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Record W3121215414

Valuing Electricity Transmission: The Case of Alberta

2012· article· en· W3121215414 on OpenAlexaffabout
Joseph A. Doucet, Andrew N. Kleit, Serkan Fikirdanis

Bibliographic record

VenueSSRN Electronic Journal · 2012
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectricity marketElectricityIncentiveTransmission (telecommunications)Investment (military)GridElectric power transmissionValue (mathematics)BusinessEconomicsIndustrial organizationElectricity priceMicroeconomicsComputer scienceEngineeringTelecommunicationsGeography
DOInot available

Abstract

fetched live from OpenAlex

Transmission economics remains a difficult area. Market forces alone are generally felt to be insufficient for signaling investment due to appropriability problems and the challenges of “parallel (loop) flow.” Further, there is no generally recognized method of estimating the value of transmission expansion. Here we extend the approach of Kleit and Reitzes (2008) to the nearly isolated electricity grid of Alberta, essentially eliminating the problems of parallel flow. With this model, we are able to determine the value of electricity flows to and from Alberta, as well as the transactions costs associated with those flows. We are also able to value the impact of transmission expansion in Alberta. We apply this model to a project currently being developed, the Montana–Alberta Tie Line (MATL). Depending on the relevant elasticity of supply, we find that the MATL owners will be able to appropriate between 80% and 96% of the societal value of their new transmission capacity. This high level of appropriability is in contrast to the conclusions of most of the literature in this area, suggesting that market forces may in some instances be effective in providing incentives for optimal transmission investment.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.683
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 integrity0.0000.001
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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2012
Admission routes2
Has abstractyes

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