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Record W3122936761

Assessing Reliability in Energy Supply Systems

2008· preprint· en· W3122936761 on OpenAlexfundno aff
Ryan McCarthy, Joan M. Ogden, Dan Sperling

Bibliographic record

VenueeScholarship (California Digital Library) · 2008
Typepreprint
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
FundersDivision of Graduate EducationNatural Resources CanadaNational Science FoundationU.S. Department of TransportationCalifornia Department of TransportationConocoPhillipsUniversity of California, DavisPetrobrasU.S. Department of EnergyU.S. Environmental Protection Agency
KeywordsReliability (semiconductor)Renewable energyElectricityEnergy supplyMains electricityReliability engineeringComputer scienceCogenerationEnergy carrierEnvironmental economicsRisk analysis (engineering)Energy (signal processing)Electricity generationEngineeringEconomicsBusinessPower (physics)
DOInot available

Abstract

fetched live from OpenAlex

Reliability has always been a concern in the energy sector, but concerns are escalating as energy demand increases and the political stability of many energy supply regions becomes more questionable. But how does one define and measure reliability? We introduce a method to assess reliability in energy supply systems in terms of adequacy and security. It derives from reliability assessment frameworks developed for the electricity sector, which are extended to include qualitative considerations and to be applicable to new energy systems by incorporating decision-making processes based on expert opinion and multi-attribute utility theory. The method presented here is flexible and can be applied to any energy system. To illustrate its use, we apply the method to two hydrogen pathways: (1) centralized steam reforming of imported liquefied natural gas with pipeline distribution of hydrogen, and (2) on-site electrolysis of water using renewable electricity produced independently from the electricity grid. (c) 2006 Elsevier Ltd. All rights reserved.

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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.792
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0160.010
Open science0.0040.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.002

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 head0.056
GPT teacher head0.305
Teacher spread0.249 · 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 designObservational
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
Published2008
Admission routes1
Has abstractyes

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