Optimizing solar farm interconnection networks using graph theory and metaheuristic algorithms with economic and reliability analysis.

As global energy demand continues to rise and the need to transition from fossil fuels becomes increasingly urgent, integrating solar farms efficiently into power grids presents a significant challenge. This study introduces a novel graph-theoretic framework for designing optimal interconnection networks among distributed solar farms. By utilizing Prim's algorithm to construct a minimum spanning tree, the proposed method effectively reduces transmission losses and infrastructure costs. The performance of this deterministic approach is benchmarked against Particle Swarm Optimization (PSO), a widely applied metaheuristic technique. To assess network robustness under potential line failures, a new graph-based reliability metric is developed. Case studies involving a cluster of solar farms demonstrate that Prim's algorithm outperforms PSO in minimizing both power losses and capital investment, while also offering higher topological reliability. Although PSO achieves better load balancing, the graph-based approach proves more effective for loss-sensitive and cost-driven design scenarios. The proposed framework naturally accommodates constraints such as terrain limitations and is scalable to hybrid renewable energy systems. By integrating classical graph theory with practical power system considerations, this work offers a computationally efficient and economically viable solution for the optimal physical integration of large-scale solar energy infrastructure. The proposed methodology also lays a foundation for future integration of AI and machine learning techniques to enable dynamic network optimization under uncertainty.