On the Impacts of Multi-Agent Transactive Energy in Distribution Networks - Part 2: Integrated ETSim Results and Findings
Bibliographic record
Abstract
The integrated quasi-static Generic Time-Series Power Flow (GTSPF) and transactive energy ETSim platform aims to simulate transactional exchanges associated with electricity generation from distributed energy resources (DERs) and analyze the resulting impact and advantages on the distribution network. In this work, the integrated ETSim platform is tested by using the IEEE 13 node test feeder, six different scenarios are evaluated which vary in terms of DERs penetration, tariff type, and DERs optimization objectives. The analysis of these scenarios has demonstrated that when tariff structures and market frameworks promote the efficient utilization of DERs and encourage energy exchange between the grid and DERs owners, there can be both positive and negative impacts. However, effective management of network stress, ensuring a stable and reliable electricity supply, it becomes possible to avoid network upgrades and brings economic benefits to both grid operators and users. The analysis offers an evaluation of multi-agent transactive energy systems in distribution networks considering the spatial and temporal correlations of multiple DERs for a proper operation of power system with high penetration of renewable energies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".