A concise, approximate representation of a collection of loads described\n by polytopes
Bibliographic record
Abstract
Aggregations of flexible loads can provide several power system services\nthrough demand response programs, for example load shifting and curtailment.\nThe capabilities of demand response should therefore be represented in system\noperators' planning and operational routines. However, incorporating models of\nevery load in an aggregation into these routines could compromise their\ntractability by adding exorbitant numbers of new variables and constraints. In\nthis paper, we propose a novel approximation for concisely representing the\ncapabilities of a heterogeneous aggregation of flexible loads. We assume that\neach load is mathematically described by a convex polytope, i.e., a set of\nlinear constraints, a class which includes deferrable loads, thermostatically\ncontrolled loads, and generic energy storage. The set-wise sum of the loads is\nthe Minkowski sum, which is in general computationally intractable. Our\nrepresentation is an outer approximation of the Minkowski sum. The new\napproximation is easily computable and only uses one variable per time period\ncorresponding to the aggregation's net power usage. Theoretical and numerical\nresults indicate that the approximation is accurate for broad classes of loads.\n
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".