Intelligent modeling and fuzzy logic for optimizing electrical dispatch with hybrid energy sources 11 6

Abstract

The transition toward sustainable energy matrices requires integrating hybrid renewable sources, which introduces a high level of uncertainty and variability that traditional electric dispatch systems fail to manage efficiently. This issue directly affects the operational stability and socioeconomic competitiveness of power systems. The goal of this work is to develop an intelligent model based on fuzzy logic to optimize the decision-making process in electric dispatch under high-penetration alternative energy scenarios. Theoretically, the study is grounded in applied computer science, operational uncertainty management, and complex process optimization theories. Methodologically, a quantitative and applied approach was adopted, structured around an experimental design based on scenario simulation. For data collection and analysis, the data were processed. Methodologically, a quantitative and applied approach was adopted, structured around an experimental design based on scenario simulation. For data collection and analysis, historical generation and demand records were processed, translating qualitative linguistic variables into fuzzy sets using specific membership functions. The results show that the presented algorithm reduces operating costs and smooths out energy uncertainties for hybrid sources better than conventional heuristic methods. It is concluded that implementing this smart modeling not only strengthens the technical security of the grid, but also represents a key organizational innovation in the knowledge economy, turning operational data into strategic value for more flexible, economical, and low-carbon energy management.