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Techno-Economic Considerations for Optimal Sizing of Isolated Hydrogen-Battery-Solar Powered Microgrids

2024· article· en· W4408281987 on OpenAlexaff
Valeria Juárez-Casildo, Anindita Golder, Ilse Cervantes, R. Huerta, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSizingBattery (electricity)HydrogenAutomotive engineeringPhotovoltaic systemSolar poweredComputer scienceElectrical engineeringEnvironmental scienceSolar energyEngineeringChemistryPower (physics)PhysicsThermodynamics

Abstract

fetched live from OpenAlex

Hydrogen and Battery Energy Storage Systems (BESS) are being widely integrated in microgrids with renewable energy storage systems. However, factors such as control strategies, capital costs, system efficiency and operation constraints have an impact on the planning of microgirds and system sizing. The effect of controller strategies, battery technologies on the system performance was studied and a sensitivity analysis has also been carried out for the system for two types of loads: single house and residential. It was found that cycle charging proved to be more effective for single house while for a residential community the load following strategy was more effetive. Also, in terms of battery chemistry Lithium Ion Nickel Managanese Cobalt Oxide (Li-Ion NMC) battery had the longest lifetime. The sensitivity analysis showed that capital cost for hydrogen electrolyzer and the solar reserve capacity had the most impact on the system sizing.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.014
GPT teacher head0.242
Teacher spread0.228 · 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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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