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Record W2625034686 · doi:10.5547/01956574.39.1.dolm

Ontario's Auction Market for Financial Transmission Rights: An Analysis of its Efficiency

2017· article· en· W2625034686 on OpenAlexaffabout
Derek Olmstead

Bibliographic record

VenueThe Energy Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCommon value auctionClearingMicroeconomicsEconomicsElectricityBusinessPaymentElectricity marketDutch auctionReverse auctionForward auctionRevenue equivalenceAuction theoryFinance

Abstract

fetched live from OpenAlex

Financial transmission rights (FTR) are financial products that entitle their holder to receive a payment based on the degree of congestion in a transmission system. In many liberalized electricity markets, FTR are sold at auction by the local electricity system operator. This paper addresses several questions about the performance of FTR auctions in Ontario's restructured electricity market, including whether auction market clearing prices approximate realized payouts and whether there is any evidence that the competitiveness of auctions, as measured by the number of bidders, affects the forward market unbiasedness or informational efficiency of the auctions. The paper finds that the auction process is inefficient in the sense that market clearing prices are substantially and systematically lower than realized payouts, resulting in substantial transfers away from consumers. However, there is some evidence that the auction market is more efficient when there are three or more bidders.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.701

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.007
GPT teacher head0.214
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations8
Published2017
Admission routes2
Has abstractyes

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