Imitation Learning for Intra-Day Power Grid Operation through Topology Actions

TL;DR

This paper explores imitation learning for power grid management, training neural networks to mimic expert agents using topology actions, achieving near-expert performance with lower computational costs.

Contribution

It demonstrates that imitation learning can produce efficient power grid agents that perform comparably to expert rules with reduced computational effort.

Findings

Neural networks perform slightly worse than expert agents.

Class imbalance and overlap limit classification accuracy.

Hybrid agents match expert performance with fewer simulations.

Abstract

Power grid operation is becoming increasingly complex due to the increase in generation of renewable energy. The recent series of Learning To Run a Power Network (L2RPN) competitions have encouraged the use of artificial agents to assist human dispatchers in operating power grids. In this paper we study the performance of imitation learning for day-ahead power grid operation through topology actions. In particular, we consider two rule-based expert agents: a greedy agent and a N-1 agent. While the latter is more computationally expensive since it takes N-1 safety considerations into account, it exhibits a much higher operational performance. We train a fully-connected neural network (FCNN) on expert state-action pairs and evaluate it in two ways. First, we find that classification accuracy is limited despite extensive hyperparameter tuning, due to class imbalance and class overlap. Second, as a power system agent, the FCNN performs only slightly worse than expert agents. Furthermore, hybrid agents, which incorporate minimal additional simulations, match expert agents' performance with significantly lower computational cost. Consequently, imitation learning shows promise for developing fast, high-performing power grid agents, motivating its further exploration in future L2RPN studies.