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Record W4414642673 · doi:10.32920/30251581.v1

Energy, Exergy and Economic (3E) Analyses of Thermal Power Energy Systems for a Micro-Scale Community in Okotoks, Alberta, Canada

2025· preprint· en· W4414642673 on OpenAlexaboutno aff
Roshaan Mudasar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsPayback periodExergyElectricity generationRenewable energyCost of electricity by sourceOrganic Rankine cycleWork (physics)Energy consumptionElectricityEnergy conservation

Abstract

fetched live from OpenAlex

<p dir="ltr">Organic Rankine cycles (ORC) along with renewable energy are an energy solution for a community. The promising features are its modular nature and less dependency on the main grid. Such system is studied to understand the fulfilment of year-round energy needs like district heating, district cooling, and domestic hot water. Solar energy is utilised to run the ORC to generate the required power and cogenerate heat. ORC is also combined with the heat pump (HP) cycle and absorption refrigeration cycle (ARC) to make a comparison for better thermal performance. The prime objective of the study is to achieve power sufficiency where power generation meets the power consumption requirements. The combined cooling, heating, and power generation (CCHP) system is studied based on energy, exergy, and financial analyses for a regional community called Drake Landing Solar Community (DLSC) in Okotoks, Alberta, Canada. For an ORC-ARC system, the annual net work output is 71313 kWh compared to an ORC-HP system, which resulted in 15773 kWh. The levelized cost of electricity is 2.5 US$/kWh for the ORC-ARC system, whereas this cost is 11.55 US$/kWh for the ORC-HP system. The simple payback period is shorter for the ORC-ARC system at 19.7 years, and the discounted payback period is 37.5 years. Results revealed that the ORC-ARC system is a preferred choice to fulfil summer and winter energy requirements for a small community like DLSC.</p>

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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.

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

Citations0
Published2025
Admission routes1
Has abstractyes

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