Optimal tie‐line and battery sizing for remote provisional microgrids
Bibliographic record
Abstract
Abstract Unlike traditional microgrids, Provisional Microgrid (PMG) utilizes only renewable generation and small energy storage units; thus, it does not have an inherent self‐islanding capability and instead, relies on importing power from any coupled microgrid (CMG) for islanding purposes. Therefore, assuring adequate power sharing between interconnected provisional and coupled MGs is crucial for assuring self‐islanding capability and reliable operation. The optimal power that can be transferred between the coupled MG and the PMG is mainly restricted by the size of the interconnecting tie‐line; hence, determining the tie‐line optimal size becomes a crucial task that must be tackled. Furthermore, the amount of power transferred is significantly influenced by the flexibility level of each microgrid. Since battery storage systems (BSS) is the main source of flexibility in PMGs, it becomes also vital to obtain the optimal size of the BSS for planning islanded PMG system. This paper presents an optimization model to jointly determine the optimal size of the BSS in each MG and the tie‐line size to assure optimal power sharing and minimum system cost (tie‐line investment cost, BSS investment cost, and interconnected system’s operation cost). Numerical results demonstrate the effectiveness of the proposed model.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".