A Benchmark Library for Distributed Power System Analysis and Optimization
Bibliographic record
Abstract
DPLib is an open-source benchmark library created to support research and development in distributed power system analysis and optimization. Unlike centralized tools such as MATPOWER and PGLib, no general purpose, reproducible data library package currently exists for distributed power system studies. DPLib, available at \href{https://github.com/LSU-RAISE-LAB/DPLib.git}{GitHub}, fills this gap by providing 40 multi-region benchmark test cases ranging from 5 buses to 20758 buses, along with a graph-based partitioning toolkit that converts MATPOWER-compatible systems into distributed regional datasets. The toolkit generates standardized \texttt{.mat}, \texttt{.csv}, and \texttt{.m} files, regional MATPOWER version 2 cases, local and global bus mappings, generator and cost assignments, explicit inter-regional tie-line records, and bus-to-region partition maps. It supports unweighted, electrically weighted, and user-defined partitions, and is compared with METIS, KaFFPa, and an IPA-inspired baseline. DPLib also provides ADMM-based distributed DC and AC OPF solvers for validation. Numerical studies report partitioning sensitivity, centralized run times, distributed OPF iterations, run times, and optimality gaps. These results establish DPLib as a reproducible data layer for distributed power system research.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.009 |
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".