A Comparative Study of Reinforcement Learning Algorithms for Distribution Network Reconfiguration with Deep Q-learning-based Action Sampling

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...

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Vydané v:	IEEE access Ročník 11; s. 1
Hlavní autori:	Gholizadeh, Nastaran, Kazemi, Nazli, Musilek, Petr
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 01.01.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Comparative studies data-driven control deep Q-learning Distribution network reconfiguration Distribution networks Electric power demand Electric vehicles Load modeling Loss reduction Machine learning Machine learning algorithms Mathematical models Optimization proximal policy optimization Q-learning Reconfiguration reinforcement learning Sampling methods soft actor-critic Switches
ISSN:	2169-3536, 2169-3536
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