GRAPH NEURAL NETWORK–BASED AI FOR SOLAR FARM AND GRID INTERACTION MODELING.

Abstract

The challenge of growing accessibility to the distributed photovoltaic (PV) systems poses a significant threat to the stability, control, and predictability of power grid operations. The complexity of the solar farm-grid interactions in space and time is not well modeled by traditional data-driven and deep learning models, including convolutional neural networks (CNNs) and long short-term memory (LSTM) models, which restrict their scalability and responding flexibility to the transient real-world (Jia et al., 2021). Graph Neural Networks (GNNs) have been proposed as a radical paradigm to depict intricate relational organizations and dynamic energy flow in the interconnected nodes in renewable energy networks (Liao et al., 2021; Zhao et al., 2024). The work is a synthesis of the modern progress in GNN-based AI systems in solar power forecasting, grid interaction modeling and voltage control optimization, focusing on the fact that they significantly outperform in terms of their capabilities to learn both spatial and temporal dynamics (Abdelkader et al., 2025; Dang et al., 2025). The paper presents a significant advancement in the accuracy of prediction, resistance to the environmental heterogeneity and cross-site generalization of GNN architecture through an extensive review and comparative synthesis of the most popular GNN architectures, such as spatial-temporal GCNs, graph attention networks, or hybrid GNN-LSTM systems (Zhang et al., 2022; Sun et al., 2025). Moreover, this article mentions not only methodological advances, including physics-informed GNNs and decentralized microgrid learning, which are more interpretable and efficient to use (Xu et al., 2024; Meng et al., 2025), but also keeps the readers informed in the related domain. The conclusions show that the GNN-powered AI models can optimally predict PV power and synchronize it with the grid, but they can also provide the foundation to the next generation of intelligent energy systems that will be able to learn and adapt on their own and integrate into the grid in a self-sustainable way. This study is relevant to the current discourse on AI-powered smart grids that provide both theoretical and practical avenues of implementing GNNs in large-scale renewable systems (Murugesan et al., 2025; Theiler and Fink, 2025).