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Record W2328442432 · doi:10.1504/ijpse.2015.075127

CHP within smart micro energy grid: optimum operation with distributed energy resources

2015· article· en· W2328442432 on OpenAlexaff
Hossam A. Gabbar, F. R. Islam, Mayn Tomal

Bibliographic record

VenueInternational Journal of Process Systems Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Energy Management
Canadian institutionsOntario Tech University
FundersOak Ridge National Laboratory
KeywordsDistributed generationSmart gridGridEngineeringInvestment (military)CogenerationEnvironmental economicsEnergy storageAutomotive engineeringPower (physics)Reliability engineeringRenewable energyElectrical engineeringElectricity generationEconomics

Abstract

fetched live from OpenAlex

A well-designed and maintained combined heat and power (CHP) plant can deliver significant economic and environmental benefits by reducing energy bills and CO2 emission. Recently, CHP is receiving increasing considerations for investment by national and international governments, as well as power grid companies to maximise the benefits from CHP. The variable demand of heat and power loads from the CHP plant and utility grid changing tariffs during the peak and off peak hours introduce more complexities to the control and the performance optimisation of CHP operation. In this paper, potential key performance indicators of CHP-based micro energy grid and the optimum utilisation of a specific grid with distributed energy sources are discussed. Optimisation algorithm is introduced to maximise the overall performance of CHP implementations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.195
Teacher spread0.188 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2015
Admission routes1
Has abstractyes

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