Economic model predictive control for multi-energy system considering hydrogen-thermal-electric dynamics and waste heat recovery of MW-level alkaline electrolyzer

Abstract

A large-scale alkaline electrolyzer (AE), can convert renewable electricity into green hydrogen and recoverable heat, and consequently unlock great flexibility to accommodate renewable power variability. This paper proposes an economic model predictive control (EMPC) based daily optimal operation strategy for a multi-energy system (MES) which unleashes the cross-sectoral operational flexibility of AE. A dynamic power-to-hydrogen&heat (P2H2) model for AE is presented, considering heat recovery and efficiency variation under different loading conditions. Instead of incorporating the nonlinear P2H2 model into the operating optimization model of MES, this paper has developed a more computationally efficient algorithm that uses the P2H2 model for post-evaluation of optimal scheduling derived from a mixed-integer linear programming (MILP)-based EMPC. Comparative cases based on real data of a Danish energy island Bornholm, demonstrate the effectiveness, robustness of the proposed method and the potential value of AE’s operational flexibility in the MESs. The results reveal that the proposed method contributes to additional operation cost savings of 59% and 38% compared to a traditional rule-based strategy and an economic strategy without P2H2 model.