Optimal allocation of distributed generation for planning master–slave controlled microgrids
Bibliographic record
Abstract
This study proposes a novel problem formulation for a planning distributed generation (DG) allocation for microgrids, using the master–slave approach. In the previous planning studies, all DGs have the same operating mode (e.g. operate at unity power factor). For master–slave controlled microgrid, DGs have two possible operating modes: master (non‐unity power factor operation) and slave (unity power factor operation). For planning a master–slave controlled microgrid, in addition to DG siting, the optimal DG operating mode is determined by including a new set of constraints in the planning problem. Thus, the proposed formulation is capable of determining the optimal location of the master and slave DGs with the main objective of minimizing the microgrid's energy losses. The proposed model is formulated as a mixed‐integer non‐linear programming problem; incorporated into an optimal power flow framework and tested on the IEEE 38‐bus systems considering a variable load profile. In addition to this, sensitivity analysis is carried for case studies with different load types and reactive power injection by the slave DGs in the system (e.g. operate at fixed non‐unity power factor). The proposed approach can serve as an efficient tool for utility operators for planning microgrids.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".