Enhancing microgrid renewable energy integration at SEKEM farm
Bibliographic record
Abstract
This study explores the feasibility of implementing a hybrid microgrid system powered by renewable energy sources. Including solar photovoltaics, wind energy, and fuel cells to ensure a reliable and sustainable electricity supply for the SEKEM farm in WAHAT, Egypt. The study utilizes MATLAB/Simulink software to conduct simulations based on sun irradiation and wind speed data. Various control techniques, such as the proportional-integral (PI) controller, Fuzzy Logic Controller for PI tuning (fuzzy-PI), and neuro-fuzzy controllers, were evaluated to improve the performance of the microgrid. The results demonstrate that the Fuzzy-PI control strategy outperforms the alternative control systems, enhancing the overall dependability and long-term viability of energy provision. The hybrid system was integrated with a voltage source control (VSC) and fuzzy PI controller, which effectively addressed power fluctuations and improved the stability and reliability of the energy supply. Furthermore, it provides insightful information on how to design and implement a 100% renewable energy system, with the fuzzy PI controller emerging as a viable method of control that can guarantee the system’s resilience and outperform other approaches, such as the standalone PI controller and the neuro-fuzzy controller.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".