C. Panayiotou B. Azzopardi

Malta College of Arts, Science and Technology

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Elias Kyriakides

University of Cyprus

Ana Gracia Amillo

National Renewable Energy Centre

Alex Rizzo

Malta College of Arts, Science and Technology

Avgoustina Kyriakou

University of Cyprus

Nicholas Borg

Electronic Systems Design (Malta)

Brian Bartolo

Malta College of Arts, Science and Technology

Brian Azzopardi

Malta College of Arts, Science and Technology

Melodie de l’Epine

Lenos Hadjidemetriou

University of Cyprus

Mario Cardona

University of Bari Aldo Moro

Cooperative Institutions

Malta College of Arts, Science and Technology

University of Cyprus

German Jordanian University

University of Manchester

National Renewable Energy Centre

Heritage Malta

Frederick University

Arizona State University

University of Milan

Joint Research Centre

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Islanding and Resynchronization Procedure of a University Campus Microgrid

2022 International Conference on Smart Energy Systems and Technologies (SEST) (2018)

Lazaros Zacharia Avgoustina Kyriakou Lenos Hadjidemetriou Elias Kyriakides C. Panayiotou B. Azzopardi

During the last decade, microgrids are attracting a significant attention due to their numerous advantages. Amongst them, the most important one is their ability to operate either in grid-connected or in islanded (autonomous) mode. However, the transition between the two modes can be challenging in terms of maintaining the stability and integrity of the microgrid. In this paper, an optimization-based islanding methodology is developed to ensure a timely and smooth transition from the grid-connected to the islanded mode. This is achieved through shedding loads, by defining the generation level ofthe photovoltaics (energy spill) and by regulating the charging/discharging rate of batteries. A resynchronization method is also presented along with the requirements that need to be satisfied for the smooth reconnection of the microgrid back to the main grid. The effectiveness of the proposed approach is demonstrated through simulation results for the events of surplus energy production, and excess energy demand of the microgrid.

Microgrid

Islanding

Mode (computer interface)

10.1109/sest.2018.8495828

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Citations (15)

Enhancing Photovoltaic Module Fault Detection and Diagnosis Skills through Experiential Learning

2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) (2024)

Carlos Germán Meza Brian Azzopardi Bernhard Kubicek Ana Gracia Amillo Melodie de l’Epine

10.1109/isgteurope62998.2024.10863512

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A Study of the Factors that Influence the Employment Potential of MCAST Level 4 Graduates

MCAST Journal of Applied Research & Practice (2020)

C. Panayiotou B. Azzopardi Alex Rizzo Mario Cardona

This graduate tracer study was carried out among Level 4 students from all MCAST Institutes who completed their final study programmes in the academic years 2014/2015 and 2015/2016. It intends to provide information about the situation graduates face after completing their studies and supply data to help stakeholders make informed decisions in relation to the transition from education to employment. The research process included secondary and primary research. Factor analysis was the multivariate technique used to group the factors into four different independent variables: Student academic performance; quality of instruction; acquired skills and competences, and MCAST services. As well as using the multivariate technique for the dependent variable: Graduates’ employment potential. The conclusions from Stage 1 pre-test factor analysis resulted in the refined hypotheses of this study. The conceptual framework was also refined, with the variables identified in the pre-test of this research. A new updated instrument was then created and used for Stage 2 regression. Stage 2 consists of multivariate regression on the final respondent data. The results of this study show that, out of these independent variables, the acquired skills and competences variable showed a strong positive relationship with the graduates’ employment potential dependent variable with a beta value of 0.66 and sig. 0.000. The other independent variables achieved a beta value less than 0.1, indicating a weak relationship to the dependent variables. This research project has been a learning experience as well as a pilot programme for future MCAST graduate surveys.

Variables

10.5604/01.3001.0014.4405

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