Deep Reinforcement Learning Enabled Bi-Level Robust Parameter Optimization of Hydropower-Dominated Systems for Damping Ultra-Low Frequency Oscillation

Abstract

This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations (ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control formulation that can only deal with a limited number of operating conditions, the proposed method reformulates the control problem into a bi-level robust parameter optimization model. This allows us to consider a wide range of system operating conditions. To speed up the bi-level optimization process, the deep deterministic policy gradient (DDPG) based deep reinforcement learning algorithm is developed to train an intelligent agent. This agent can provide very fast lower-level decision variables for the upper-level model, significantly enhancing its computational efficiency. Simulation results demonstrate that the proposed method can achieve much better damping control performance than other alternatives with slightly degraded dynamic response performance of the governor under various types of operating conditions.

Keywords

• Bi-level robust parameter optimization
• deep reinforcement learning
• deep deterministic policy gradient
• ultra-low frequency oscillation
• damping control stability