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

Nastaran Gholizadeh and Petr Musilek ()
Additional contact information
Nastaran Gholizadeh: Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada
Petr Musilek: Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada

Energies, 2024, vol. 17, issue 20, 1-18

Abstract: 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 algorithm based on reinforcement learning is developed for DNR in this paper. The proposed algorithm comprises two main parts. The first part, named action-space sampling, aims at analyzing the network structure, finding all feasible reconfiguration actions, and reducing the size of the action space to only the most optimal actions. In the second part, deep Q-learning (DQN) and dueling DQN methods are used to train an agent to take the best switching actions according to the switch states and loads of the system. The results show that both DQN and dueling DQN are effective in reducing system losses through grid reconfiguration. The proposed methods have faster execution time compared to the conventional methods and are more scalable.

Keywords: data-driven control; deep reinforcement learning; distribution network reconfiguration; deep Q-learning; dueling deep Q-learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/20/5187/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/20/5187/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:20:p:5187-:d:1501500

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().