Explainable reinforcement learning for distribution network reconfiguration

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Explainable reinforcement learning for distribution network reconfiguration

Popis výsledku v původním jazyce

The lack of transparency in reinforcement learning methods&apos; 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&apos;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.

Název v anglickém jazyce

Explainable reinforcement learning for distribution network reconfiguration

Popis výsledku anglicky

The lack of transparency in reinforcement learning methods&apos; 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&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Energy Reports

ISSN

2352-4847

e-ISSN

2352-4847

Svazek periodika

11

Číslo periodika v rámci svazku

June

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

5703-5715

Kód UT WoS článku

001246417900001

EID výsledku v databázi Scopus

2-s2.0-85194043749