Optimization of Micro Gas Turbine Based Hybrid Systems for Remote Off-grid Communities
Bibliographic record
Abstract
Inherent fuel flexibility of micro gas turbine (MGT) makes the engine a promising energy solution to remote Canadian communities that are not connected to the North American electricity grid, where bio-oils derived from locally available bio-mass may be utilized to meet local power and heat demands to reduce fossil fuel consumption. The switch to bio-oils enabled by MGTs reduces not only carbon footprints but also operating expenses due to high transportation costs of fossil fuels. However, MGT efficiencies are greatly reduced at partial loads. This work investigates the feasibility of addressing MGT efficiency drops at partial loads by incorporating MGT with a Battery Energy Storage System (BESS) to form a hybrid system so that the MGT can be operated at near full power at all times for better efficiencies. In this study, a daily power demand profile of a typical Canadian household is adopted for optimizing battery size and MGT operating strategies. By optimizing MGT daily start time and the engine's threshold partial load factor, the specific fuel consumption and battery size can be minimized for a specific number of households on a micro-grid supported by the MGT-based hybrid power system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".