Intelligent load-frequency control in a deregulated environment: Continuous-valued input, extended classifier system approach

This study presents an intelligent solution for load-frequency control in a restructured power system using a modified traditional frequency response model, suitable for a bilateral-based deregulation policy. The new approach is based on an extended classifier system with continuous-valued inputs (XCSR) which is the most successful learning classifier systems. The proposed intelligent solution does not require an accurate model of the system and is more flexible in specifying the control objectives. Also it is an automated learning-based approach. It means there is not any need to training data and expert knowledge of the system to determine the states and actions, which is a very time-consuming and difficult stage of designing reinforcement learning-based solutions. To demonstrate the effectiveness of the proposed method, its performance on a three-area restructured power system with possible contract scenarios, large load demands and area disturbances has been compared with multi-agent reinforcement learning-based controller. The results show that the proposed intelligent solution achieves good robust performance for a wide range of load changes in the presence of system nonlinearities and has good ability to track the contracted and non-contracted demands.