Diesel Plant Sizing and Performance Analysis of a Remote Wind-Diesel Microgrid
Bibliographic record
Abstract
This paper introduces an energy-flow model developed for performance analysis and unit sizing of an autonomous wind-diesel microgrid. A remote community in Canada is used as the study system, for which a medium penetration wind power plant has been integrated into a system served by a diesel plant with three equally sized diesel generators. Based on field observations and monitored data for almost two years of operation, an energy-flow model is developed which incorporates operating constraints and control requirements of the autonomous wind- diesel system. The model is employed to analyze the interaction of wind and diesel power plants in order to identify alternative unit sizing approaches that improve wind-energy absorption rate of the wind plant, fuel savings and overall efficiency of the diesel plant. Optimization criteria for unit sizing of the diesel plant in the presence of the wind farm are discussed and system performance for several configurations based on multiple units with reduced-size diesel unit are investigated. The simulation results from the energy-flow model for two operation scenarios are compared with the field observations and an optimum combination of multiple diesels with reduced-size units is suggested.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".