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Record W3122344694

Modeling Alberta Power Prices Through Fundamentals

2016· article· en· W3122344694 on OpenAlexaffabout
Elham Negahdary, Tony Ware

Bibliographic record

VenueSSRN Electronic Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBiddingEconometricsRange (aeronautics)EconomicsSensitivity (control systems)Computer scienceModel riskProbability distributionMicroeconomicsOperations researchRisk managementEngineering
DOInot available

Abstract

fetched live from OpenAlex

ABSTRACT We model medium- and long-term Alberta power prices by identifying the primary price drivers and characterizing their dynamics in an engineering-based bottom-up model. This fundamental model is based on the economic theory of supply and demand. Power prices will be represented naturally while satisfying operational constraints. In view of the uncertainty around their future values, we model independent exogenous variables such as fuel prices, outages and load as stochastic processes. The model simulates bid stack by incorporating historical bidding behavior. This simulated bid stack model is an original, creative approach to modeling not just spot prices but also different risk measures and forward contracts. The interactions of simulations of different factors produce a distribution of prices, with a probability associated with each price range, rather than having a single price. While common optimization models in the literature mimic the role of a market administrator, our simulation-based model aims to combine the influences of a wide range of underlying variables in a mathematical approach. Fundamental models can potentially be used for delta analysis, scenario/sensitivity analysis, measuring risk matrixes, market-price-of-risk analyses, development of trading strategies and decision support for investments or acquisitions.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.884
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.006
GPT teacher head0.206
Teacher spread0.200 · 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
Published2016
Admission routes2
Has abstractyes

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