Machine learning and optimization in hybrid energy storage systems: Integrating MPC, PSO, MILP, and ANN for grid stability and efficiency

Hybrid Energy Storage Systems (HESS) can provide solutions for grid stability and integration of renewable energy sources. A novel model integrating Model Predictive Control (MPC), Particle Swarm Optimization (PSO), Mixed-Integer Linear Programming (MILP), and Artificial Neural Networks (ANN) is developed to optimize HESS performance. MPC is employed for its predictive capabilities, enabling real-time control under dynamic grid conditions, while PSO optimizes system configurations for cost and efficiency. In the real-time operations of the grid, the MPC framework successfully handles complicated constraints with the possibility of being adaptively predictive. PSO optimizes the system by obtaining optimal configurations in non-linear and multimodal conditions with balanced cost and performance. MILP ensures the operating strategy of the system by fulfilling capacity, efficiency, and stability conditions at the grid level. ANN provides high precision predictions for energy management, hence enhancing the process of decision making for various cases. Simulations show up to 15.8% reductions in energy losses, 21.4% enhancements in grid stability, and 90% improvements in the operation efficiency as compared to the current methods. The integration benefits highlight superior adaptability and cost effectiveness. System costs are reduced to $1.6 million. Conclusion scalable pathway for enhancing renewable energy penetration and addressing the demands of the grid. The proposed model, employing advanced control and optimization techniques, ensures resilient energy systems capable of assimilating various REs and dynamic grid conditions that ensure proper transition towards low-carbon energy grids.