Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment

Abstract

The increasingly challenging task of maintaining power grid security requires innovative solutions. Novel approaches using reinforcement learning (RL) agents have been proposed to help grid operators navigate the massive decision space and nonlinear behavior of these complex networks. However, applying RL to the assessment of the security of the power grid, specifically for combinatorially difficult contingency analysis problems, has proven to be difficult to scale. The integration of quantum computing into these RL frameworks helps scale by improving computational efficiency and boosting agent proficiency by leveraging quantum advantages in action exploration and model-based interdependence. To demonstrate a proof-of-concept use of quantum computing for RL agent training and simulation, we propose a hybrid agent that runs on quantum hardware using IBM’s Qiskit Runtime. We also provide detailed insight into the construction of parameterized quantum circuits (PQCs) to generate relevant quantum output. The increased ability of this agent to maintain grid stability is demonstrated relative to a reference model without quantum enhancement using N − k contingency analysis. Additionally, we offer a comparative assessment of the training procedures for RL models integrated with a quantum backend.