| Abstract |
Advanced computational techniques are essential for tackling the issues associated with the integration of Distributed Generation (DG) into power distribution networks, especially in the context of modern smart grids and their role in the development of smart cities. This research establishes an extensive framework that integrates probabilistic modeling, multi-objective optimization, and high-performance computing to assess and improve system reliability and economic efficiency. A probabilistic reliability assessment model is introduced to encapsulate the stochastic properties of distributed generation output and load fluctuations, utilizing Monte Carlo simulation to quantify essential reliability metrics such as Expected Energy Not Supplied (ENS) and System Average Interruption Duration Index (SAIDI). An augmented Particle Swarm Optimization (PSO) algorithm is developed to attain a balance between reliability enhancement and cost reduction, using adaptive strategies for superior convergence and solution quality. Simulations performed on an IEEE 33-bus benchmark network demonstrate that increased distributed generation penetration markedly decreases expected non-supply (ENS) and System Average Interruption Duration Index (SAIDI), but with declining results after specific thresholds. Photovoltaic (PV) systems, which utilize optical energy conversion processes, are highlighted as a key DG technology in this study. This research aligns with the objectives of smart cities by enhancing the efficiency, reliability, and sustainability of intelligent power distribution systems. The interdisciplinary approach, which combines probabilistic modeling, optimization algorithms, and high-performance computing, contributes to advancing the integration of distributed generation into future-ready smart grids, addressing the growing energy demands of interconnected urban infrastructures. © 2025 The Authors. |
