Imitation Learning for Intra-Day Power Grid Operation through Topology Actions
This work addresses the challenge of efficient power grid management for dispatchers, but it is incremental as it builds on existing imitation learning methods applied to a specific domain.
The paper tackled the problem of using imitation learning to assist in power grid operation by training a neural network on expert agents' actions, finding that while classification accuracy was limited, the resulting agent performed only slightly worse than experts, and hybrid agents matched expert performance with lower computational cost.
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.