Data-driven optimal control of fuel cells for frequency regulation: Simulation and experimental validation

Abstract

Fuel cells (FCs) have attracted significant attention as a promising technology for enhancing sector coupling and improving grid power balancing in the pursuit of a low-carbon energy system. This paper presents a comprehensive experimental investigation into the operational characteristics of an FC system, including stack temperature, voltage, current, and power. The findings confirm the potential of FC systems to effectively support real-time frequency regulation (FR). An experimental setup was implemented to analyze the dynamic responses of FC systems, and a data-driven model predictive control (MPC) strategy was proposed to optimize power sharing between FC systems and distributed generators (DGs). The MPC strategy enables FC systems to mitigate power supply-and-demand imbalances, while DGs compensate for the remaining imbalances. Small-signal analysis was conducted to assess the contribution and sensitivity of the proposed FR strategy. Comparative experimental case studies further validate the accuracy of the developed FC model and the effectiveness of the proposed control strategy. The results demonstrate that the proposed approach significantly reduces frequency deviations under various grid conditions, including varying net load demands, plug-and-play operations, communication time delays, and various control parameters.