Optimizing Energy Management of Hybrid Battery-Supercapacitor Energy Storage System by Using PSO-Based Fractional Order Controller for Photovoltaic Off-Grid Installation
Bibliographic record
Abstract
The integration of hybrid energy sources, such as batteries and supercapacitors, in off-grid photovoltaic installations is of crucial importance.This method promotes energy autonomy, offers operational flexibility, compensates for fluctuations in solar production, and can result in long-term economical savings.It also allows for optimized energy management through efficient storage and redistribution.This work details the design and simulation of a self-sufficient solar system that uses supercapacitors and batteries as part of a hybrid energy storage system.Recognizing the increasing significance of efficient energy systems, this study addresses the importance of such installations in delivering sustainable energy solutions.The FOPI-PSO controller optimized using the Particle Swarm Optimization (PSO) technique; demonstrates greater flexibility with a greater number of parameters, surpassing the adaptability of the conventional PI controller.By using multiple simulation scenarios that take into consideration both variations in load and irradiance, the study compares the effectiveness of both controllers in terms of synchronizing batteries and supercapacitors.The results demonstrate the PSO-based FOPI control strategy's outstanding performance, showing its optimal efficiency and robustness.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".