Title : ( Distribution Network Reconfiguration in the Presence of Distribution Generation Using Deep Reinforcement Learning )
Authors: Amirhossein Ghaemipour , Habib Rajabi Mashhadi , Seyed Hossein Mostafavi ,
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Abstract
Due to the increasing complexity of distribution networks for various reasons, such as the widespread use of distributed generation, demand response, various load management programs, and smart devices in network control, efficient distribution network (DN) operation has become an important issue. Utilizing distribution network reconfiguration (DNR) in these situations can provide appropriate flexibility to achieve the goals of the network operator. In this research, reinforcement learning theory is used for DNR which, unlike other methods, does not require any previously prepared data or parameters. Based on trial and error and receiving feedback on its performance from the environment, it reaches the optimal solution. The deep Q network (DQN) algorithm minimizes power losses and improves the voltage profile. This design has been tested and verified on an IEEE 16-bus DN.
Keywords
, Distribution network, reconfiguration, Distribution Generation, Deep reinforcement learning, Deep Q Network (DQN) algorithm,
@inproceedings{paperid:1104024,
author = {Ghaemipour, Amirhossein and Rajabi Mashhadi, Habib and Mostafavi, Seyed Hossein},
title = {Distribution Network Reconfiguration in the Presence of Distribution Generation Using Deep Reinforcement Learning},
booktitle = {2025 6th International Conference on Optimizing Electrical Energy Consumption (OEEC), 25-26 February, 2025},
year = {2025},
location = {IRAN},
keywords = {Distribution network; reconfiguration;
Distribution Generation; Deep reinforcement learning; Deep Q
Network (DQN) algorithm;},
}
%0 Conference Proceedings
%T Distribution Network Reconfiguration in the Presence of Distribution Generation Using Deep Reinforcement Learning
%A Ghaemipour, Amirhossein
%A Rajabi Mashhadi, Habib
%A Mostafavi, Seyed Hossein
%J 2025 6th International Conference on Optimizing Electrical Energy Consumption (OEEC), 25-26 February, 2025
%D 2025
