A Dynamic Model-Based Minute-Level Optimal Operation Strategy for Alkaline Electrolyzers in Wind-Hydrogen Systems

Maintaining the export power of wind-hydrogen systems within a stable range is critical for power system security. However, this is challenged by the mismatch between large time-scale of alkaline electrolyzer (AWE) scheduling strategies and the short-term fluctuations of wind power. To address this issue, this paper proposes a novel minute-level optimization strategy for AWE operation. Developing effective small time-scale strategies requires a detailed consideration of AWE dynamics. To this end, we first introduce its steady-state electrochemical characteristics and third-order dynamic models for both temperature and Hydrogen-to-Oxygen (HTO) ratio. Based on these refined models, we develop an AWE optimization framework that enables electrolysis power to track minute-level wind power fluctuations by dynamically adjusting fine-grained variables, such as the lye flow rate, cooling flow rate, and pressure, at 1-minute intervals. To overcome the computational challenges posed by the detailed modeling, we propose an improved model predictive control (MPC) framework. This framework incorporates model simplifications to improve computational efficiency, along with an optimization-simulation iterative procedure to ensure operational feasibility. Case studies demonstrate that the proposed strategy extends the AWE load range by 13.8% and reduces wind power curtailment by 15.06%. Additionally, synergies among control variables enable the system to achieve a balance between operational efficiency, stability, and security, highlighting the potential of this approach to enhance the performance of wind-hydrogen integrated systems.