Optimized energy management in Grid-Connected microgrids leveraging K-means clustering algorithm and Artificial Neural network models
Bibliographic record
Abstract
The increasing integration of renewable energy sources (RESs) in grid-connected microgrids necessitates advanced energy management strategies to enhance efficiency, reliability, and sustainability. This study proposes an optimized energy management framework leveraging the One-to-One-Based Optimizer (OOBO) for microgrid scheduling, combined with K-means clustering and Artificial Neural Networks (ANNs) for load forecasting. The proposed method dynamically schedules distributed energy resources (DERs), battery energy storage systems (BESS), and diesel generators while minimizing operational costs and carbon emissions. Simulation results demonstrate that the OOBO-based optimization achieves a 20–48% reduction in operational costs and a 25–38% decrease in carbon emissions, outperforming conventional methods such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Differential Evolution (DE). The comparative analysis highlights the superior convergence speed of OOBO, reducing computational time by 30–45%, making it suitable for real-time applications. Furthermore, the study evaluates three scenarios: reliance solely on a diesel generator, optimization without BESS, and optimization with BESS, where BESS integration led to a 38% reduction in emissions compared to diesel generator-only configurations. The novelty of this work lies in the synergistic integration of OOBO, AI-driven forecasting models, and adaptive resource scheduling, ensuring optimal cost savings and energy efficiency. The results confirm the scalability and robustness of the proposed framework, making it a promising solution for future multi-microgrid and multi-energy system applications. These findings provide a strong foundation for sustainable energy transitions, reducing dependence on fossil fuels and enhancing grid stability.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".