Optimal control strategy for solid oxide fuel cell-based hybrid energy system using deep reinforcement learning

Abstract

This paper proposes a self-adaptive control strategy for solid oxide fuel cell (SOFC) based hybrid energy system using deep reinforcement learning (DRL) techniques. Highly efficient use of hydrogen in a hybrid energy system with the aid of SOFC could create a new paradigm of renewable energy ecosystem and a series of operation principles. Instead of modeling the energy system operation decision-making process as an optimization problem, a DRL framework is used to seek the optimal control strategy with consideration of various physical constraints in the SOFC components and hybrid energy system operation. Specifically, a deep deterministic policy gradient (DDPG) algorithm is used to solve the operation problem and provide the optimal policy guiding the control actions of a tubular SOFC stack, which involves various dynamic characteristics besides the electric measurement. The learnt control strategy may not produce the best result every time, but can guarantee the ultimate benefit in a non-deterministic way in the long-term operation.