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Record W2558178876 · doi:10.1109/tsg.2016.2633280

Considering Thermodynamic Characteristics of a CAES Facility in Self-Scheduling in Energy and Reserve Markets

2016· article· en· W2558178876 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

VenueIEEE Transactions on Smart Grid · 2016
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCompressed air energy storageElectricityScheduling (production processes)Energy storageSpinningRevenueEnvironmental scienceComputer scienceEngineeringThermodynamicsPhysicsEconomicsMechanical engineeringElectrical engineeringOperations managementFinancePower (physics)

Abstract

fetched live from OpenAlex

The efficiency of a compressed air energy storage (CAES) facility deviates significantly from its nominal value depending on its thermodynamics and operational conditions. Thus, the thermodynamic characteristics of the facility should be incorporated in its scheduling to model the variation in efficiency. This paper proposes a self-scheduling approach for a CAES facility that participates in energy, spinning, and non-spinning reserves markets. Considering the thermodynamic characteristics of the facility, the limitations imposed on the facility are modeled when devising operations schedules. Thus, the model leads to a more realistic view of the revenues. Numerical simulations are provided using the historical hourly energy and reserve prices of the ERCOT electricity market for years 2011 to 2015. The results are compared with those of derived from by using the conventional model with constant efficiency parameters.

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 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: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.693

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 head0.009
GPT teacher head0.191
Teacher spread0.181 · 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