Diesel Plant Sizing and Performance Analysis of a Remote Wind-Diesel Microgrid
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it