Capacity and Reliability Assessment of Distributed Photovoltaic Microgrid Systems Based on Advanced Computational Methods

Distributed Photovoltaic (PV) Microgrids represent a transformative leap in modern energy systems, aligning with the thematic goals of computational advancements in sustainable energy systems, as highlighted by the special issue’s focus on cutting-edge computer science applications. Despite their potential for fostering resilience and energy independence, existing approaches to microgrid design face critical limitations in managing solar variability, ensuring system reliability, and optimizing energy distribution under dynamic and stochastic conditions. This study addresses these gaps by proposing a novel framework that integrates predictive machine learning models, decentralized optimization strategies, and dynamic energy storage control to enhance the operational efficiency and reliability of Distributed PV Microgrids. The proposed Adaptive Solar-Energy Network Model (ASENM) incorporates advanced spatio-temporal forecasting for solar generation and demand, enabling precise energy balancing under uncertain conditions. It leverages a Cooperative Decentralized Energy Optimization (CDEO) strategy that facilitates real-time coordination across distributed nodes through event-triggered control and energy-sharing protocols. Results from comprehensive simulations demonstrate the superiority of this hybrid approach in reducing energy imbalances by 18%, improving system scalability, and lowering operational costs by 12% compared to traditional centralized methods. This work not only advances the computational modeling of renewable energy systems but also establishes a robust paradigm for the real-time adaptability and sustainability of distributed energy networks.