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Urban energy systems are undergoing profound structural transformations driven by decarbonization policies, digitalization, and demands for more democratic and inclusive governance. In this context, Positive Energy Districts (PEDs) and Local Energy Communities (LEC) have emerged as key paradigms in the pursuit of climate-neutral cities. PEDs aim to generate more energy than they consume through spatially integrated design, technological efficiency, and centralized coordination at the district scale (Casamassima et al., 2022); (Kvasnica & Vaľková, 2022). In contrast, (Local) Energy Communities prioritize citizen empowerment, local ownership, and collective management of renewable energy resources, operating through decentralized, often bottom-up governance structures (Volpe et al., 2024).
At the same time, Artificial Intelligence (AI) is becoming increasingly embedded in urban energy planning and operations. AI tools are used to monitor, simulate and optimize energy flows, forecast consumption patterns, and support real-time decision-making (Han et al., 2023). These technological developments are not value-neutral: they reshape power dynamics, influence design processes and raise new ethical and regulatory challenges for planners.
This paper critically examines the tensions and complementarities between PEDs, Energy Communities, and AI systems. These converging trends are reshaping the competencies and roles required of urban planners, calling for an urgent rethinking of planning education. Traditional curricula often remain siloed, technocratic, and insufficiently responsive to the complexity of today’s urban energy landscape. There is a pressing need to equip future planners with new forms of literacy—energy literacy, digital literacy, and socio-technical systems thinking—that enable them to work across disciplines, engage with communities, and navigate uncertainty (Gondeck et al., 2024); (Kozlowska et al., 2024).
The contribution argues that integrating these paradigms into planning practices is not merely a technical update, but a paradigm shift in how planning itself is taught and practiced. Preparing planners to mediate between centralized and decentralized energy logics, to critically engage with AI tools, and to foster socially just transitions is essential to building sustainable, inclusive, and resilient urban futures.
This work is carried out within the European project DUT 4A4PEDs - DUT Joint Call 2023 - “MUR funding, application code DUT23_00117”.
References
Casamassima, L., Bottecchia, L., Bruck, A., Kranzl, L., Haas, R., 2022. Economic, social, and environmental aspects of Positive Energy Districts—A review. WIREs Energy & Environment 11, e452. https://doi.org/10.1002/wene.452
Gondeck, M., Triebel, M.-A., Steingrube, A., Albert-Seifried, V., Stryi-Hipp, G., 2024. Recommendations for a positive energy district framework – Application and evaluation of different energetic assessment methodologies. Smart Energy 15, 100147. https://doi.org/10.1016/j.segy.2024.100147
Han, M., Shah, J., Zhang, X., 2023. Review of natural language processing techniques for characterizing positive energy districts. J. Phys.: Conf. Ser. 2600, 082024. https://doi.org/10.1088/1742-6596/2600/8/082024
Kozlowska, A., Guarino, F., Volpe, R., Bisello, A., Gabaldòn, A., Rezaei, A., Albert-Seifried, V., Alpagut, B., Vandevyvere, H., Reda, F., Tumminia, G., Ranjbar, S., Rincione, R., Cellura, S., Eicker, U., Zamini, S., Balsategui, S.D.D.G., Haase, M., Di Pilla, L., 2024. Positive Energy Districts: Fundamentals, Assessment Methodologies, Modeling and Research Gaps. Energies 17, 4425. https://doi.org/10.3390/en17174425
Kvasnica, J., Válková, K., 2022. Holistic assessment methodology for Positive Energy Districts. APP 38, 162–168. https://doi.org/10.14311/APP.2022.38.0162
Volpe, R., Cutore, E., Fichera, A., 2024. Design and Operational Indicators to Foster the Transition of Existing Renewable Energy Communities towards Positive Energy Districts. J. sustain. dev. energy water environ. syst. 12, 1–22. https://doi.org/10.13044/j.sdewes.d12.0513
| Keywords | Positive Energy Districts; Energy Communities; Artificial Intelligence; Power dynamics; Urban |
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