arXiv subjects

Mohammed Ben-Idris

Publications and source records attributed to Mohammed Ben-Idris.

14 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

Detection of Synchronized AI Data Center Load Episodes Using SCADA Telemetry

AI data centers running distributed training workloads impose episodic, spatially correlated active-power disturbances on the transmission grid. These synchronized episodes increase cross-substation load correlation and limit the diversification benefit that reserve margin planning assumes. Conventional energy management systems evaluate each substation independently and do not extract the cross-substation statistical structure that defines a synchronized episode. This paper develops a detection method that identifies synchronized AI data-center load episodes from standard active-power telemetry without new instrumentation, trained classifiers, or labeled data. The method computes a Synchronization Index, the dominant eigenvalue fraction of a sliding sample covariance matrix across substations. A cumulative-sum (CUSUM) sequential test converts the index into a delay-bounded episode alarm. The same eigen decomposition yields, at no additional cost, a dominant eigenvector that attributes a detected episode to the substations that drive it. Tests on a real-time digital simulator (RTDS) model of the IEEE 39-bus system with three AI data-center buses show that the method separates episode and normal windows with a wide margin over chance and attributes episode participation at substation granularity.

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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

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

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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arXiv2023

Co-optimization of Operational Unit Commitment and Reserve Power Scheduling for Modern Grid

Modern power grids combine conventional generators with distributed energy resource (DER) generators in response to concerns over climate change and long-term energy security. Due to the intermittent nature of DERs, different types of energy storage devices (ESDs) must be installed to minimize unit commitment problems and accommodate spinning reserve power. ESDs have operational and resource constraints, such as charge and discharge rates or maximum and minimum state of charge (SoC). This paper proposes a linear programming (LP) optimization framework to maximize the unit-committed power for a specific optimum spinning reserve power for a particular power grid. Using this optimization framework, we also determine the total dispatchable power, non-dispatchable power, spinning reserve power, and arbitrage power using DER and ESD resource constraints. To describe the ESD and DER constraints, this paper evaluates several factors: availability, dispatchability, non-dispatchability, spinning reserve, and arbitrage factor. These factors are used as constraints in this LP optimization to determine the total optimal reserve power from the existing DERs. The proposed optimization framework maximizes the ratio of dispatchable to non-dispatchable power to minimize unit commitment problems within a specific range of spinning reserve power set to each DER. This optimization framework is implemented in the modified IEEE 34-bus distribution system, adding ten DERs in ten different buses to verify its efficacy.

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arXiv2023

Coalitional Game Theory in Power Systems: Applications, Challenges, and Future Directions

Game theory-based approaches have recently gained traction in a wide range of applications, importantly in power and energy systems. With the onset of cooperation as a new perspective for solving power system problems, as well as the nature of power system problems, it is now necessary to seek appropriate game theory-based tools that permit the investigation and analysis of the behavior and relationships of various players in power system problems. In this context, this paper performs a literature review on coalitional game theory's most recent advancements and applications in power and energy systems. First, we provide a brief overview of the coalitional game theory's fundamental ideas, current theoretical advancements, and various solution concepts. Second, we examine the recent applications in power and energy systems. Finally, we explore the challenges, limitations, and future research possibilities with applications in power and energy systems in the hopes of furthering the literature by strengthening the applications of coalitional game theory in power and energy systems.

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arXiv2023

Cyber-Physical Power System Layers: Classification, Characterization, and Interactions

This paper provides a strategy to identify layers and sub-layers of cyber-physical power systems (CPPS) and characterize their inter- and intra-actions. The physical layer usually consists of the power grid and protection devices whereas the cyber layer consists of communication, and computation and control components. Combining components of the cyber layer in one layer complicates the process of modeling intra-actions because each component has different failure modes. On the other hand, dividing the cyber layers into a large number of sub-layers may unnecessarily increase the number of system states and increase the computational burden. In this paper, we classify system layers based on their common, coupled, and shared functions. Also, interactions between the classified layers are identified, characterized, and clustered based on their impact on the system. Furthermore, based on the overall function of each layer and types of its components, intra-actions within layers are characterized. The strategies developed in this paper for comprehensive classification of system layers and characterization of their inter- and intra-actions contribute toward the goal of accurate and detailed modeling of state transition and failure and attack propagation in CPPS, which can be used for various reliability assessment studies.

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arXiv2022

Determination of Optimal Size and Number of Movable Energy Resources for Distribution System Resilience Enhancement

This paper proposes an approach based on graph theory and combinatorial enumeration for sizing of movable energy resources (MERs) to improve the resilience of the electric power supply. The proposed approach determines the size and number of MERs to be deployed in a distribution system to ensure the quickest possible recovery of the distribution system following an extreme event. The proposed approach starts by generating multiple line outage scenarios based on fragility curves of distribution lines. The generated scenarios are reduced using the k-means method. The distribution network is modeled as a graph where distribution network reconfiguration is performed for each reduced line outage scenario. The combinatorial enumeration technique is used to compute all combinations of total MER by size and number. The expected load curtailment (ELC) corresponding to each locational combination of MERs is determined. The minimum ELCs of all combinations of total MER are used to construct a minimum ELC matrix, which is later utilized to determine optimal size and number of MERs. The proposed approach is validated through a case study performed on a 33-node distribution test system.

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arXiv2021

Hosting Capacity Approach Implications

This paper revisits the generation hosting capacity (HC) calculation approach to account for grid operational flexibility--the ability to reconfigure the system safely. In essence, the generation hosting capacity is determined against the set of limiting factors--voltage, thermal (conductor loading), reverse flow (at the feeder head, station transformer, or substation), and change in the voltage (due to sudden change in generation output)). Not that long ago, California Investor-Owned Utilities (IOUs) added a new criterion that does not allow reverse flow at the supervisory control and data acquisition (SCADA) points that can change the system configuration, aiming to prevent the potential transfer of reverse flow to an adjacent feeder. This new criterion intended to capture operational constraints as part of hosting capacity-known as hosting capacity with operational flexibility (OpFlex). This paper explores the shortfalls of such an approach and proposes performing actual transfer analysis when determining hosting capacity rather than implementing the OpFlex approach. Furthermore, we discuss the need for transition to determining hosting capacity profile (all intervals) rather than a flat line (one, worst performing interval) hosting capacity. A hosting capacity profile would inform the developers of interval-by-interval limits and opportunities, creating new opportunities to reach higher penetration of DERs at a lower cost. With technological and computational advancements, such an approach is neither out of implementation reach nor that computationally expensive. In return, far more DER can be interconnected once programmed not to violate certain generation profiles as part of the interconnection requirement, and utilities would be better informed of their actual operational flexibility, benefiting society overall.

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arXiv2021

A Cooperative Game Theory-based Approach to Under-frequency Load Shedding Control

This paper proposes a cooperative game theory-based under-frequency load shedding (UFLS) approach for frequency stability and control in power systems. UFLS is a crucial factor for frequency stability and control especially in power grids with high penetration of renewable energy sources and restructured power systems. Conventional UFLS methods, most of which are off-line, usually shed fixed amounts of predetermined loads based on a predetermined schedule which can lead to over or under curtailment of load. This paper presents a co-operative game theory-based two-stage strategy to effectively and precisely determine locations and amounts of loads to be shed for UFLS control. In the first stage, the total amount of loads to be shed, also referred to as deficit in generation or the disturbance power, is computed using the initial rate of change of frequency (ROCOF) referred to the equivalent inertial center. In the second stage, the Shapley value, one of the solution concepts of cooperative game theory, is used to determine load shedding amounts and locations. The proposed method is implemented on the reduced 9-bus 3-machine Western Electricity Coordinating Council (WECC) system and simulated on Real-time Digital Simulators (RTDS). The results show that the proposed UFLS approach can effectively return the system to normal state after disturbances.

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