Network Methods for Head-dependent Hydro Power Scheduling
Bibliographic record
Abstract
. We study short-term planning of hydro power with a nonlinear objective function. Given prices on the power one seeks to maximize the value of the production over a time-horizon. By assuming a bilinear dependency on head and discharged water we prove that the objective varies concavely when one sends flow along cycles. It follows that in each set of points of equal value containing a local optimum, there is an extreme point of the feasible set. This suggests computing stationary points by using a modified minimum cost network flow code. The model also allows us to derive explicit convex lower bounding functions of the objective. We present computational results for a real-sized hydro-power system. 1 Introduction Hydro power is an important source of electricity in many countries. In Sweden it accounts for around 50% of the electricity produced. Other countries with high share of hydro power are e.g. Canada, Norway, and Switzerland. An important benefit of hydro power is that it does ...
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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 | 0.001 | 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".