Enhancing Microgrid Efficiency via a Novel Hybrid ICA-PSO Approach for Demand Response
Bibliographic record
Abstract
This study introduces a novel approach to enhancing microgrid operations by incorporating demand response (DR) strategies with a hybrid optimization model that combines the strengths of the Imperialist Competitive Algorithm (ICA) and Particle Swarm Optimization (PSO). The approach aims to improve energy management, decrease the losses, stabilize voltage and frequency, and achieve a balanced alignment between energy supply and demand. A notable aspect of this work is the use of the IEEE 37-Bus test system, which provides a realistic environment to evaluate the hybrid ICA-PSO method. The findings show that this approach significantly outperforms traditional methods, delivering superior results in power losses reduction, voltage regulation, and frequency stabilization. These outcomes highlight the method’s practicality for real-world microgrid applications. The paper further explores the unique benefits of ICA and PSO, explaining the rationale for their integration and emphasizing how demand response enhances microgrid efficiency. It also suggests areas for future investigation, such as addressing uncertainties and integrating real-time data, to improve the adaptability and reliability of the proposed method in complex operational scenarios. The problem modeling has been done using MATLAB software. The results proved 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".