Reduction in Microgrid Topology Selection Time via Hybrid Branch and Bound and k-Nearest Neighbors Techniques
Bibliographic record
Abstract
The global adoption of hybrid renewable energy systems (HRESs) is accelerating as a strategic response to escalating energy demands and the imperative to mitigate greenhouse gas emissions. Despite the development of various technological tools, such as pre-feasibility analysis, sizing, and simulation tools, challenges persist due to their limited flexibility in modifying system architectures and their typically long computation times, which hinder their practical efficiency. This study introduces a novel hybrid method that integrates the Branch and Bound (BB) heuristic search algorithm with the k-Nearest Neighbors (kNN) algorithm to drastically reduce the simulation time of microgrid models in Simulink. Validation considering four distinct case studies reveals that our method can decrease the simulation time by up to 94.68% while maintaining an acceptable accuracy. Specifically, simulation times in certain cases were reduced from approximately 21,780 and 118,580 s to 1442.7969 and 6306.0625 s, respectively. This significant reduction facilitates the rapid evaluation and selection of optimal HRES configurations, enhancing the efficiency of both editable and non-editable systems. Through streamlining the simulation process, this approach not only accelerates the design and analysis phases but also supports the broader adoption and deployment of HRESs, which is critical for achieving a sustainable future. This advancement offers a robust and efficient methodology for optimizing simulation times, thereby addressing a key bottleneck in the development and implementation of hybrid renewable energy solutions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".