TY - GEN
T1 - Optimal Operation of a Hydrogen-based Building Multi-Energy System under Uncertainties
AU - Yu, Liang
AU - Qin, Shuqi
AU - Shen, Chao
N1 - Publisher Copyright:
© 2021 IEEE
PY - 2021
Y1 - 2021
N2 - This paper investigates an optimal operation problem of a hydrogen-based building multi-energy system (HBMES). Specifically, we first formulate an operational cost minimization problem of HBMES. Since there are uncertain parameters, inexplicit building thermal dynamics model, temporally coupled operational constraints, as well as the coupling between electricity and heat, solving the minimization problem is nontrivial. To overcome the challenge, we reformulate the problem as a Markov decision process (MDP). Then, we design an algorithm to solve the MDP based on deep deterministic policy gradients and prioritized experience replay. The designed algorithm supports real-time decision without any process of searching optimal solution. Performance evaluation verifies the effectiveness and robustness of the designed algorithm.
AB - This paper investigates an optimal operation problem of a hydrogen-based building multi-energy system (HBMES). Specifically, we first formulate an operational cost minimization problem of HBMES. Since there are uncertain parameters, inexplicit building thermal dynamics model, temporally coupled operational constraints, as well as the coupling between electricity and heat, solving the minimization problem is nontrivial. To overcome the challenge, we reformulate the problem as a Markov decision process (MDP). Then, we design an algorithm to solve the MDP based on deep deterministic policy gradients and prioritized experience replay. The designed algorithm supports real-time decision without any process of searching optimal solution. Performance evaluation verifies the effectiveness and robustness of the designed algorithm.
KW - deep reinforcement learning
KW - hydrogen-based multi-energy systems
KW - operational cost
UR - https://www.scopus.com/pages/publications/85128016700
U2 - 10.1109/CAC53003.2021.9727527
DO - 10.1109/CAC53003.2021.9727527
M3 - 会议稿件
AN - SCOPUS:85128016700
T3 - Proceeding - 2021 China Automation Congress, CAC 2021
SP - 1583
EP - 1588
BT - Proceeding - 2021 China Automation Congress, CAC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 China Automation Congress, CAC 2021
Y2 - 22 October 2021 through 24 October 2021
ER -