Transformative Artificial Intelligence Methodologies for Renewable Energy System Optimization: A Comprehensive Framework for Enhanced Forecasting, Grid Integration, and Sustainable Management

Abstract

The global integration of renewable energy sources (RES) into the power grid is paramount for decarbonization but introduces profound challenges due to their stochastic, non-dispatchable, and geographically dispersed nature. Traditional optimization paradigms often fall short in addressing the high-dimensional, non-linear, and multi-temporal complexities inherent to modern renewable-rich power systems. This paper proposes a novel, unified framework that systematically leverages cutting-edge Artificial Intelligence (AI) paradigms to address these challenges across the entire RES lifecycle. The proposed methodology provides a structured decision-making pipeline for problem characterization, AI architecture selection, and robust implementation tailored to four critical domains: (i) probabilistic forecasting and prediction, (ii) strategic resource allocation and sizing, (iii) real-time control and operational management, and (iv) resilient grid integration and stability. The framework incorporates and defines the role of advanced AI architectures, including Transformer-based models for multi-horizon spatio-temporal forecasting, selective state space models like MAMBA for efficient long-sequence processing, large language models (LLMs) for technical knowledge extraction and constraint formulation, and Graph Neural Networks (GNNs) for topology-aware spatial optimization. A comprehensive implementation strategy elaborates on data fusion, hybrid (physics-informed AI) modeling, validation protocols, and deployment considerations for computationally constrained environments. This structured approach bridges the gap between theoretical AI advancements and their practical, impactful deployment, ultimately facilitating a more reliable, efficient, and scalable renewable energy infrastructure.