Design of an intelligent AI-based multi-layer optimization framework for grid-tied solar PV-fuel cell hybrid energy systems.

The method proposed in this work focused on a multi-level AI framework capable of enhancing the performance of a grid-tied solar PV-fuel cell hybrid energy system. RL-ENN stands for Reinforcement Learning-Driven Evolutionary Neural Network, while T-STFREP is a generalized acronym for Transformer-Based Spatiotemporal Forecasting. FL-DEO stands for Federated Learning-Based Distributed Optimization. GNN-HSCO is Graph Neural Networks Power Router. Q-GAN-ESO is Quantum-Inspired Generative Adversarial Networks. RL-ENN helps minimize Net Present Cost (NPC) and Cost of Energy (COE) in real-time with adaptive energy dispatch strategies; T-STFREP verifies accuracy based on forecasting transformer temporal sequence; FL-DEO allows for optimization in decentralized manner with privacy; and finally, GNN-HSCO considers hybrid networking as energy graphs to diminish transmission losses whereas finally Q-GAN-ESO looks at synthetic degradation scenarios to allow for optimal management of energy storage. The application under consideration was simulated in MATLAB/Simulink and Python with TensorFlow, with a 30-year evaluation of meteorological data. The proposed model has decreased NPC by 27.5 %, COE by 18.2 %, and battery life by 30.2 %. The results validate its capability when compared against traditional methods such as Genetic Algorithms and Particle Swarm Optimization. With this, we now have a scalable and real-time energy-efficient solution for future smart grid systems.•