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A Flexible System-level Hydrogen Transportation System Operating Structure Interacting with Urban Transportation System

2022· article· en· W4313549720 on OpenAlexaff
Elahe Sahraie, Innocent Kamwa

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSolverComputer scienceReliability (semiconductor)Automotive engineeringPower (physics)Engineering

Abstract

fetched live from OpenAlex

Electrification of hard-to-abate sectors through green hydrogen is a promising strategy for achieving the Paris Agreement's decarbonization targets. However, hydrogen transportation problem remains a pressing concern in a system-level operation of hydrogen energy system (HES) in an integrated electric power and hydrogen system (IPHS). This paper presents a system-level operation structure for hydrogen transportation system (HTS). Proposed structure is intended to facilitate the coordination between HTS and the remaining parts of an IPHS by using a decomposed operation structure, which allows for different timescales to be set for each party. In the proposed structure, hydrogen tube trailers (HTs) are used to deliver hydrogen across the urban transportation system (UTS) using an extended version of a vehicle routing problem (VRP) coupled with a new set of constraints for incorporating the traffic density of roads and their availability. Furthermore, certain constraints have been considered to align with the priority of supplying sensitive loads. As part of the proposed HTS operating structure, a trade-off between system reliability and cost-affordability is also managed by assigning the adjustable weights to each parties' representative in HTS operating objective. The proposed structure is intended to minimize the operating costs of HTS in an interaction with IPHS as well as minimize the delay costs as a penalty in the event of hydrogen load loss. The proposed HTS operating structure is formulated in the form of a mixed integer linear problem and is then solved by Gurobi solver using YALMIP toolbox in MATLAB.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.012
GPT teacher head0.205
Teacher spread0.193 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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