Research on energy management of hydrogen electric coupling system based on deep reinforcement learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F23%3APU150453" target="_blank" >RIV/00216305:26210/23:PU150453 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S0360544223015682?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0360544223015682?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.energy.2023.128174" target="_blank" >10.1016/j.energy.2023.128174</a>

Result language

angličtina

Original language name

Research on energy management of hydrogen electric coupling system based on deep reinforcement learning

Original language description

In this paper, a deep reinforcement learning-based energy optimization management method for hydrogenelectric coupling system is proposed for the conversion and utilization and joint optimization operation of hydrogen, wind and solar energy forms considering information uncertainty on the demand side of smart grid. Based on the wind energy, photovoltaic energy generation and load forecast information, the method uses deep Q network to simulate the energy management strategy set of the hydrogen-electric coupling system, and obtains the optimal strategy through reinforcement learning to finally realize the optimal operation of the hydrogenelectric coupling system based on the demand response. Firstly, based on the energy management model, a research framework and equipment model for integrated energy systems is established. On the basis of fundamental theories of reinforcement learning framework, Q-learning algorithm and DQN algorithm, the empirical replay mechanism and freezing parameter mechanism to improve the performance of DQN are analyzed, and the energy management and optimization of integrated energy system is completed with the objective of economy. By comparing the performance of DQN algorithms with different parameters in integrated energy system energy management, the simulation results demonstrate the improvement of algorithm performance after inheriting the set of strategies, and verify the feasibility and superiority of deep reinforcement learning compared to genetic algorithm in integrated energy system energy management applications.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20303 - Thermodynamics

Project

<a href="/en/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Sustainable Process Integration Laboratory (SPIL)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Energy

ISSN

0360-5442

e-ISSN

1873-6785

Volume of the periodical

282

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

11

Pages from-to

„“-„“

UT code for WoS article

001042576300001

EID of the result in the Scopus database

2-s2.0-85165011665