Co-Simulation Framework OpenDSS-Python to Consider Distribution Grid Constraints in a Transactive Energy System
Bibliographic record
Abstract
Recent developments in Transactive Energy Systems (TES) unveiled innovative electricity markets that improve the economic efficiency of the grid. These markets must consider the grid's operative constraints to support the transactions with a satisfactory quality of service. In this regard, this paper presents a co-simulation OpenDSS-Python approach to evaluate the feasibility of TES mechanisms, given the technical limits of the distribution grid. The proposed approach takes the TES aggregated demand of residential customers as input and derives the active and reactive power demand using a probabilistic conversion module. Then, the voltage profiles and line losses are calculated by solving dynamic power flows. The outcomes are contrasted with predefined thresholds and enclosed in a transactive report for the Distribution System Operator (DSO). As a case study, the IEEE-33-bus distribution system is assessed with the proposed co-simulation framework. The results evidence the impact of TES in the distribution system operation for the DSO to authorize the transactions. Transactive reports also present relevant information for grid expansion planning since they reveal demand coordination efficacy.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".