The rapid expansion of renewable energy resources has fundamentally transformed the operational dynamics of distributed power systems. Hybrid microgrids integrating photo- voltaic arrays, wind turbines, and battery storage offer localized resilience and improved energy efficiency. However, renewable intermittency introduces significant uncertainty in forecasting and dispatch planning, particularly under high penetration sce- narios. Conventional energy management systems rely on static weighting strategies and centralized machine learning models, limiting adaptability and scalability in heterogeneous microgrid environments. This paper proposes a unified self-adaptive renewable energy orchestration framework that integrates meta-learning, federated gradient aggregation, and convex hybrid dispatch optimization within a hierarchical edge–fog–cloud architecture. An adaptive renewable weighting mechanism is mathematically formulated using softmax-based meta-parameterization, enabling dynamic regulation of solar, wind, and storage contributions. A distributed convex optimization problem is constructed for each microgrid, ensuring bounded state-of-charge dynamics under operational constraints. Federated learning enables cross-microgrid parame- ter adaptation without raw data exchange, preserving scalability and data locality. Formal stability guarantees are established under bounded learning rates and convex feasibility conditions. Semi-realistic dataset-driven simulations involving multiple hybrid microgrids demonstrate statistically significant improvements in forecasting accuracy, renewable utilization, and operational cost compared to static and centralized baselines. The proposed framework provides a scalable and resilient foundation for intelligent de- centralized renewable energy management systems.