arXiv subjects

Artun Sel

Publications and source records attributed to Artun Sel.

3 recordsLinked to original sources

arXiv2026

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

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.

cs.IT↗

arXiv2026

Estimation of Regions of Attraction for Nonlinear Systems via Coordinate-Transformed TS Models

This paper presents a novel method for estimating larger Region of Attractions (ROAs) for continuous-time nonlinear systems modeled via the Takagi-Sugeno (TS) framework. While classical approaches rely on a single TS representation derived from the original nonlinear system to compute an ROA using Lyapunov-based analysis, the proposed method enhances this process through a systematic coordinate transformation strategy. Specifically, we construct multiple TS models, each obtained from the original nonlinear system under a distinct linear coordinate transformation. Each transformed system yields a local ROA estimate, and the overall ROA is taken as the union of these individual estimates. This strategy leverages the variability introduced by the transformations to reduce conservatism and expand the certified stable region. Numerical examples demonstrate that this approach consistently provides larger ROAs compared to conventional single-model TS-based techniques, highlighting its effectiveness and potential for improved nonlinear stability analysis.

eess.SY↗

arXiv2025

Estimation of Regions of Attraction for Nonlinear Systems via Coordinate-Transformed TS Models and Piecewise Quadratic Lyapunov Functions

This paper presents a novel approach for computing enlarged Region of Attractions (ROA) for nonlinear dynamical systems through the integration of multiple coordinate transformations and piecewise quadratic Lyapunov functions within the Takagi-Sugeno (TS) modeling framework. While existing methods typically follow a single-path approach of original system $\rightarrow$ TS model $\rightarrow$ ROA computation, the proposed methodology systematically applies a sequence of coordinate transformations to generate multiple system representations, each yielding distinct ROA estimations. Specifically, the approach transforms the original nonlinear system using transformation matrices $T_1, T_2, \ldots, T_N$ to obtain $N$ different coordinate representations, constructs corresponding TS models for each transformed system, and computes individual ROAs using piecewise quadratic Lyapunov functions. The final ROA estimate is obtained as the union of all computed regions, leveraging the flexibility inherent in piecewise quadratic Lyapunov functions compared to traditional quadratic approaches. The enhanced methodology demonstrates significant improvements in ROA size estimation compared to conventional single-transformation techniques, as evidenced through comparative analysis with existing TS-based stability methods.

math.DS↗