Integrating Microgrids into Engineering Education: Modeling and Analysis for Voltage Stability in Modern Power Systems

Farheen Bano, Ali Rizwan, Suhail H. Serbaya, Faraz Hasan, Christos-Spyridon Karavas and Georgios Fotis ()
Additional contact information
Farheen Bano: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Ali Rizwan: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Suhail H. Serbaya: Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Faraz Hasan: Department of Computer Science and Engineering, GITAM (Deemed to be University), Hyderabad Campus, Hyderabad 502329, Telangana, India
Christos-Spyridon Karavas: Department of Natural Resources Management and Agricultural Engineering, Agricultural University of Athens, 11855 Athens, Greece
Georgios Fotis: Centre for Energy Technologies, Aarhus University, Birk Centerpark 15, Innovatorium, 7400 Herning, Denmark

Energies, 2024, vol. 17, issue 19, 1-29

Abstract: The research focuses on incorporating microgrids into engineering curricula for achieving voltage stability in today’s power systems. This helps to meet the increasing demand for engineers to integrate distributed power generation and renewable energy sources. Some limitations of the current literature include the absence of models outlining approaches to microgrid education and limited insight into teaching strategies for electrical power systems. The research used a quantitative methodology to survey 100 engineering students enrolled in a microgrid modeling class to achieve the study’s objectives. The data analysis involved machine learning models such as Random Forest, Gradient Boosting, K-Means, hierarchical clustering, and regression models. The major findings identified exam score as the most significant determiner of student performance (weight ≈ 0.40). Based on the clustering analysis, it was found that microgrid systems can be grouped into four operational states. It was also seen that linear regression models were highly accurate and better than other highly complex models, like Decision Tree, with a model accuracy of R 2 ≈ 0.4. One of the study’s major strengths is the potential impact of the proposed framework for integrating microgrids into engineering education on the professional training of engineers. This framework, based on theoretical knowledge and practical experience as well as on developing advanced analytical skills, can significantly enhance the professional training of engineers to deal with the complexities of contemporary power systems, including microgrids and sustainable energy progress.

Keywords: engineering curriculum; engineering education; gradient boosting; hierarchical clustering; microgrids; machine learning; power generation; renewable energy sources; random forest; voltage stability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/19/4865/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/19/4865/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:19:p:4865-:d:1487665

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().