Generalized model-predictive control for supercapacitor and superconducting magnetic energy storage systems

The integration of renewable energy sources requires efficient and reliable energy storage systems to stabilize grid operation and address the inherent variability of this type of generation. This study focuses on electric energy storage systems (EESS), which encompass supercapacitor energy storage (SCES) and superconducting magnetic energy storage (SMES). Leveraging their shared structural properties, it is possible to propose a unified modeling framework. A model predictive control (MPC) strategy is developed within this framework, offering precise regulation of active and reactive power while ensuring system stability. The proposed strategy incorporates a discrete bilinear model and a one-step control horizon to optimize performance under dynamic operating conditions. Numerical simulations demonstrate the proposed MPC approach’s effectiveness in reducing power oscillations, enhancing response dynamics, and maintaining grid stability in scenarios with variable loads, renewable energy fluctuations, and a three-phase fault in microgrid. The proposed control is compared to conventional strategies, showing superior performance with faster adaptation and fewer oscillations. Quantitative results based on standard performance indices (IAE, ITAE, ITSE, T s , and M p ) further confirm the superior transient and steady-state behavior of the proposed MPC strategy. In addition, passivity and stability are formally guaranteed via the Lyapunov theorem.