Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2509.01778

Grid Transmission Evaluation for Solar Deployment and Data Center Growth

Kajal Sheth·Dhvanil Patel·Shyam Kareepadath Sajeev
View original source ↗Read source document ↗Export metadata ↓

Abstract

The rapid growth of renewable energy deployment and data center demand in the United States has intensified challenges in grid interconnection, with project delays and escalating costs threatening both economic expansion and energy reliability. This study investigates transmission constraints using the IEEE 39-bus New England Power System model to evaluate the simultaneous interconnection of a 1 GW solar facility and a 1 GW data center load. Employing PSSE and Python-based automation (psspy), we conducted 1,560 load flow simulations across varying siting configurations to assess branch overloads and transmission line limits. Results revealed that only 14 configurations avoided overloads, while most scenarios highlighted recurring congestion on specific network branches, particularly between buses 21 and 22. Optimal siting was identified with the load at bus #35 and the generator at bus #39, yielding minimal overloads (maximum 91.1% loading). Conversely, poor siting decisions resulted in severe congestion with maximum branch loading above 220%. The findings underscore the critical importance of optimized siting and modernized, automated interconnection studies to reduce delays and costs in renewable integration. This research demonstrates the potential of advanced modeling tools to accelerate interconnection processes, improve system reliability, and inform future strategies for balancing renewable energy deployment with rising data center demand.

Explore related subjects

stat.OT

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kajal Sheth, Dhvanil Patel, Shyam Kareepadath Sajeev. 2025-09-11. Grid Transmission Evaluation for Solar Deployment and Data Center Growth. https://arxiv.org/abs/2509.01778

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Why is Regularization Underused? An Empirical Study on Trust and Adoption of Statistical Methods

Statistical practice does not automatically follow methodological innovation. Regularization methods, widely advocated to reduce overfitting and stabilize inference, are readily available in modern software, but are not consistently used by data analysts. We investigate this implementation gap in a large-scale empirical study of trust in, and acceptance of, regularization techniques, based on $N = 606$ data analysts. Drawing on measurement frameworks from technology acceptance research, we survey practitioners and embed a randomized experiment to test whether written recommendation of regularization methods increases trust or intended use. We find no evidence of such an effect. Instead, adoption intentions are strongly associated with analysts' perceptions of ease of implementation and practical benefit, such as improved bias control or interpretability. Perceived social norms also emerge as a central driver. These results indicate that uptake of statistical methodology depends less on formal recommendations than on usability, perceived utility, and community practice.

stat.OT↗
arXiv2026

Exact analysis of a split--merge queue with latent Erlang-factor dependent subtask times

This paper studies a two-server split--merge queue with positively dependent subtask service times modeled through a latent-factor bivariate Erlang construction. An exact characterization of the split--merge completion time is obtained, including explicit formulas for its first two moments and the resulting mean waiting time. Under fixed marginal service-time distributions, independence is shown to stochastically increase the completion time and hence overestimate mean waiting time. Numerical illustrations show that this benchmark gap can be substantial.

stat.OT↗
arXiv2026

Statistical Compatibility, Refutational Information, and Acceptability

This paper develops an interpretive framework for divergence P-values and S-values within a descriptive frequentist perspective. Statistical analysis is framed as operating within idealized worlds defined by a set of assumptions and a target hypothesis, where probabilities describe the behavior of data under the model but do not assign truth values to hypotheses. Within this view, P-values are interpreted as graded indices of compatibility between the observed result and the predictions generated by the assumed model; accordingly, small P-values should not be read as indicating logical impossibility or strict inconsistency of the model itself. Building on this distinction, the paper argues that practical inference requires moving beyond the internal logic of the model toward judgments of overall acceptability, which depend not only on data-model compatibility but also on multiple contextual considerations such as subject-matter knowledge, plausibility of assumptions, data quality, usefulness, and loss - all interpreted through the competence, intentions, perceptions, and moral values of the specific analyst. S-values are therefore interpreted not as evidence against the epistemic status of the model, but as a specific form of refutational information that contributes to the broader body of information used by the analyst to judge whether a model remains acceptable for an intended practical purpose. The paper also examines the linguistic and conceptual risks associated with the language of incompatibility, distinguishes probability from rarity, and clarifies different notions of surprise - including a possible definition of Shannon-type surprise, to be distinguished from Bayesian belief revision. Overall, the article proposes a more cautious and explicit interpretation of frequentist measures, centered on model-based description, analyst responsibility, and decision acceptability.

stat.OT↗