OpenATE: A Distributed Co-simulation Engine for Transactive Energy Systems
Bibliographic record
Abstract
Transactive Energy Systems (TES) have created appealing opportunities to improve grid operation and decentralize management. TES advantages have been theorized for different market models at all grid scales. However, it is imperative to develop proofs-of-concept simulations and test the new management schemes before implementing them with actual users. In this regard, this paper presents a modular co-simulation engine for TES forward markets. The engine comprises an event-oriented software architecture for controlling simulations and an automatic modeling process for creating case studies. An orchestration tool helps reduce the overhead in distributed computer clusters. In addition, the proposed architecture involves a database manager for reporting purposes. This co-simulation engine was tested using a straightforward TES local market with a coalitional game configuration. In such a scenario, several residential users participate as followers communicating to a single demand aggregator. Simulation results show the impact of computer cluster configuration on market clearing times. The methodical simulation insights will help TES designers and regulators to develop robust mechanisms and analyze implementation scenarios.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".