Proceedings of the International scientific and practical conference ―Science, Technology and Culture in the Era of Globalization‖ (December 24-26, 2025) / Publisher website: www.naukainfo.com. – Geneva, Switzerland, 2026. – 234 p.

196 particularly relevant given the destruction of Ukraine's centralized energy grid. This approach is supported by international organizations, including the International Energy Agency (IEA) and the World Bank, which emphasize the importance of decentralization for ensuring energy security. All modeling of generation potential and time profiles was created using the open source software RESKit and made publicly available by the authors [8]. Such transparency facilitates the widespread application of research findings to renewable energy development planning, public policymaking, and investment decision-making, thereby enhancing the concept of transition from centralized grids to local energy solutions in Ukraine. The potential of these technologies in Ukraine demonstrates significant opportunities for the development of renewable energy and the enhancement of local energy autonomy. However, the technical potential alone is insufficient for the comprehensive integration of these distributed energy sources into the national energy system. The increasing deployment of rooftop PV systems creates new challenges for grid management, associated with variability of generation, load fluctuations and the need to maintain stability. These challenges require a transition from centralized control models to more flexible and adaptive systems capable of operating in real time with a large number of distributed energy sources. In this context, digital technologies play a critical role, enabling the collection, processing, and analysis of large volumes of data directly at energy grid nodes, thereby minimizing latency and increasing the speed of decision- making. One of the promising directions for development is the application of artificial intelligence at the grid edge (Edge AI), enabling localized intelligent management of energy generation and consumption. This approach can improve the efficiency of energy flow balancing, adapt to rapidly changing weather conditions and loads, and minimize the risk of emergency situations. The study presented by de Paula Jr. et al. [2] provides a comprehensive analysis of the Collaborative Edge AI for decentralized energy systems. The authors