A multi-objective energy coordinative and management policy for solid oxide fuel cell using triune brain large-scale multi-agent deep deterministic policy gradient

Jiawen Li

Applied Energy, 2022, vol. 324, issue C, No S0306261922006675

Abstract: In this paper, a data-driven multi-objective energy coordinative management policy is proposed in order to enhance the net output power and efficiency of a solid oxide fuel cell (SOFC) and prevent constraint violations. This study focuses on the optimization agent and controller design for a SOFC power system to maintain stable oxygen excess ratio (OER) and fuel utilization (FU) ratio as well as meet the load demand simultaneously. The optimization agent is responsible for output the reference OER and FU, aiming to achieve maximum net output power and operational efficiency as well as dynamic constraint satisfaction times in terms of OER and FU in real time. By applying reference OER and FU settings, the air and hydrogen flow within the SOFC can be effectively controlled by coordination of the air control agent and hydrogen control agent, respectively. In addition, a triune brain large-scale multi-agent deep deterministic policy gradient algorithm (TBL-MADDPG) is proposed. In order to improve the robustness of the proposed policy, the design of TBL-MADDPG entails curriculum learning, imitation learning and a large-scale multi-agent training framework. The performance of this proposed method is verified by the experiment.

Keywords: Solid oxide fuel cell, data-driven energy coordinative management policy; Oxygen excess ratio; Efficiency; Constraint violations; Large-scale deep reinforcement learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261922006675
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:324:y:2022:i:c:s0306261922006675

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.119313

Access Statistics for this article

Applied Energy is currently edited by J. Yan

More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().