Battery provisioning and scheduling for a hybrid battery-diesel generator system
Bibliographic record
Abstract
Diesel generators (gensets) are commonly used to provide a reliable source of electricity in off-grid locations. Operating a genset is expensive both in terms of fuel and carbon footprint. Because genset efficiency increases with offered load, this expense can be reduced by using a storage battery to ensure that a genset always runs at full load, charging and discharging the battery as necessary. However, the cost of batteries requires us to size them parsimoniously and operate them efficiently. We, therefore, study the problem of provisioning and optimally managing a battery in a hybrid batterygenset system. To aid in sizing a battery, we analytically study the trade-off between battery size and carbon footprint. We also formulate the optimal scheduling of battery charging and discharging as a mixed-integer program, proving that it is NP-hard. We then propose a heuristic online battery scheduling scheme that we call alternate scheduling and prove that it has a competitive ratio of k 1 G / C + k 2 T u / k 1 + k 2 T u with respect to the offline optimal scheduling, where G is the genset capacity, C is the battery charging rate, k1, k2 are genset-specific constants, and Tu is the duration of a time step. We numerically demonstrate that alternate scheduling is near-optimal for four selected industrial loads.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".