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Comparative analysis among three solar energy-based systems with hydrogen and electrical battery storage in single houses

2022· preprint· en· W4283800758 on OpenAlexaffabout
Leila Abdolmaleki, Umberto Berardi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBattery (electricity)Hydrogen storageEnergy storagePhotovoltaic systemSolar energyBattery storageElectrical engineeringEnvironmental scienceHydrogenEngineering physicsEngineeringChemistryPhysicsThermodynamicsPower (physics)

Abstract

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This study investigates and compares the economic analysis of renewable energy-based systems incorporating photovoltaic (PV) panels, electrolyzer, fuel cell (FC), and a hydrogen tank for single houses in North America. Three systems consisting of PV/battery bank, PV/hydrogen, and PV/battery bank/hydrogen are simulated and optimized using the software HOMER. In this study, the electrolyzer produces green hydrogen using to the power obtained by the PV array; the generated hydrogen is stored in a hydrogen tank and powers the FC. Based on the results, the integration of 12 kW PV panels, 2.50 kW FC, 10 kW electrolyzer, 50 kg hydrogen tank, 2 kW converter, and 24 kWh of batteries is found to be the best configuration in Toronto, as it leads to the minimum net present cost (NPC) and levelized cost of energy (COE). Results show that while the battery bank can be used instead of the electrolyzer, FC, and hydrogen tank, the large batteries resulted in the highest NPC due to their high investment cost. Finally, the study is extended to Miami and Washington in the U.S., to check the validity of the conclusions with higher average annual solar radiation and to find their costeffective configurations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.071
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.022
GPT teacher head0.227
Teacher spread0.205 · 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.

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

Citations3
Published2022
Admission routes2
Has abstractyes

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