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The increasing demand for electrical energy, coupled with the global push for sustainability, has necessitated a transformative shift in power distribution systems. Distributed generation (DG) sources, often based on renewable energy technologies, are being integrated into radial distribution systems to enhance resilience, reduce losses, and promote green energy. This study presents a comprehensive numerical simulation and design approach to identify the optimal placement of DG sources within radial distribution networks. The proposed methodology begins by characterizing the existing distribution system, considering load profiles, fault analysis, and network topology. Through advanced load flow analysis and optimization algorithms, potential locations for DG sources are identified. The optimal size and type of DG units (e.g., solar photovoltaic, wind turbines, or microturbines) are determined to maximize system performance while adhering to technical constraints and economic considerations. Incorporating real-world data and weather patterns, the simulation evaluates the impact of DG integration on voltage profiles, power losses, and system reliability. Different scenarios are assessed to account for varying load conditions and generation outputs. Furthermore, this research addresses the challenges of grid stability and protection coordination in the presence of DG units, ensuring that the distribution system remains robust against faults and disturbances. The outcomes of this study offer valuable insights into the effective deployment of DG sources in radial distribution systems. By optimizing their placement, utilities and stakeholders can enhance grid resilience, reduce carbon emissions, and harness the benefits of renewable energy sources. The results also provide a foundation for decision-makers to make informed investments in sustainable energy infrastructure. In conclusion, this research contributes to the ongoing efforts to transform traditional radial distribution systems into modern, adaptive, and eco-friendly grids by leveraging numerical simulations and design techniques for the optimal integration of distributed generation sources.