Technical Analysis of the Impact of High Solar PV Penetration on the Stability of Bali's Power System
Bibliographic record
Abstract
The increasing penetration of solar power plants as an alternative clean energy source is an important element in the energy transition process towards Net Zero Emission (NZE). Bali, as a province with high electrical load growth and commitment to green energy development, thus this study aims to assess the impact of high solar PV penetration on the grid system stability of the electrical system operating at 150 kV voltage. The impact analysis was conducted using Digsilent software with high solar PV penetration baseline scenario in 2025 compared to the NZE scenario in 2060. The research includes power flow analysis, short circuit and transient analysis to evaluate the system response to generation fluctuations and systemic disturbances. The results show that a 12% penetration of solar PV leads to an increase in bus voltage, but the value is still within the tolerance limits stipulated in the national grid code. However, in the NZE scenario, a 30.4% increase in solar PV penetration reduces system inertia, which in turn amplifies frequency oscillations and makes the power system more susceptible to major disturbances such as the Jamali submarine cable failure potentially leading to greater instability during such events. This study recommends mitigation strategies by increasing the capacity of the energy storage system and strengthening the interconnection system by improving of the Java-Bali submarine cable line to ensure stability and reliability of the grid system in the future.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".