Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment

2025-04-19

AI Summary

Traditional reinforcement learning (RL) struggles with the high-dimensional combinatorial explosion and stringent real-time requirements inherent in N-k security assessment for power systems. Method: This paper proposes a quantum-enhanced RL framework, pioneering the integration of parameterized quantum circuits (PQCs) into the policy network of an RL agent, implemented via IBM Qiskit Runtime for quantum-classical hybrid decision-making. A grid-physical-constraint-aware PQC encoding scheme is designed to improve action-space exploration efficiency and enhance modeling of state-action dependencies. Contribution/Results: Evaluated on standard test systems, the proposed method achieves a 23% improvement in decision accuracy and reduces critical fault identification latency by 41% compared to classical RL baselines. These results demonstrate the effectiveness and feasibility of quantum computing in accelerating emergency analysis and improving generalization capability in power grid security assessment.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Learning to Search
• Multiagent Systems: Adversarial Agents

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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 power grid security assessment, specifically for combinatorially troublesome contingency analysis problems, has proven 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) for generating relevant quantum output. This agent's proficiency at maintaining grid stability is demonstrated relative to a benchmark 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.

Problem

• Enhancing power grid security via quantum-reinforced learning
• Scaling RL for contingency analysis with quantum computing
• Improving computational efficiency in grid stability assessment

Innovation

• Quantum-enhanced RL for grid security
• Hybrid agent using IBM Qiskit Runtime
• Parameterized quantum circuits for output