Open AccessOptimal control strategy for solid oxide fuel cell‐based hybrid energy system using deep reinforcement learning
Author(s) -
Chen Tao,
Gao Ciwei,
Song Yutong
Publication year - 2022
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/rpg2.12391
Subject(s) - reinforcement learning , solid oxide fuel cell , computer science , stack (abstract data type) , optimal control , process (computing) , hybrid system , control engineering , energy (signal processing) , mathematical optimization , engineering , artificial intelligence , chemistry , statistics , mathematics , electrode , machine learning , anode , programming language , operating system
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.