Stability Enhancement through Reinforcement Learning: Load Frequency Control Case Study
Author(s): Sara Eftekharnejad, Ali Feliachi
Reference: Proceedings of Bulk Power System Dynamics and Control – VII, August 19-24, 2007, Charleston, South Carolina, USA
Abstract: A multi-agent based control architecture using reinforcement learning is proposed to enhance power system stability. It consists of a layer of local agents and a global agent that coordinates the behavior of the local agents. Load frequency control is chosen as a case study to demonstrate the viability of the proposed concept. Simulation results illustrate the effectiveness of this controller as an online automatic generation controller (AGC) for a two area system, with and without generation rate constraints (GRC).
Keywords: multi-agent systems, reinforcement learning, load frequency control, stability, coordination