Economic Planning of the Operation of PV-Connected Distribution Network Using a Probabilistic Model for Extending the Network Lifetime
Bibliographic record
Abstract
The power sharing of PV sets with battery banks in a low voltage distribution network, is optimized with the aim of extending network lifetime. The network lifetime is analyzed using a probabilistic model, in which each PV-battery set has a certain failure probability of supplying power to any load demand center. This probability is assumed to be of normal distribution, that is related to other factors such as power rating, coverage availability, and battery DoD. To extend network lifetime, redundancies in power sharing are removed by activating different groups of PV sets at different times, with durations depending on their joint Gaussian probabilities in supplying the load demands. The contribution of each PV-battery set, is estimated in an intuitive method according to the evaluated probabilities, in which the network is converted into source nodes and load nodes distributed as an ad hoc network, with formulated Gaussian probabilities. An economic load dispatch is then evaluated among the selected PV sets of probabilities higher than a predefined threshold value, to optimize power sharing of the load. A case study of several PV-battery sets supplying several distributed loads, is analyzed and simulated, with formulated joint probabilities. It is found that lifetime is extended by 190% for three PV sets supplying two load centers.
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.
How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".