Abstract
Modern energy networks constitute complex dynamic systems characterized by operational uncertainty and nonlinear behavior. The objective of this research was to develop a physical-computational framework to analyze the stability and adaptive capacity of energy networks subjected to stochastic perturbations. A nonlinear dynamic model based on ordinary differential equations was employed, integrating Monte Carlo simulation, Latin Hypercube sampling, an Energy Antifragility Index (EAI), sensitivity analysis using Sobol indices, and bifurcation analysis. The results revealed fragile, resilient, and antifragile behaviors, with resilient scenarios predominating. Coupling intensity and perturbation magnitude were the parameters with the greatest influence on the system. Likewise, a critical threshold associated with the emergence of multiple equilibrium states and dynamic transitions was identified. It is concluded that the integration of nonlinear dynamics and probabilistic simulation makes it possible to understand the behavior of complex energy systems under uncertainty.
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