhang, Xi Yao, Juan Huang, Ye Shi

Published 2026-07-20 · arXiv · Credibility S

The surge of GPU-intensive workloads in artificial intelligence (AI) data centers drives massive energy demands, leading to soaring costs and significant stress on local power distribution networks. Coordinating delay-tolerant workload scheduling with power grid conditions via precise workload prediction can mitigate these issues. However, a critical gap remains in conventional approaches, i.e., minimizing predictio…

Abstract, interpretation and reference

Abstract

The surge of GPU-intensive workloads in artificial intelligence (AI) data centers drives massive energy demands, leading to soaring costs and significant stress on local power distribution networks. Coordinating delay-tolerant workload scheduling with power grid conditions via precise workload prediction can mitigate these issues. However, a critical gap remains in conventional approaches, i.e., minimizing prediction error does not necessarily lead to minimized downstream operational loss. Hence, this paper proposes an end-to-end Predict-Then-Schedule (PTS) framework that integrates upstream workload prediction with downstream scheduling optimization. By leveraging differentiable convex optimization, the PTS framework maps input features directly to optimal scheduling and enables gradient-based training. Furthermore, to respect the data center's capacity, a workload over-shifted loss combining electricity cost with a penalty for load-shedding is introduced to evaluate scheduling quality. Experiments demonstrate that the proposed framework significantly reduces operational cost and enhances system security compared to the conventional two-stage baseline.