Transmission and distribution co‐simulation: a review and propositions
Bibliographic record
Abstract
With the growing trend of emerging new technologies in distribution networks, such as wind turbines, solar panels, electric vehicles, and distributed generations, the passive distribution systems may become ‘active’ which requires more study in the area of integrated transmission and distribution systems (ITDSs) and corresponding bilateral interactions. To solve this problem, most of the studies connect distinct simulators to create a novel co‐simulation framework for ITDS. In this study, the authors present a literature survey of existing ITDS co‐simulation frameworks along co‐optimisation in ITDS. These frameworks are categorised on multiple characteristics, such as simulation tools, synchronisation methods, and research topics. Furthermore, they propose a software platform that is focused on the integrated generation, transmission, distribution, and customer systems (IGTDCSs). The proposed framework also comprises several technological dimensions such as stochastic optimisation, high‐performance computing, and high‐level design software architecture for planning integrated and flexible power networks and optimising their technological trajectories and operational functioning considering uncertainties. By developing a prototype informed with software engineering and complex system design approaches, they will demonstrate the relevance of a unified vision of IGTDCS simulation in a minute‐by‐minute horizon, a vision that may later benefit electromagnetic transient simulation or stability co‐simulation tools, in horizons from the microsecond to the second.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".