Analyzing the economic potential for DG CHP systems at the University of Guelph
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".