Investigating distributed generation systems performance using Monte Carlo simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Summary form only given. A novel algorithm to evaluate the performance of electric distribution systems including distributed generation is proposed. This algorithm addresses the deterministic and the stochastic natures of these electrical systems. Monte Carlo simulation is employed to solve the system operation randomness problem taking into consideration the system operation constraints. The uncertainties in the locations, exported penetration level and the states (on or off) of the distributed generation units constitute the random parameters of the studied systems. The introduced algorithm incorporates these parameters with the traditional Newton-Raphson solution of the power flow equations. Monte Carlo simulation is implemented to perform the analysis of all the possible operation scenarios of the system under study and thus ensure the validity of the results. The proposed algorithm is employed to obtain the hourly power flow solution for a typical distributed generation connected system. The system loading follows several typical load curves based on load bus types. Furthermore, new hourly steady state operating system parameters are evaluated to describe the system behavior under the distributed generation random operation. The results obtained are presented and discussed
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it