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Record W6929206942 · doi:10.48336/vkfq-bp51

Efficient water-based electricity strategies to reduce the number of switching operations in a smart grid

2025· article· en· W6929206942 on OpenAlexaff

Bibliographic record

VenueMemorial University Research Repository (Memorial University) · 2025
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neuropharmacology Research
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSmart gridFlexibility (engineering)Reliability (semiconductor)Renewable energyElectric power systemGridTransmission (telecommunications)Function (biology)Energy management system

Abstract

fetched live from OpenAlex

The increasing complexity of modern power systems, driven by the integration of renewable energy sources and the need for enhanced operational efficiency, has led to the widespread adoption of Transmission Switching (TS) as a cost-reduction strategy. TS optimizes the configuration of the transmission network by selectively switching transmission lines, thereby reducing the overall operational costs. However, this approach comes with significant drawbacks. The frequent switching operations required can degrade critical system components, particularly circuit breakers (CBs), leading to a shorter lifespan, higher maintenance and repair costs, increased likelihood of line outages, and a greater probability of load shedding. Moreover, these issues can collectively undermine the reliability of the entire power system. To address these challenges, this thesis presents a novel congestion management framework integrated within the Security-Constrained Unit Commitment (SCUC) problem. The primary objective of the proposed framework is to minimize the number of TS operations necessary to manage congestion, thereby mitigating the adverse effects on CBs and enhancing the overall reliability of the power grid. The framework introduces a grid-connected water-power system that leverages a fuel cell-based renewable energy source, coupled with a hydrogen storage tank, to provide additional flexibility in managing grid congestion. By utilizing this water-power system, the framework reduces the need for frequent TS operations, thus alleviating the associated strain on the transmission network. Additionally, the thesis addresses the inherent uncertainties in grid operations, particularly those related to fluctuating renewable energy output and unpredictable demand. To this end, an uncertainty-based Unscented Transform (UT) function is incorporated into the SCUC framework. This function enhances the robustness of the proposed methodology, ensuring that it remains effective under a wide range of operational scenarios and uncertainties. The proposed framework is validated through comprehensive simulations conducted on two standard test systems: a 6-bus and a 118-bus IEEE grid. These simulations are performed using Bender’s decomposition method in GAMS software, a widely recognized tool for large-scale optimization problems in power systems. The results from these simulations demonstrate that the proposed strategy significantly reduces line congestion and the number of TS operations required. Specifically, the framework achieves a 77% reduction in switching operations for the 6-bus system and a 45% reduction for the 118-bus system. These reductions not only extend the lifespan of CBs but also lead to substantial decreases in operational costs, thereby offering a more sustainable and cost-effective solution for modern power systems. The findings of this research contribute to the ongoing development of more resilient, efficient, and sustainable power systems, particularly in light of the increasing reliance on renewable energy sources. The proposed framework offers a viable path forward for grid operators seeking to balance cost efficiency with system reliability, all while integrating more renewable energy into the power grid.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.041
GPT teacher head0.328
Teacher spread0.288 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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