Learning to run a power network with trust
Antoine Marot (Reseau de Transport d'Electricite)
Benjamin Donnot (Reseau de Transport d'Electricite)
Karim Chaouache (Reseau de Transport d'Electricite)
Adrian Kelly (Electric Power Research Institute (EPRI) Europe)
Qiuhua Huang (Pacific Northwest National Laboratory)
Ramij Raja Hossain (Iowa State University)
Jochen Cremer (TU Delft - Intelligent Electrical Power Grids)
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Abstract
Artificial agents are promising for real-time power network operations, particularly, to compute remedial actions for congestion management. However, due to high reliability requirements, purely autonomous agents will not be deployed any time soon and operators will be in charge of taking action for the foreseeable future. Aiming at designing assistant for operators, we instead consider humans in the loop and propose an original formulation. We first advance an agent with the ability to send to the operator alarms ahead of time when the proposed actions are of low confidence. We further model the operator's available attention as a budget that decreases when alarms are sent. We present the design and results of our competition “Learning to run a power network with trust” in which we evaluate our formulation and benchmark the ability of submitted agents to send relevant alarms while operating the network to their best.