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The U.S. Interconnection Queue System: Cascading Vulnerability Analysis and a Resilience Engineering Framework

Zahra Heidari

arXiv:2609.10455v1eess.SYcs.SY

TL;DR

The paper examines how project withdrawals and concentrated queue capacity may expose electrically interdependent projects to cascading risk. It combines empirical analysis of withdrawal patterns with a stylized network model and circuit-breaker interventions. The analysis finds clustered withdrawals and identifies cost-allocation design as a condition shaping modeled cascade severity.

Abstract

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As of 2025, the U.S. interconnection queues, the grid-access gateway for new generation and storage, contain roughly 8,200 projects totaling 2,061 GW of capacity. Only 13% of capacity queued in 2000-2020 (19% by project count) has reached operation. We argue that the queue architecture is vulnerable to self-reinforcing project withdrawal cascades arising from restudy and cost reallocation, independent of study delays. Adapting failure contagion models from financial and interdependent infrastructure networks, we model the queue as a complex adaptive system and test it with a statistical analysis of withdrawal temporal clustering and co-withdrawal across seven ISO/RTOs and a stylized network contagion simulation of cost-sharing interdependencies with circuit-breaker interventions. Using LBNL project-level data through 2025, we find temporal clustering (dispersion indices 5.0-102.4; p < 0.001) and identify 39 monthly withdrawal bursts with the largest cluster-dated, reaching 67.4x the regional mean. Withdrawal timing is concentrated within technology categories in all seven regions (1,000-permutation test; z = 2.58-4.78, p <= 0.005). Co-withdrawal within cohorts is significant only in four regions (0.5-1.8 percentage points). The model yields net cascade amplification of 1.0-1.5x at low-to-moderate connectivity (average network degree k = 3-10). At k = 20 a phase-transition-like shift to systemic instability occurs, exceeding 8.7x amplification at a 10% shock (97.7% cascade size); a boundary condition of the stylized network that disappears under pro-rata redistribution. Capping per-neighbor cost reallocation reduces cascade magnitude by up to 20% relative to no intervention, with a 44% lower mean per-neighbor transfer. These findings characterize the interconnection queue as critical infrastructure with cascading vulnerability whose severity is conditional on cost allocation design.

I. INTRODUCTION

The queue is a major grid-access bottleneck: only a small share of capacity in the 2000–2020 cohort reached operation. The paper argues that withdrawals can trigger restudies and cost shifts across electrically interdependent projects, creating cascading risks beyond processing delays.

A. Scale and Attrition Statistics T

  • About 2,061 GW of capacity was queued, exceeding the approximately 1,280 GW of installed U.S. generating capacity.Queued capacity included 773 GW of solar, 749 GW of storage, and 220 GW of wind.Source: A. Scale and Attrition Statistics · p. 2
  • 13% of planned capacity and 19% of projects in the 2000–2020 cohort reached commercial operation by the end of 2025.Approximately 76% of capacity was withdrawn, while 10% remained active.Source: A. Scale and Attrition Statistics · p. 2

B. Structural Properties Beyond Processing Delay I

  • A project withdrawal can alter study assumptions, prompting restudies and cost reallocation that may make remaining projects uneconomic and trigger further withdrawals.Operators MISO and SPP have described cascading restudies and upgrade-cost shifts following withdrawals.Source: B. Structural Properties Beyond… · pp. 2–3
  • The queue lacks a formal failure-propagation model, stress-testing framework, and resilience metrics for its tightly coupled interdependent network.The paper distinguishes this network-level vulnerability from study delays and other process-level problems.Source: B. Structural Properties Beyond… · pp. 2–3

C. Study Contributions F

The paper introduces a system-level framework for queue fragility and combines empirical withdrawal analysis with a stylized contagion model. It situates this approach alongside existing queue reforms and discusses the queue’s relevance to reliable power for critical loads.

C. Study Contributions F

  • The paper adapts threshold-cascade, interdependent-network, and financial-clearing models to study queue fragility, withdrawal dynamics, and circuit-breaker interventions.Its contributions include formalizing four queue-specific propagation properties and presenting empirical analyses across all seven ISO/RTOs.Source: C. Study Contributions · p. 3

D. Security Considerations T

  • Interconnection delays and cascading dynamics affect the arrival of new generation needed for reliable power supply to data centers and defense-sector loads.The paper states that it does not quantify foregone emissions reductions or economic value.Source: D. Security Considerations · p. 4

A. FERC Order 2023 and Cluster Study Reforms F

  • Cluster-based reforms target study-process problems, but the paper argues that they do not address network-level interdependence or prevent failure propagation.MISO’s expedited resource proposal moved priority projects to serial studies to reduce restudy risk.Source: A. FERC Order 2023 and Cluster Study… · pp. 4–5
  • Existing modernization efforts address processing efficiency or transparency, while the paper focuses on resilience to cascading failure in the queue’s interdependencies.The authors frame resilience engineering as complementary to efforts such as automation and grid-visibility tools.Source: C. Study Contributions · p. 3, A. FERC Order 2023 and Cluster Study… · p. 5

B. Resilience Engineering Gap

The paper frames the queue as a complex system whose network-level fragility is not addressed by reforms focused on entry, staffing, or study speed. Its framework links interdependent project studies and cost reallocations to possible withdrawal cascades.

B. Resilience Engineering Gap

  • The queue’s scale and changing mix of simultaneous requests motivate applying resilience engineering to a system susceptible to systemic failure.The paper says existing reforms address speculative entry, slow studies, and staffing, but not cascading failure vulnerability.Source: B. Resilience Engineering Gap · pp. 5–6

A. Theoretical Foundations W

  • The queue meets the paper’s five complex-adaptive-system criteria: heterogeneous agents, nonlinear interactions, adaptation, emergence, and path dependence.Project withdrawals or modifications can change upgrade requirements and cost allocations in subsequent studies.Source: A. Theoretical Foundations · p. 6

B. Cascading Failure Mechanisms F

  • A withdrawal can invalidate cost-sharing assumptions, prompting restudies that redistribute upgrade costs and may make remaining projects uneconomic.The passage reports average assessed costs of $373/kW for withdrawn projects and $73/kW for completed projects.Source: B. Cascading Failure Mechanisms · p. 6
  • Withdrawals clustering around study or financial milestones can create shocks that exceed affected projects’ absorption capacity and amplify propagation.Each withdrawal can alter assumptions and trigger another round of restudies.Source: B. Cascading Failure Mechanisms · pp. 6–7
  • Higher deposits may prompt earlier exits during cost reallocation, while expedited processing may divert resources from standard queues and raise attrition risk.The paper notes that these second-order effects have been largely absent from regulatory impact assessments.Source: B. Cascading Failure Mechanisms · p. 7

C. Built-in Fragility Characteristics W

The paper identifies tight coupling, concentration risk, information asymmetry, and absent circuit-breakers as structural sources of queue fragility. It draws on financial and infrastructure-network analogies to frame how local failures may propagate.

C. Built-in Fragility Characteristics W

  • A withdrawal can change assumptions for electrically adjacent projects, with restudy and cost disturbances traveling beyond administrative boundaries.The paper reports that 41% of capacity with executed interconnection agreements subsequently withdrew among projects from 2000 to 2022.Source: C. Built-in Fragility Characteristics · p. 7
  • Capacity concentrated at a small number of interconnection points and corridors makes smaller projects vulnerable to disproportionate cost reallocation when larger projects withdraw.The paper compares this counterparty-concentration risk to financial networks.Source: C. Built-in Fragility Characteristics · p. 7
  • Opaque cluster studies leave developers unable to assess peer-project viability or withdrawal probability, while remaining viable projects may absorb more repricing risk.The passage describes this as a mechanism that may concentrate reallocation risk among projects that remain in the queue.Source: C. Built-in Fragility Characteristics · p. 7
  • Without a queue mechanism to interrupt a withdrawal cascade, it may continue until a new equilibrium is reached, risking substantial project losses.The paper characterizes the queue as “robust yet fragile” under routine conditions but vulnerable to rare cascades.Source: C. Built-in Fragility Characteristics · p. 7

D. System Analogies R

  • Financial and power-grid contagion models provide analogies for how failures can spread through interdependent networks.The paper adopts financial contagion models for the queue system.Source: D. System Analogies · p. 8

E. Formal Mapping to Cascading Failure Models T

The framework maps interconnection-queue dependencies, withdrawals, and cost reallocation to established cascade models, while identifying important differences between those models and queue operations. It tests whether connectivity and exposure to non-dilutive costs jointly condition systemic collapse.

E. Formal Mapping to Cascading Failure Models T

  • Queue cost-sharing links are treated as interdependent-network links, withdrawals as threshold responses, and redistributed upgrade costs as an Eisenberg–Noe-type clearing process.The withdrawal response uses a Watts-type threshold rule.Source: E. Formal Mapping to Cascading Failure… · pp. 8–9
  • The mappings are approximate because queue dependencies are multilateral, viability thresholds shift, and restudies and revised cost allocations occur sequentially.The compared models instead use bilateral links, static thresholds, and simultaneous clearing.Source: E. Formal Mapping to Cascading Failure… · p. 9
  • Greater cost-sharing connectivity can stabilize small shocks by distributing withdrawal costs yet amplify large shocks through additional contagion channels.The paper relates this phase-transition behavior to financial-contagion findings and tests it in a stylized Erdős–Rényi network.Source: E. Formal Mapping to Cascading Failure… · p. 9
  • The framework hypothesizes moderate amplification under current conditions, with systemic collapse requiring both high cost-sharing interdependence and non-dilutive per-project costs.The paper tests these propositions through empirical and computational analyses.Source: E. Formal Mapping to Cascading Failure… · p. 9

IV. PRELIMINARY EMPIRICAL ANALYSIS

The preliminary empirical analysis uses LBNL project records across seven ISO/RTOs to measure temporal clustering, capacity withdrawal bursts, and co-withdrawal within project cohorts. Its methods distinguish aggregate clustering from cohort- and technology-associated timing while noting data coverage and proxy limitations.

A. Data Source and Methods F

  • 25,770 projects across seven ISO/RTOs form the empirical sample, including 16,206 withdrawals.The analysis uses the LBNL Queued Up 2026 Edition, with records from more than 50 transmission operators.Source: A. Data Source and Methods · p. 9
  • Withdrawal dates are available for 13,984 of 16,206 withdrawn projects (86.3%), with low ERCOT and NYISO coverage treated as lower bounds in temporal-clustering estimates.Coverage is 48.7% in both ERCOT and NYISO.Source: A. Data Source and Methods · p. 9
  • The dispersion index is the variance-to-mean ratio of monthly withdrawal counts from 2010 to 2025; values above 1 indicate overdispersion, tested against a Poisson null.The analysis uses a chi-squared goodness-of-fit test and also recalculates indices against each year's mean to adjust for queue growth.Source: A. Data Source and Methods · pp. 9–10
  • A withdrawal burst is a month with withdrawn capacity above the regional mean plus two standard deviations; normalized monthly capacity ratios are also compared with an exponential reference distribution.Burst and complementary cumulative distribution analyses use capacity, while the dispersion index uses withdrawal counts.Source: A. Data Source and Methods · p. 10
  • Cohort peers share an ISO/RTO and technology category and entered within ±6 months; a co-withdrawal occurs when another peer exits within ±12 months.The peer set is an observable proxy for shared cost-reallocation exposure, not electrical connectivity or cost-sharing adjacency.Source: A. Data Source and Methods · pp. 10–11
  • The date-based analyses exclude 53 withdrawal dates after 31 December 2025 and records with internally inconsistent date combinations.Some records also lack entry dates.Source: A. Data Source and Methods · pp. 10–11
  • Three 1,000-permutation nulls test cohort- or technology-associated timing, with constrained permutations preserving entry–exit order or temporal structure.The unrestricted null is a baseline rather than a standalone recommendation because it can produce withdrawals before project entry.Source: A. Data Source and Methods · p. 11

B. Empirical Analysis Results T

Withdrawals are strongly overdispersed across all seven ISO/RTOs, and the clustering persists under checks for extreme months and date-assignment practices. Cohort and technology permutation tests show distinct patterns, while the pooled upper tail requires caution because regional distributions differ.

Empirical results

  • Dispersion indices range from 4.96 in NYISO to 102.40 in CAISO, with overdispersion significant in all seven regions (p < 0.001).The Poisson benchmark is D = 1; CAISO’s high index is largely attributable to its December 2024 cluster-deadline event.Source: B. Empirical Analysis Results · p. 11, Table III · p. 12
  • After removing the three largest monthly counts, dispersion indices still range from 3.76 to 29.66, all above the Poisson benchmark (p < 0.001).Overdispersion also persists after excluding cluster-level or bulk-assigned dates and after removing extreme dates.Source: B. Empirical Analysis Results · pp. 11, 13, Table IV · p. 13
  • 39 burst months occurred across the seven regions; CAISO’s December 2024 burst reached 67.4× its monthly mean across 426 projects.That month’s 168.6 GW withdrawal coincided with the cluster-study decision deadline; 303 records belonged to one Cluster 15 block.Source: B. Empirical Analysis Results · pp. 11–12
  • The pooled monthly-withdrawal-ratio CCDF departs from an exponential reference in the upper tail, but regional distributions differ substantially.Regional skewness ranges from 1.9 to 13.1, so the pooled tail may partly reflect cross-region heterogeneity.Source: B. Empirical Analysis Results · p. 12
  • Entry-year-stratified tests found significant cohort co-withdrawal in four regions, with excesses of 0.5–1.8 percentage points.The other three regions were not significant; the entry-year-stratified null preserves entry-to-exit ordering.Source: B. Empirical Analysis Results · pp. 12–13
  • Technology-category withdrawal timing was significantly concentrated in all seven regions, with excesses of 0.7–5.0 percentage points (p ≤ 0.005).The test permuted technology labels while holding entry and withdrawal dates fixed.Source: B. Empirical Analysis Results · p. 13

C. Interpretation T

The withdrawal patterns are consistent with dependent failure dynamics, but the evidence does not establish direct causal cascades. Many projects also withdraw after contractual commitment, leaving substantial capacity exposed to possible restudy and cost-reallocation risk.

Interpretation

  • Project withdrawals showed statistically significant temporal clustering across all seven ISO/RTOs, with dispersion indices ranging from 5.0 to 102.4 (all p < 0.001), rather than independent random queue events.This satisfies a necessary, though not sufficient, condition for the proposed failure dynamics; permutation tests also indicate co-withdrawal among specific project cohorts and technology categories beyond overall regional clustering.Source: C. Interpretation · p. 13
  • Withdrawal bursts often coincide with queue-stress periods or procedural milestones, but available data cannot distinguish endogenous cascades from common-cause clustering.The withdrawal patterns are consistent with cascading-failure characteristics, but do not establish direct causal events.Source: C. Interpretation · p. 14
  • By the end of 2025, 34.8% of interconnection-agreement projects and 41% of their capacity had withdrawn, while 549 GW with draft or executed agreements remained unoperated.These post-agreement withdrawals cannot be attributed solely to speculative applications; cost-reallocation and restudy exposure is a possible explanation.Source: C. Interpretation · pp. 14–15

D. The 2025 Withdrawal Wave T

The 2025 withdrawal wave was unusually large and temporally concentrated amid the first full implementation cycle of FERC Order 2023 cluster reforms. The section also describes a stylized project network in which withdrawals redistribute costs and can trigger further failures.

D. The 2025 Withdrawal Wave T

  • 2025 saw about 3.5 times as many withdrawals by project count and 3.8 times by capacity as the 2010–2024 annual average, the dataset’s largest withdrawal year.Timestamped records cover 2,417 withdrawals, or roughly 398 GW, across seven ISO/RTOs.Source: D. The 2025 Withdrawal Wave · p. 15
  • MISO recorded 171.7 GW of withdrawals, including 146.4 GW in four burst months, while seven-region monthly withdrawals reached 74.0 GW in December 2025.The wave coincided with the first full implementation cycle of FERC Order 2023 cluster reforms and readiness-deposit and cluster-decision milestones.Source: D. The 2025 Withdrawal Wave · p. 15
  • The wave is consistent with both reforms screening speculative projects and standardized deadlines synchronizing withdrawal decisions as a correlated shock.The section presents these as complementary interpretations, not mutually exclusive explanations.Source: D. The 2025 Withdrawal Wave · p. 15
  • Roughly 25% of active projects across the seven ISO/RTOs withdrew in one year, an unprecedented share in the dataset; propagation through restudy cycles requires later data vintages to assess.The 2025 withdrawals correspond to a calibrated model scenario yielding 38.5% total failure at k = 10.Source: D. The 2025 Withdrawal Wave · p. 15

A. Model Description W

  • The model represents projects as nodes and cost-sharing ties as edges in an Erdős–Rényi graph; a project withdraws when cumulative load exceeds its viability threshold.An initial shock redistributes failed projects’ costs to active neighbors, and each resulting withdrawal can trigger further redistribution.Source: A. Model Description · pp. 15–16
  • A circuit-breaker caps cost redistribution per adjacent project, abstracting mechanisms such as suspending cascading restudies or limiting reallocated upgrade costs.The modeled cap is a stylized cost-reallocation cap applied to each pair of adjacent projects.Source: A. Model Description · p. 16

B. Simulation Design F

The simulations vary network connectivity, shock size, threshold distributions, topology, and redistribution rules, then test cost-reallocation caps and empirically calibrated shock scenarios. Results show amplification at low-to-moderate connectivity and a k = 20 collapse regime under the non-divisible cost rule, while pro-rata redistribution removes that regime.

Simulation design

  • The design uses 200 simulations of 500-node networks, varying connectivity and shock fraction; additional experiments change thresholds, topology, targeting, and redistribution.The empirically calibrated scenarios draw project sizes from a fitted lognormal distribution.Source: B. Simulation Design · pp. 16–17

Simulation results

  • At k = 3 and k = 10, a 10% shock yields 1.06× and 1.38× net amplification, respectively; at k = 20, the model enters an unstable regime.At k = 20, the load added per failure exceeds the load removed, and zero-shock simulations are themselves unstable.Source: C. Computational Contagion Model Results · p. 17
  • At k = 10, a 10% shock produces 15.3% total failure without a cap, versus 12.3% at β = 0.02, a 20% reduction.The strictest cap also reduces mean per-neighbor transfer by 44% and net amplification from 1.38× to 1.08×.Source: C. Computational Contagion Model Results · pp. 17–18, Figure 4 · p. 19

Sensitivity analyses

Simulation results

  • At k = 20, non-divisible redistribution produces 97.7% total failure and 8.69× net amplification, while divided redistribution reduces these to 11.1% and 1.00×.The collapse regime disappears under divided cost exposure; the comparison isolates the structural effect of whether project-level cost exposure remains undiluted.Source: C. Computational Contagion Model Results · p. 18, Table V · p. 18, Figure 4 · p. 19

Sensitivity analyses

  • At k = 10, the scale-free network has 26.7% total failure versus 15.3% for Erdős–Rényi, while targeted removal of high-degree nodes yields 17.6% versus 15.3% under random shocks.At k = 20, collapse does not occur under the scale-free topology.Source: C. Computational Contagion Model Results · p. 18

Empirically calibrated shocks

  • Using LBNL-calibrated shocks, the model yields 1.34×–1.49× net amplification across median, 75th-percentile, 2025 annual-wave, and CAISO December 2024 event scenarios under assumed moderate connectivity (k = 10).Total failures were 6.1%, 12.7%, 38.5%, and 62.3%, respectively; calibrated shocks remained in the amplification regime without boundary-condition collapse at k = 10.Source: B. Simulation Design · p. 17, C. Computational Contagion Model Results · pp. 18–19

D. Model Limitations and Validation T

The model is a proof of concept rather than a tariff forecast: its results depend on stylized network and cost-allocation assumptions, and empirical validation requires data not yet consolidated. Its collapse regime is specific to undiluted cost exposure and disappears under pro-rata redistribution.

Model limitations and validation

  • The model uses a stylized random graph and uniform cost reallocation rather than the queue’s full institutional interdependencies or FERC Order 2023 proportional-impact methods.The authors describe the work as a proof of concept for cascade amplification and phase-transition behavior.Source: D. Model Limitations and Validation · pp. 19–20
  • Fixed thresholds and α = 0.08 condition the results; the non-conservative rule overstates actual cost reallocation, so collapse represents stylized accounting rather than tariff behavior.Developers’ actual decisions can respond dynamically to evolving costs, timelines, and project-specific conditions.Source: D. Model Limitations and Validation · p. 19
  • The phase transition disappears under fully pro-rata redistribution and depends on whether individual projects absorb undiluted reallocated costs.The reported monotone trend applies specifically to the non-dilutive rule; pro-rata total failure peaks at moderate connectivity and then declines.Source: D. Model Limitations and Validation · pp. 19–20
  • Amplification magnitudes vary with model parameters, and empirical validation requires project-level cost-allocation records and study-milestone timestamps that remain fragmented across ISO/RTO portals.Across configurations, amplification magnitudes vary by a factor of 4–6×.Source: D. Model Limitations and Validation · p. 20

VI. POLICY AND NATIONAL SECURITY DIMENSIONS

The section frames the queue as critical infrastructure with interdependence and withdrawal clustering, but without a structured resilience analysis. It recommends shared risk metrics and standards, and argues that queue fragility is also a constraint for AI data-center deployment.

A. Resilience Standards for the Interconnection Queue U

  • The queue is classified as critical infrastructure but has not yet received structured resilience analysis, despite shared upgrades, cost reallocation, and statistically significant withdrawal clustering across all seven ISO/RTOs.The reported dispersion indices are 5.0–102.4, with p < 0.001.Source: A. Resilience Standards for the… · p. 20
  • The authors recommend queue-wide risk assessment and standardized cascade metrics, a NERC- or FERC-led resilience standards body, and research support.The proposed risk assessment is modeled structurally on financial stress tests.Source: A. Resilience Standards for the… · p. 20

B. Implications for AI Data Center Infrastructure P

  • Data-center schedules remain exposed to cascading restudy and cost-reallocation mechanisms regardless of whether they enter through collocated generation or contracted resources.The section concludes that queue fragility is a structural constraint on AI data-center deployment.Source: B. Implications for AI Data Center… · pp. 20–21
  • Accelerating data-center interconnections without addressing queue fragility could shift cascade risk elsewhere, while standard-queue attrition could slow power-sector decarbonization.The authors state that scalable grid expansion is constrained by queue fragility.Source: B. Implications for AI Data Center… · p. 21

C. Comparative Institutional Architecture T

In serial, project-by-project queues, withdrawals trigger restudies and reallocate shared upgrade costs, coupling otherwise independent applications. The analysis reports clustered withdrawals and modelled cascades, while finding that cascade vulnerability depends on cost-allocation architecture and is not inherent to market-based electricity systems.

C. Comparative Institutional Architecture T

  • In serial project-by-project processing, one withdrawal triggers mandatory restudies and reallocates upgrade costs, coupling otherwise independent applications.The paper identifies this interdependence as necessary for withdrawal contagion to spread.Source: C. Comparative Institutional… · p. 21
  • IRP, centralized siting, and coordinated transmission-and-generation planning avoid project-by-project shared-cost allocation, eliminating the identified cost-reallocation interdependency and contagion mechanism.The paper concludes that queue fragility follows from current ISO/RTO processing architecture, not from market-based electricity systems; eliminating the interdependency can suppress the cascade without affecting competitive market design.Source: C. Comparative Institutional… · p. 21

VII. CONCLUSION

  • Although current reforms may accelerate study throughput, the system-level cascading failure mechanisms remain unaddressed at the architecture level.The queue was designed for lower volume and complexity; its completion and withdrawal rates directly affect the pace of grid decarbonization, while foregone emissions reductions and economic value are not quantified here.Source: §VII · pp. 21–22
  • Dispersion indices of 5.0–102.4 (p < 0.001) show temporally clustered withdrawals across all seven ISO/RTOs.The analysis identified 39 monthly withdrawal bursts, with withdrawn capacity reaching 67.4× the regional mean.Source: §VII · p. 21
  • Withdrawal timing concentrated within technology classes in every region, while cohort co-withdrawal was significant in four of seven regions and small under fixed entry-exit trend.The reported association is temporal and technology specific; the withdrawal series are not independent draws.Source: §VII · p. 21
  • At k = 3–10, the model produced net cascade amplification of 1.0–1.5×; beyond its critical connectivity threshold, it shifted toward near-total failure.The threshold is located by kα and does not arise under mass-conserving pro-rata redistribution.Source: §VII · p. 21
  • At k = 10, simulated circuit-breaker mechanisms reduced cascade magnitude by up to 20% relative to the no-intervention baseline.This is a result of the model, not an empirical intervention estimate.Source: §VII · p. 21
  • The queue is a structural bottleneck between the clean-energy project pipeline and a decarbonized electricity system, motivating resilience-engineering research on the queue and cost-isolation mechanisms.The queue also affects reliable power supply at predictable costs for critical infrastructure, particularly data-center and defense-sector loads.Source: §VII · p. 22
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