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Record W4302379523 · doi:10.48550/arxiv.1711.04175

Optimal Subhourly Electricity Resource Dispatch Under Multiple Price\n Signals With High Renewable Generation Availability

2017· preprint· W4302379523 on OpenAlexfundno aff
David P. Chassin, Sahand Behboodi, Ned Djilali

Bibliographic record

VenuearXiv (Cornell University) · 2017
Typepreprint
Language
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsnot available
FundersSLAC National Accelerator LaboratoryPacific Institute for Climate SolutionsUniversity of VictoriaCalifornia Energy CommissionU.S. Department of Energy
KeywordsEconomic dispatchRenewable energyComputer scienceElectricityElectric power systemOptimal controlResource (disambiguation)InterconnectionMathematical optimizationElectricity generationTrajectoryPower (physics)EngineeringTelecommunicationsElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

This paper proposes a system-wide optimal resource dispatch strategy that\nenables a shift from a primarily energy cost-based approach, to a strategy\nusing simultaneous price signals for energy, power and ramping behavior. A\nformal method to compute the optimal sub-hourly power trajectory is derived for\na system when the price of energy and ramping are both significant. Optimal\ncontrol functions are obtained in both time and frequency domains, and a\ndiscrete-time solution suitable for periodic feedback control systems is\npresented. The method is applied to North America Western Interconnection for\nthe planning year 2024, and it is shown that an optimal dispatch strategy that\nsimultaneously considers both the cost of energy and the cost of ramping leads\nto significant cost savings in systems with high levels of renewable\ngeneration: the savings exceed 25% of the total system operating cost for a 50%\nrenewables scenario.\n

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow)
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 score0.535
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
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 head0.040
GPT teacher head0.170
Teacher spread0.130 · 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; both teacher heads agree on what is shown here.

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
Published2017
Admission routes1
Has abstractyes

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