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Record W1992970170 · doi:10.1109/tsg.2014.2302396

An Optimal Energy Storage Control Strategy for Grid-connected Microgrids

2014· article· en· W1992970170 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 · 2014
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMathematical optimizationEnergy storageComputer scienceRenewable energyEnergy managementDemand responseSmoothingSmart gridGridElectricityElectricity generationEnergy (signal processing)Power (physics)EngineeringMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents an online optimal energy/power control method for the operation of energy storage in grid-connected electricity microgrids. The approach is based on a mixed-integer-linear-program optimization formulated over a rolling horizon window, considering predicted future electricity usage and renewable energy generation. Performance objectives include electricity usage cost, battery operation costs, and utility oriented goals related to the peak demand and load smoothing. A robust counterpart formulation of the optimization problem is also proposed to handle uncertainty in energy demand/generation prediction in a computationally efficient way. Further reduction in the computations is achieved by employing variable time steps and relaxing binary constraints. A series of simulations demonstrate the effectiveness of various features of the proposed energy/power management methodology in different scenarios.

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 categoriesMeta-epidemiology (narrow)
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 score0.985
Threshold uncertainty score1.000

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 head0.007
GPT teacher head0.200
Teacher spread0.194 · 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