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Record W2087698011 · doi:10.1109/iecon.2013.6699455

MILP-based rolling horizon control for microgrids with battery storage

2013· article· en· W2087698011 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMcMaster University
FundersCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsMicrogridRenewable energyComputer scienceBattery (electricity)Controller (irrigation)GridEnergy storageFlow batteryTime horizonEnergy managementPower (physics)Automotive engineeringMathematical optimizationEngineeringEnergy (signal processing)Electrical engineering

Abstract

fetched live from OpenAlex

An energy management system is proposed for a grid-connected microgrid with on-site battery storage and renewable energy sources. The system controls power flow between the microgrid and grid in order to effectively utilize renewable energy and maximize economic benefits for the customer and the utility operator. The power flow is optimized by formulating and solving a mixed-integer-linear-program optimization over a rolling horizon window. Multiple objectives concerning economic benefits, cost of battery operation and grid power profile shaping are built into the optimization. So called battery incremental red-zone power rates are among unique battery management features of this formulation. Careful consideration is also given to reducing the computations so the controller can run on an embedded computer in real time. Simulation results highlight the effectiveness of various novel aspects of the proposed controller.

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: none
Teacher disagreement score0.923
Threshold uncertainty score0.507

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 head0.003
GPT teacher head0.151
Teacher spread0.149 · 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

Quick stats

Citations20
Published2013
Admission routes2
Has abstractyes

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