Reinforcement Learning for Optimal Power Flow in Smart Grids

Abstract

In this paper, we present an enhanced Q-learning approach with constraint-aware reward shaping for solving the optimal power flow (OPF) problem in smart grids. Unlike conventional reinforcement learning applications, our methodology integrates adaptive exploration strategies and multiobjective optimization specifically designed for power system operational constraints. The smart grid environment incorporates real-time phasor measurement unit (PMU) data, dynamic load variations, and renewable energy fluctuations to provide comprehensive system observability. Our approach achieved significant performance improvements with a 7.5% operational cost reduction compared to the Newton–Raphson method ($45,200 versus $48,900 daily operational cost), 5.2% improvement over the interior point method, and 3.8% enhancement over particle swarm optimization. The reinforcement learning agent demonstrated superior convergence speed of 5 ms compared to 120 ms for traditional methods, reduced constraint violations to 0.3% compared to 0.8% for conventional approaches, and achieved rapid adaptation to sudden load changes within 2–3 versus 10–15 s required by traditional optimization methods. Comprehensive validation on IEEE 30-bus system with scalability analysis extending to IEEE 57 and 118-bus systems confirms the approach’s effectiveness for real-time smart grid control, achieving computational efficiency of 200 solutions per second. The study addresses practical implementation challenges including communication delays, measurement uncertainties, and cybersecurity considerations, providing a robust framework for real-world deployment in modern power systems.