Nonlinear Model Predictive Control for Dynamic Operation of an Alkaline Electrolyzer

Abstract

This paper demonstrates how incorporating future input power information impacts the performance of a nonlinear model predictive control (NMPC) algorithm for an alkaline electrolyzer (AEL) plant. The primary objective of the NMPC is to maintain the stack temperature and number of moles of water in the AEL within operating limits, despite large variations in the input power. The NMPC combines an optimal control problem (OCP) with a continuous-discrete extended Kalman filter (CD-EKF). For both the OCP and the CD-EKF, we use a model that is different from the AEL simulation model. We present three closed-loop simulations: two where the NMPC operates at different stack temperature and water mole setpoints with only current input power information, and one where it receives information about future power changes in advance. The results show a 1.583% increase in hydrogen production when the NMPC utilizes information about future power changes.