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Record W2119196062 · doi:10.1109/pecon.2012.6450334

Analyzing the economic potential for DG CHP systems at the University of Guelph

2012· article· en· W2119196062 on OpenAlexaffabout
Andrew B. Northmore, Ehab F. El‐Saadany

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCogenerationElectricityElectricity generationEnvironmental economicsEconomic dispatchDistributed generationCost of electricity by sourceSustainable energyBiomass (ecology)Energy modelingBusinessEnvironmental scienceRenewable energyElectric power systemComputer sciencePower (physics)EngineeringEconomicsEfficient energy useElectrical engineering

Abstract

fetched live from OpenAlex

Economic modeling of distributed generation (DG) systems has become an important area of research with the modern push towards greener and more sustainable electricity generation practices as any proposal without a solid business case is bound to flounder with the state of the global economy. This paper assessed the state-of-the-art in DG economic modeling and based on this developed a model to determine the economic suitability of DG projects in Ontario, Canada. This model was applied to the energy profile of the University of Guelph in Guelph, Ontario and it was found that using 2× 5MW biomass combined heat and power (CHP) DGs and selling electricity to the grid will save them $2.56 million annually on energy costs. This paper recommends that further research is done in optimizing between local distribution companies (LDCs) and DG operator economic benefits of DGs, applying risk and uncertainty to economic modeling, analyzing the cost of biomass pellets in Ontario, and doing hour-by-hour modeling of the University of Guelph's energy usage to verify the findings of this paper.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.718
Threshold uncertainty score0.156

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 head0.006
GPT teacher head0.170
Teacher spread0.164 · 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 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
Published2012
Admission routes2
Has abstractyes

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