On the Impacts of Multi-Agent Transactive Energy in Distribution Networks - Part 1: GTSPF Construction and ETSim Integration
Bibliographic record
Abstract
To contribute the global ongoing energy transition by offering a new holistic approach to model and simulate local energy markets and its impacts in the network, a quasistatic Generic Time-Series Power Flow (GTSPF) is developed to be integrated in the innovative transactive energy ETSim platform. The GTSPF tool is designed in Python environment while utilizing OpenDSS as system solver. The primary objective of the integrated platform is to concentrate on the advancement and testing of decentralized and low-carbon electrical systems. Specifically, it aims to simulate transactional exchanges associated with electricity generation from distributed energy resources (DERs) and analyze the resulting impacts and advantages on the distribution network. This project plays a significant role in investigating methods to achieve dynamic energy exchanges that are customized to the requirements of participating agents, as well as market and technical conditions of the electricity grid.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".