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Simulation of a decentralized floating offshore wind hydrogen production system with hydro-pneumatic energy storage and subsea isobaric hydrogen storage

2024· article· en· W4405536970 on OpenAlexaff
Zecheng Zhao, Wei Xiong, Hu Wang, Tonio Sant, Rupp Carriveau, David S.‐K. Ting, Zhiwen Wang

Bibliographic record

VenueJournal of Physics Conference Series · 2024
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsSubseaHydrogen storageSubmarine pipelineEnvironmental scienceHydrogen productionProduction (economics)Marine engineeringPetroleum engineeringEnergy storageOffshore wind powerIsobaric processHydrogenWind powerEngineeringChemistryPhysicsElectrical engineeringGeotechnical engineeringPower (physics)Thermodynamics

Abstract

fetched live from OpenAlex

Abstract Hydrogen production using offshore wind power is a promising solution for producing clean fuels in remote areas. However, the intermittency of offshore wind power poses significant challenges to Proton exchange membrane (PEM) water electrolysis systems. Besides, there is generally an imbalance between hydrogen production and hydrogen demand. In this study, hydro-pneumatic electricity energy storage and subsea isobaric hydrogen storage are integrated into the decentralized offshore wind hydrogen production system. The hydro-pneumatic energy storage unit (HPES) is used for mitigating the intermittency and fluctuation of wind power, thereby prolonging the lifespan of PEM electrolyzers. Subsea isobaric hydrogen storage unit is used for replacing conventional isochoric hydrogen storage. In this study, simulation models of a decentralized offshore wind hydrogen production system with various configurations are established with the software Simcenter Amesim 2021.1. The results show that an 83% reduction in the on/off operation can be achieved with the help of hydro-pneumatic electricity energy storage. The isobaric and isothermal storage of compressed hydrogen can be achieved with subsea isobaric hydrogen storage, saving compression energy and facilitating thermal management. In terms of the investigated decentralized offshore wind hydrogen production system, the amount of produced hydrogen is increased by less than 1% by integrating hydro-pneumatic energy storage and subsea isobaric hydrogen storage.

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 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 score0.379
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.226
Teacher spread0.210 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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