Bibliographic record
Abstract
Power tracing is the task of disaggregating the power injection of a generator (or a load) into a sum of constituent components that can unambiguously be attributed to loads (generators) and losses. Applications of power tracing range the broad spectrum of: transmission services pricing, loss allocation in distribution networks, fixed-cost allocation, modelling bilateral transactions, and financial storage rights. This paper develops an analytical approach to power tracing leveraging elementary circuit laws. The method is rigorous from a system-theoretic vantage point, and it yields unambiguous results that are consistent with constitutive principles that describe the steady-state behaviour of power networks. Moreover, it can be implemented with limited computational burden, applies to networks with arbitrary topologies, and preserves the coupling between activeand reactive-power injections. Numerical experiments indicate that given a solved power-flow solution, disaggregations can be computed for a test system with 2383 buses, 327 generators, and 2056 loads in 4.34 s on a personal computer, hence establishing computational scalability. Furthermore, applications are demonstrated in distribution and transmission networks with case studies focused on quantifying the impact of distributed generation on loss allocation and extracting nodal contributions to bilateral transactions, respectively.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".