Towards an AI assistant for human grid operators

Power systems are becoming more complex to operate in the digital age. As a result, real-time decision-making is getting more challenging as the human operator has to deal with more information, more uncertainty, more applications and more coordination. While supervision has been primarily used to help them make decisions over the last decades, it cannot reasonably scale up anymore. There is a great need for rethinking the human-machine interface under more unified and interactive frameworks. Taking advantage of the latest developments in Human-machine Interactions and Artificial intelligence, we share the vision of a new assistant framework relying on an hypervision interface and greater bidirectional interactions. We review the known principles of decision-making that drives the assistant design and supporting assistance functions we present. We finally share some guidelines to make progress towards the development of such an assistant

Towards an AI assistant for human grid operators

Power systems are becoming more complex to operate in the digital age. As a result, real-time decision-making is getting more challenging as the human operator has to deal with more information, more uncertainty, more applications and more coordination. While supervision has been primarily used to help them make decisions over the last decades, it cannot reasonably scale up anymore. There is a great need for rethinking the human-machine interface under more unified and interactive frameworks. Taking advantage of the latest developments in Human-machine Interactions and Artificial intelligence, we share the vision of a new assistant framework relying on an hypervision interface and greater bidirectional interactions. We review the known principles of decision-making that drives the assistant design and supporting assistance functions we present. We finally share some guidelines to make progress towards the development of such an assistant

A Decision Support Assistant to Operate a Power Grids with Zonal Automatons

International audienceFollowing 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 design an emulator of 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

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

International audienceFollowing 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