MATLAB-TRNSYS simulation framework for MPC-based optimization of hybrid renewable energy systems

Abstract

• Predictive MPC optimizes hybrid renewable energy system management. • Hydrogen consumption reduced by 34.6% in predictive vs. deterministic case. • Hydrogen storage stability improved by 50% in predictive control strategy. • MATLAB-TRNSYS integration enables precise simulation and optimization Hybrid renewable energy systems (HRES) combining wind turbines, photovoltaic arrays and hydrogen storage can supply dispatchable low‑carbon power while buffering resource variability. This study presents a nonlinear Model Predictive Controller (MPC) implemented in a MATLAB–TRNSYS co‑simulation to coordinate generation, electrolysis, compressed‑gas storage and PEM fuel‑cell reconversion over a 6 h rolling horizon. The controller maximises renewable utilisation and maintains the hydrogen state‑of‑charge (SOC) within safe limits, enabling the stored H₂ to serve later as an energy vector or chemical feedstock. Relative to a deterministic single‑step strategy, the predictive MPC reduces hydrogen consumption by 34.6 %, halves the SOC variance and increases the H₂/O₂ co‑production rate by 37 %, yielding a higher overall conversion efficiency. Under a variable 1.2 MW demand profile the scheme meets the load with a renewable penetration of 54 %. These results demonstrate that anticipatory, constraint‑aware control provides a robust pathway for reliable and scalable hydrogen‑centred HRES.