Multi-Objective Graph-Based Reinforcement Learning for Voltage and Frequency Stability in Grid-Connected Renewable Energy Systems

The increasing penetration of renewable energy sources in modern power grids poses significant challenges in maintaining voltage stability, frequency regulation, and optimal power dispatch due to the stochastic nature of generation and demand. This paper introduces a multi-objective graph-based reinforcement learning (MO-GRL) framework that optimally manages energy storage systems (ESS) to enhance voltage stability, provide synthetic inertia for frequency support, and maximize energy efficiency. The proposed model integrates Koopman operator theory for predictive ESS dynamics, graph neural networks for learning spatial relationships in power networks, and soft actor-critic reinforcement learning for multi-objective optimization. Furthermore, a Lyapunov-based stability constraint is incorporated to ensure the robustness of voltage and frequency control policies under uncertain conditions. Compared to conventional techniques, MO-GRL dynamically adjusts control policies based on real-time grid states, offering improved adaptability. Simulations on an IEEE 33-bus test system demonstrate significant improvements, including a 42% reduction in voltage deviations, a 39% decrease in frequency deviations, and a 37% improvement in ESS utilization efficiency. The proposed method effectively mitigated voltage spikes that previously rose above 1.04 p.u., successfully limiting peak voltage levels to 1.03 p.u. and lower. Similarly, during peak evening demand conditions, the voltage dips that fell below 0.95 p.u. were stabilized above 0.96 p.u. The controlled voltage profile-maintained values near the desired reference of 1.00 p.u., ensuring improved voltage stability across the 24-hour operation.