  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.
