<![CDATA[Rapid expansion of renewable energy resources has increased the operational complexity of modern electricity grids as intermittent generation, distributed energy resources, and dynamic demand challenge conventional optimization methods. This study evaluated the effectiveness of computational intelligence and heuristic algorithms in optimizing renewable energy grids by improving power system resilience, renewable energy utilization, operational efficiency, and adaptive grid management. A mixed-methods sequential explanatory design was applied using 2,400 simulation scenarios across forty benchmark renewable energy distribution systems with varying renewable penetration, battery storage capacities, distributed generation configurations, and demand response conditions. Quantitative analyses included structural equation modeling, hierarchical regression, multivariate analysis, mediation, and moderation analyses, while qualitative evidence from expert interviews, engineering discussions, and technical document reviews was examined through thematic analysis. The findings showed that hybrid computational intelligence algorithms consistently outperformed conventional optimization approaches by enhancing renewable energy utilization, reducing power losses, improving voltage stability, accelerating computational convergence, and strengthening grid resilience. Distributed energy coordination and renewable forecasting further improved optimization performance under uncertain operating conditions. Overall, intelligent optimization represents an adaptive cyber-physical framework integrating renewable generation, energy storage, and demand response into resilient and sustainable electricity distribution systems. The proposed framework provides practical guidance for utility operators, system planners, and policymakers to accelerate reliable renewable energy integration while supporting long-term decarbonization and sustainable power system transformation.]]>
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