Driven by the global energy transition and carbon neutrality targets

alkaline water electrolysis has emerged as a key technology for coupling variable renewable generation with clean hydrogen production

offering considerable potential for absorbing surplus power and enhancing grid flexibility. However

conventional control architectures typically treat the power converter and electrolyzer as independent units

neglecting their dynamic interactions and thereby limiting overall system performance under practical operating conditions. This review critically examines existing control approaches

ranging from classical proportional-integral schemes to model predictive control

fuzzy-logic algorithms

and data-driven methods

evaluating their effectiveness in managing dynamic response

multivariable coupling

and operational constraints as well as their inherent limitations. Attention is then focused on the performance requirements of the hydrogen-production converter

including current ripple suppression

rapid transient response

adaptive thermal regulation

and stable power delivery. An integrated co control framework is proposed

aligning converter output with electrolyzer demand across steadystate operation

variable renewable input

and emergency shutdown scenarios to achieve higher efficiency

extended equipment lifetime

and enhanced operational safety. Finally

prospects for advancing unified control methodologies are outlined

with emphasis on constraint-aware predictive control

machine-learning-enhanced modeling

and real time co optimization for future alkaline electrolyzer systems.