A Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration With Deep Q-Learning-Based Action Sampling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F23%3A50020287" target="_blank" >RIV/62690094:18470/23:50020287 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10040655" target="_blank" >https://ieeexplore.ieee.org/document/10040655</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ACCESS.2023.3243549" target="_blank" >10.1109/ACCESS.2023.3243549</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration With Deep Q-Learning-Based Action Sampling

Popis výsledku v původním jazyce

Distribution network reconfiguration (DNR) is one of the most important methods to cope with the increasing electricity demand due to the massive integration of electric vehicles. Most existing DNR methods rely on accurate network parameters and lack scalability and optimality. This study uses model-free reinforcement learning algorithms for training agents to take the best DNR actions in a given distribution system. Five reinforcement algorithms are applied to the DNR problem in 33- and 136-node test systems and their performances are compared: deep Q-learning, dueling deep Q-learning, deep Q-learning with prioritized experience replay, soft actor-critic, and proximal policy optimization. In addition, a new deep Q-learning-based action sampling method is developed to reduce the size of the action space and optimize the loss reduction in the system. Finally, the developed algorithms are compared against the existing methods in literature.

Název v anglickém jazyce

A Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration With Deep Q-Learning-Based Action Sampling

Popis výsledku anglicky

Distribution network reconfiguration (DNR) is one of the most important methods to cope with the increasing electricity demand due to the massive integration of electric vehicles. Most existing DNR methods rely on accurate network parameters and lack scalability and optimality. This study uses model-free reinforcement learning algorithms for training agents to take the best DNR actions in a given distribution system. Five reinforcement algorithms are applied to the DNR problem in 33- and 136-node test systems and their performances are compared: deep Q-learning, dueling deep Q-learning, deep Q-learning with prioritized experience replay, soft actor-critic, and proximal policy optimization. In addition, a new deep Q-learning-based action sampling method is developed to reduce the size of the action space and optimize the loss reduction in the system. Finally, the developed algorithms are compared against the existing methods in literature.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

IEEE Access

ISSN

2169-3536

e-ISSN

2169-3536

Svazek periodika

11

Číslo periodika v rámci svazku

February

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

13714-13723

Kód UT WoS článku

000933724700001

EID výsledku v databázi Scopus

2-s2.0-85148445614