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Record W2772191069 · doi:10.1063/1.5014015

Smart grid integration of small-scale trigeneration systems

2017· article· en· W2772191069 on OpenAlexfundno aff
Gergana Vacheva, Hristiyan Kanchev, Nikolay Hinov

Bibliographic record

VenueAIP conference proceedings · 2017
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsnot available
FundersAgence Universitaire de la Francophonie
KeywordsCogenerationAir conditioningSmart gridDistributed generationAbsorption refrigeratorScheduleAutomotive engineeringElectricity generationEnvironmental scienceElectricityChillerElectric power systemProcess engineeringEngineeringElectrical engineeringPower (physics)Renewable energyComputer scienceMechanical engineeringRefrigeration

Abstract

fetched live from OpenAlex

This paper presents a study on the possibilities for implementation of local heating, air-conditioning and electricity generation (trigeneration) as distributed energy resource in the Smart Grid. By the means of microturbine-based generators and absorption chillers buildings are able to meet partially or entirely their electrical load curve or even supply power to the grid by following their heating and air-conditioning daily schedule. The principles of small-scale cooling, heating and power generation systems are presented at first, then the thermal calculations of an example building are performed: the heat losses due to thermal conductivity and the estimated daily heating and air-conditioning load curves. By considering daily power consumption curves and weather data for several winter and summer days, the heating/air-conditioning schedule is estimated and the available electrical energy from a microturbine-based cogeneration system is estimated. Simulation results confirm the potential of using cogeneration and trigeneration systems for local distributed electricity generation and grid support in the daily peaks of power consumption.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.892
Threshold uncertainty score0.717

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 communication0.0000.001
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 head0.024
GPT teacher head0.223
Teacher spread0.199 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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