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Record W4408378983 · doi:10.1051/e3sconf/202561901012

Management and Control of a Fuel Cell, Solar Photovoltaic, and Battery Energy Storage System Linked to a DC Nano Grid

2025· article· en· W4408378983 on OpenAlexaff
Patil Mounica, R. Muniraj, Gaurav Sethi, V. Lakshmi Devi, S. Shanmuga Priyan, M. Mahesh

Bibliographic record

VenueE3S Web of Conferences · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsHorizon College and Seminary
Fundersnot available
KeywordsPhotovoltaic systemEnergy storageBattery (electricity)Fuel cellsGridAutomotive engineeringSolar cellElectrical engineeringPhotovoltaicsBattery storageComputer scienceEnvironmental scienceEngineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

The electric grid can be connected to non-conventional energy sources such as solar cells and fuel cells to effectively meet the distribution- side load requirements. The electricity grid has started incorporating more battery-powered energy storage systems (BESS), which increases the demand for energy management systems. In this study, we looked at various different PV operating modes, as well as an FC-based DC Nano Grid that was powered by BESS, and a recommendation towards an efficient energy management system are all explored. The primary benefit is that it maintains a consistent direct current (DC) voltage across the grid. The Simulink/MATLAB software is used extensively in these simulated case studies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.772
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.003
GPT teacher head0.168
Teacher spread0.164 · 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 teacher head, 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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