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Record W3199433642 · doi:10.32920/ryerson.14643876.v1

A life-cycle based decision-making framework for electricity generation system planning

2021· preprint· en· W3199433642 on OpenAlexaboutno aff
Steven James Norrie

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsStakeholderElectricity generationFlexibility (engineering)Environmental economicsElectricityGreenhouse gasLife-cycle assessmentContext (archaeology)Renewable energyReliability (semiconductor)Operations researchComputer scienceEngineeringProduction (economics)EconomicsPower (physics)

Abstract

fetched live from OpenAlex

This thesis proposed a decision-making framework to consider multiple objectives in long-term planning situations, and asserts that planning for power generation systems should consider relevant environmental and/or social objectives at the same decision level as traditional economic or reliability objectives. The framework was applied to the case study of long-term planning for Ontario's power generation system. The framework integrates life-cycle based information and decision-maker preferences toward multiple objectives in the context of sustainable development. Six decision criteria evaluated as measures of the objectives include life-cycle cost of electricity, a system flexibility indicator, demand reduction, land use requirements, greenhouse gas emissions, and air emissions. Stakeholder values were derived through questionnaires. Three hypothetical electricity generation scenarios were compared to test the decision-making framework. The results of the application indicated that the scenario which included aggressive renewable energy development and demand reduction was favourable, even given the tradeoffs of reliability and costs.

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)0.001
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: Methods · Consensus signal: none
Teacher disagreement score0.630
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.293
Teacher spread0.270 · 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
GenreMethods

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

Citations0
Published2021
Admission routes1
Has abstractyes

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