Learning to run a power network with trust

TL;DR

This paper introduces a human-in-the-loop approach for power network management, where agents send alarms to operators based on confidence levels, aiming to improve reliability and trust in real-time operations.

Contribution

It proposes a novel formulation integrating operator attention as a budget and evaluates agents in a competition setting for alarm relevance and network operation.

Findings

Agents can effectively predict low-confidence actions.

Alarm strategies influence operator attention management.

Benchmark results show varying agent performance.

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.