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Poster Communications Year : 2023

Decentralized Partially Observable Markov Decision Process for Power Grid Management

Processus de Décision de Markov Décentralisé et Partiellement Observable pour la Gestion du Réseau Electrique

Abstract

Following the energy transition, Réseau de Transport d' Electricité (RTE) - the French Transport System Operator- is developing new adaptive zonal automatons. Each automaton monitors a zone of the power grid thanks to an optimization algorithm. An automaton can act on the power grid configuration in case of constraints. Each automaton can receive a target setpoint from human operators to decide the appropriate actions. Examples of a target path include (un)desirable configurations in future hours or information about other zones. To make operators’ work easier, RTE needs a decision-support assistant to recommend relevant target setpoints. Reinforcement Learning (RL) is a promising method to elaborate such target setpoints. However, RL requires a high number of training iterations which is time-consuming. In this poster, we present our research to emulate an automaton with a short response time, thanks to a RL approach, to afford a sufficient number of iterations and reasonable computation times during training.
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Dates and versions

hal-04396121 , version 1 (15-01-2024)

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  • HAL Id : hal-04396121 , version 1

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Eva Boguslawski, Alessandro Leite, Marc Schoenauer, Matthieu Dussartre, Benjamin Donnot. Decentralized Partially Observable Markov Decision Process for Power Grid Management. Reinforcement Learning Summer School 2023, Jun 2023, Barcelone, Spain. . ⟨hal-04396121⟩
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