MétaCan
Menu
Back to cohort

Optimal energy management for cooperative microgrids with renewable energy resources

2013· article· en· W1984757241 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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsRenewable energyComputer scienceEnergy managementDemand responseExploitMicrogridMathematical optimizationScheduling (production processes)GridEnergy (signal processing)Distributed computingElectricityEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, we present an optimal energy management framework for a cooperative network of heterogeneous microgrids (MGs) where energy exchange among connected MGs is allowed to exploit the fluctuations of stochastic energy sources and demands. A multi-objective function is introduced that seeks to achieve an efficient tradeoff between low operation cost and good energy service for customers. The objective function captures the total cost of power exchange with the main grid, the startup and shutdown costs, the operating cost of distributed generators (DGs), the payment for demand response load, the penalty costs for involuntary load curtailment, and renewable energy spillage. We propose to employ the scenario-based two-stage stochastic optimization approach to deal with the uncertainties of renewable energy resources and load demand in the energy scheduling problem. The efficacy of the proposed energy management solution is demonstrated via numerical results.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.692
Threshold uncertainty score0.907

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 head0.004
GPT teacher head0.165
Teacher spread0.161 · 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

Citations72
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicSmart Grid Energy ManagementFrench-language works237,207