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Record W2898489418 · doi:10.1049/iet-gtd.2018.5105

Multi‐stage bi‐level linear model for low carbon expansion planning of multi‐area power systems

2018· article· en· W2898489418 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET Generation Transmission & Distribution · 2018
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsHydro-QuébecUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsStage (stratigraphy)Carbon fibersPower (physics)Computer scienceMathematical optimizationEnvironmental scienceMathematicsGeologyAlgorithmPhysicsThermodynamics

Abstract

fetched live from OpenAlex

This study proposes a multi‐stage expansion model for coordinated transmission and generation of expansion planning of a multi‐area power system (MAPS) wherein each region seeks to benefit from the changes. The proposed model adopts a bi‐level optimisation approach. In the first level, the expansion cost and carbon emissions are calculated for each region separately for expansion planning. In the second level of optimisation, the calculated cost and emission values are used as the upper limits for the cost of expansion and emissions of the regions in multi‐area expansion planning. The proposed bi‐level approach prevents one region from bearing additional expansion costs without compensatory benefits and provides advantageous collaboration for the participant regions in the MAPS expansion. The proposed linear model requires fewer computational expansion models for long‐term planning of the MAPS, but takes the uncertain generation of renewable units into account.

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.

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: none
Teacher disagreement score0.870
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.060
GPT teacher head0.275
Teacher spread0.215 · 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