REINFORCEMENT LEARNING CONTROLLERS FOR AUTOMATIC GENERATION CONTROL IN POWER SYSTEMS HAVING REHEAT UNITS WITH GRC AND DEAD-BAND

摘要

A new automatic generation controller (AGC) design approach, adopting reinforcement learning (RL) techniques, was recently proposed. In this paper we demonstrate the design and performance of controllers based on this RL approach for automatic generation control of systems consisting of units having complex dynamics—the reheat type of thermal units. For such systems, we also assess the capabilities of RL approach in handling realistic system features such as network changes, parameter variations, generation rate constraint (GRC), and governor deadband.