Load-Flow Time-Series Simulation of a Distribution Grid with PV Modules and Voltage Regulation
Bibliographic record
Abstract
Load-flow time-series (LFTS) simulation is a common type of simulation used for assessing the power flow and the system voltage over a large time window for renewable energy integration studies. This paper presents a JavaScript-based execution of LFTS simulations in EMTP software and analyzes the effect of photovoltaic (PV) generation, volt-var control, and voltage regulators on the grid voltage for the IEEE-34 benchmark distribution grid. This study considers realistic profiles for the load demand and the PV generation. The accuracy of the LFTS simulation results is verified by comparison with time-domain simulation results. The results show that a combined usage of voltage regulators and volt-var control can help to mitigate undervoltage issues on a highly loaded node if sufficient PV generation is installed. Volt-var control provides additional support for the grid voltage without significantly impacting the active power flow at the distribution grid's connection point with the transmission grid.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".