Explainable reinforcement learning for distribution network reconfiguration
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F24%3A50022658" target="_blank" >RIV/62690094:18470/24:50022658 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2352484724003147?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352484724003147?via%3Dihub</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.egyr.2024.05.031" target="_blank" >10.1016/j.egyr.2024.05.031</a>
Alternative languages
Result language
angličtina
Original language name
Explainable reinforcement learning for distribution network reconfiguration
Original language description
The lack of transparency in reinforcement learning methods' decision-making process has resulted in a significant lack of trust towards these models, subsequently limiting their utilization in critical decisionmaking applications. The use of reinforcement learning in distribution network reconfiguration is an inherently sensitive application due to the need to change the states of the switches, which can significantly impact the lifespan of the switches. Consequently, executing this process requires meticulous and deliberate consideration. This study presents a new methodology to analyze and elucidate reinforcement learning-based decisions in distribution network reconfiguration. The proposed approach involves the training of an explainer neural network based on the decisions of the reinforcement learning agent. The explainer network receives as input the active and reactive power of the buses at each hour and outputs the line states determined by the agent. To delve deeper into the inner workings of the explainer network, attribution methods are employed. These techniques facilitate the examination of the intricate relationship between the inputs and outputs of the network, offering valuable insights into the agent's decision-making process. The efficacy of this novel approach is demonstrated through its application to both the 33- and 136 -bus test systems, and the obtained results are presented.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20201 - Electrical and electronic engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Energy Reports
ISSN
2352-4847
e-ISSN
2352-4847
Volume of the periodical
11
Issue of the periodical within the volume
June
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
13
Pages from-to
5703-5715
UT code for WoS article
001246417900001
EID of the result in the Scopus database
2-s2.0-85194043749