A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling

A Generalized Deep Reinforcement Learning Model for Distribution Network Reconfiguration with Power Flow-Based Action-Space Sampling

Distribution network reconfiguration (DNR) is used by utilities to enhance power system performance in various ways, such as reducing line losses. Conventional DNR algorithms rely on accurate values of network parameters and lack scalability and optimality. To tackle these issues, a new data-driven...

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Bibliographic Details
Main Authors:	Nastaran Gholizadeh, Petr Musilek
Format:	Article
Language:	English
Published:	MDPI AG 2024-10-01
Series:	Energies
Subjects:	data-driven control deep reinforcement learning distribution network reconfiguration deep Q-learning dueling deep Q-learning
Online Access:	https://www.mdpi.com/1996-1073/17/20/5187
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