Abstract:

The rapid emergence of Renewable Energy Communities is reshaping power system operation and regulation, yet these developments are still only partially reflected in engineering curricula [1]. Students often encounter energy communities mainly at a conceptual level, with limited exposure to realistic models, data, and regulatory frameworks, while research on community energy systems increasingly relies on advanced modeling tools that could support more authentic learning experiences [2, 3].

This work presents the adaptation of the Automatic Energy Community Modeler (AutoECoMo), a MATLAB-Simulink tool originally developed for research on collective self-consumption (CSC) based energy communities, into a structured teaching resource. AutoECoMo configures and simulates energy communities from metered or synthetic demand and generation profiles and a compact specification of consumers, prosumers, and contractual parameters. It automatically generates a modular Simulink model and computes key performance indicators such as self-consumption, self-sufficiency, community-level energy balances, and basic economic metrics. For educational use, the tool is provided through preconfigured templates, curated datasets, and scaffolded workflows with student-oriented documentation and guided lab activities, enabling students to focus on interpreting results and exploring design options, rather than on low-level implementation details [4].

The paper outlines a proposed integration of AutoECoMo into engineering education, defining intended learning outcomes and example activities in which students analyze CSC-based energy communities and compare alternative configurations. The contribution illustrates how a research-grade modeling tool can be repurposed as a virtual laboratory that links theory, regulation, quantitative analysis, and economic assessment in the context of the energy transition.

References:
[1] Á. V. Espinosa, C. R. Casas, M. E. Estevez, M. D. R. Lozano, and C. M. Cruz, “Education in smart grids: a perspective from the field of engineering,” in 2020 XIV Technologies Applied to Electronics Teaching Conference (TAEE), 2020, pp. 1-8, doi: 10.1109/TAEE46915.2020.9163735.
[2] J.-L. Hu and P.-S. Yang, “Interactive Cycles between Energy Education and Energy Preferences: A Literature Review on Empirical Evidence,” Energies, vol. 17, no. 20, p. 5092, Oct. 2024, doi: 10.3390/en17205092.
[3] A. Biancardi, A. Colasante, I. D’Adamo, et al., “Strategies for developing sustainable communities in higher education institutions,” Scientific Reports, vol. 13, p. 20596, 2023, doi: 10.1038/s41598-023-48021-8.
[4] S. Taylor, K. B. Walsh, G. L. Theodori, J. Jacquet, A. Kroepsch, and J. H. Haggerty, “Addressing Research Fatigue in Energy Communities: New Tools to Prepare Researchers for Better Community Engagement,” Society & Natural Resources, vol. 34, no. 3, pp. 403-408, Jan. 2021, doi: 10.1080/08941920.2020.1866724.

Keywords:

Energy Communities, Engineering Education, Simulation-based Learning, Virtual Laboratory.