arXiv subjects

Chandan Chaudhary

Publications and source records attributed to Chandan Chaudhary.

8 recordsLinked to original sources

arXiv2026

Real-Time Edge-based Detection of Correlated AI Data-Center Load Episodes

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.

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arXiv2026

A Hierarchical Semi-Markov Load Model for AI Data Centers Coupling Job Scheduling with Bulk-Synchronous-Parallel Power Dynamics

AI data centers are emerging as a dominant new load class with their power dynamics fundamentally from conventional industrial loads. Inside a training job, the bulk-synchronous-parallel algorithm moves each node through compute, sync, and checkpoint steps, which swings power between full load and near idle within seconds. Across the whole facility, jobs arrive, take blocks of nodes for hours to days, then leave, so the number of busy nodes changes daily, weekly, and yearly. This slower shift drives facility-wide swings and the peak demand that sets the size of the grid link. A model that looks only at within-job behavior, and treats the facility as a fixed set of busy nodes, smooths out these swings and misses the true peak-to-average ratio. This paper develops a hierarchical semi-Markov Data-Center (HSM-DC) load model that couples two layers across two timescales. A job-scheduling layer creates jobs through a non-homogeneous compound-Poisson process shaped by daily, weekly, and seasonal patterns, gives each job a heavy-tailed node count and length, and places jobs on a fixed pool of nodes on a first-come basis. A within-job layer moves each busy node through a five-state semi-Markov chain for the BSP steps, with state-based Ornstein-Uhlenbeck noise. Facility power comes from this changing node count and the per-node power, set to match measured node data and the facility's straight-line power-versus-load curve. Configured to the reference facility at the same scale, the model matches mean power, its spread, and the peak-to-average ratio across load levels, with fit scores of 0.9997, 0.92, and 0.82. It also matches the share of queued jobs to within one point at high load. Facility-wide swings and peak demand come from how jobs arrive and get scheduled, so grid planning must model that process, not just scale up a single node's power curve.

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arXiv2026

Large-Load Demand Flexibility as Virtual Storage

Water electrolysis plants, hyperscale data centers, and aluminum potlines represent gigawatts of demand-side flexibility for bulk power system balancing, operational planning, and procurement services. Such loads are scheduled through per-interval power bounds and horizon energy windows, whereas co-located battery energy storage systems (BESS) operate under state-of-charge dynamics. The two formulations share no common mathematical structure, and the joint procurement value of co-located loads and storage goes unrealized as a result. This paper establishes the connection between the two formulations through a virtual storage (VS) equivalence. Every feasible large-load trajectory under power-bound and energy-window constraints is a valid charge trajectory of a VS device that operates at unity accounting efficiency in the grid power balance. Production and service-level costs lie outside this abstraction and enter the dispatch through curtailment opportunity costs. For a portfolio co-located with a BESS, aggregation reduces the constraint count from O(NT) to O(T) and yields a co-dispatch price for both resources. Validation on the IEEE RTS-GMLC with three representative load classes shows that virtual storage delivers the dominant share of joint procurement savings. In the tested case, savings are additive because the two resources dispatch to non-overlapping intervals, and the curtailment shadow price tracks the peak-price band onset rather than the daily peak price.

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arXiv2026

Storage as a Transmission Asset (SATA) for Large-Load Congestion Relief

Hyperscale data centers and other large concentrated loads can impose substantial new demand on existing transmission networks. If import corridors lack sufficient transfer capability, operators may need to curtail load, delay interconnection, or reinforce the network to maintain reliable service. An energy storage system (ESS) deployed as a storage-as-transmission asset (SATA) offers a non-wires alternative by providing operator-directed support to constrained import corridors. However, the operating-level reliability value of SATA dispatch remains insufficiently quantified. This paper evaluates operator-directed SATA using a day-ahead DC optimal power flow that co-optimizes generation, ESS dispatch, and load curtailment across Monte Carlo scenarios of demand and generator availability. Operating reliability is assessed using expected energy not served (EENS), loss-of-load hours (LOLH), and the conditional value at risk (CVaR) of daily unserved energy. Congestion-price and flow-sensitivity metrics are used to identify the limiting corridor and storage location. The interconnection is then screened to determine whether SATA is suitable, reinforcement is required, or storage would provide little transmission value. Results show that operator-directed SATA reduces average unserved energy, loss-of-load exposure, and tail risk compared with deploying the same ESS for pure arbitrage. These results demonstrate that the operating designation of storage is a primary driver of its transmission value.

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arXiv2026

Two-Stage Optimization for Dynamic Line Rating and Energy Storage Deployment

The increasing penetration of distributed energy resources (DER) and weather-driven variability has intensified congestion and reliability stress in transmission networks. Strategies that enhance the utilization of existing infrastructure, such as static line ratings (SLR) and energy storage systems (ESS), have therefore become necessary. SLRs rely on conservative ambient assumptions and often understate thermal limits, whereas dynamic line ratings (DLR) adjust capacity according to weather conditions and unlock additional transfer capability. Energy storage systems provide temporal flexibility, but their transmission-level effectiveness depends on proper siting and sizing. This paper proposes a two-stage optimization method for joint placement of DLR installations and utility-scale energy storage. In the first stage, a mixed-integer linear program selects DLR corridors and ESS buses by minimizing operating cost, DER curtailment, and load-shedding penalties subject to DC power flow and investment constraints. In the second stage, the model determines ESS energy capacity and operating schedules under ambient-driven line ratings. Ambient weather data is used to generate DLR profiles, and sequential Monte Carlo simulation is applied to assess system adequacy. The proposed method, when deployed on the modified IEEE RTS 24-bus system, shows that coordinated DLR and ESS planning improves transmission capability, mitigates congestion, and strengthens system adequacy under weather variability.

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arXiv2026

A Pre-Dispatch Resonance Safety Criterion for AI Training Clusters

Hyperscale AI training clusters operate under the Bulk Synchronous Parallel protocol, which impose a periodic power swing on the transmission grid. Every GPU in the job transitions between compute and idle in lockstep, so the aggregate power traces a square wave at the training iteration period. Production iteration periods of one to ten seconds place the forcing frequency within the inter-area electromechanical mode band of large interconnections, where a training schedule can drive a mode at resonance. This paper derives a closed-form pre-dispatch safety criterion that bounds the maximum cluster size a grid can absorb at any proposed iteration period. The derivation inverts the steady-state forced two-area swing equations. The criterion defines a danger band of iteration periods, extends to the square-wave harmonics, and parameterizes the modal response from planning-study eigenanalysis and the forcing amplitude from GPU specifications. Applied to the IEEE 39-bus system at a production-representative duty cycle, the criterion shows that the maximum safe cluster at resonance is $66\,900$ GPUs under light damping. Rescheduling the same job less than one second away from resonance reduces the deviation $7.4\times$ with no hardware change. These results establish the training iteration period as a controllable grid-safety parameter and supply the analytic screening tool that reliability directives on current large loads lack.

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arXiv2026

Modal Analysis of Spatial Load Correlation in AI Data Center-Dominated Power Systems

Hyperscale AI data centers induce spatially and temporally correlated load fluctuations that violate classical independence assumptions and are not captured by time-averaged spectral methods. These correlations are episodic and non-stationary, so they demand analysis that resolves transient structure. This paper applies Dynamic Mode Decomposition (DMD) to the temporal evolution of pairwise inter-bus correlation coefficients and forms a low-dimensional state representation that enables modal analysis without a stationarity assumption. The recovered modes distinguish sustained coherence, decaying transients, and intensifying events, and their oscillation timescales map to underlying physical coupling mechanisms. The method is evaluated on an IEEE 39-bus Real-Time Digital Simulator (RTDS) testbed with three converter-interfaced AI data center loads driven by synthetic workload profiles. A global analysis attributes the dominant correlation energy to a slow thermal band, and a sliding-window analysis identifies brief intensification events in a small fraction of windows that align with stochastic workload coincidences. Cross-validation with RTDS voltage coherence confirms elevated coupling during these intervals. The proposed modal growth indicator provides an early-warning signal of correlation intensification, with a lead of of about 4~s before pairwise coherence reaches its peak.

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arXiv2026

Spatial Load Correlation in AI Data-Center-Dominated Power Systems

The proliferation of large-scale data centers introduces spatially correlated demand profiles that challenge the long-standing assumption of statistical independence of loads in power system analysis. This paper examines the emergence of such load correlations and evaluates their impact on data-center-dominated grids. Analytical derivations reveal that correlated load fluctuations amplify aggregate stochastic disturbances, reduce voltage stability margins through weakened reactive power stiffness, and degrade frequency stability margin by erosion of natural load diversity effects. Real-time digital simulation studies confirm that moderate spatial correlation in distributed data centers produces simultaneous frequency deviations and voltage fluctuations across multiple buses. The findings offer transmission system operators a physics-based perspective to interpret emerging oscillatory phenomena and establish stability planning criteria grounded in measurable load-correlation structures rather than traditional diversity assumptions.

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