@Article{ee.2026.081393,
AUTHOR = {Geng Zhang, Fangzheng He, Jiayi Hu, Jiangjiang Wang},
TITLE = {Robust Model Predictive Control Optimization of Hybrid System with Power-Heat-Hydrogen Storage},
JOURNAL = {Energy Engineering},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/energy/online/detail/26937},
ISSN = {1546-0118},
ABSTRACT = {Hybrid systems face significant challenges in maintaining stable operation due to inherent uncertainties in renewable sources and demand loads. To address this, this study proposes a robust model predictive control framework for hybrid system with multi-energy storage (electrical/thermal/hydrogen storage). The system integrates photovoltaic, gas turbine, electrolyzer, and heat pump, leveraging multi-vector storage to buffer uncertainties through dynamic energy conversion and storage coordination. A hybrid forecasting model generates time-varying prediction intervals for source-load uncertainties, quantifying probabilistic bounds via kernel density estimation. The robust model employs these intervals to formulate a min-max optimization problem, proactively coordinating multi-energy storage dispatch under uncertainty. The framework is reformulated into a tractable robust counterpart through constraint tightening, ensuring feasibility across all uncertainty realizations. A case study validates the proposed method. The impacts of confidence levels in balancing robustness against conservatism within uncertainty sets and prediction horizon length on scheduling adaptability are analyzed. The simulation results demonstrate that a 50.6% reduction in grid dependency vs. deterministic model predictive control, despite a 29.7% higher nominal cost, ensuring feasibility under worst-case scenarios. Shrinking prediction horizons from 6 to 2 steps increases costs by 43.6% due to conservative electricity procurement. The approach explicitly co-designs uncertainty-aware predictions with multi-storage dynamics, advancing real-time scheduling robustness for complex hybrid system.},
DOI = {10.32604/ee.2026.081393}
}