ABSTRACT
This chapter presents an introduction of multiagent systems (MAS) and introduces a multiagent reinforcement-learning-based Automatic generation control (AGC) scheme. MASs have been widely used in many fields of control engineering, such as power system control, manufacturing/industrial control, congestion control, distributed control, hybrid control, robotics and formation control, remote control, and traffic control. Multiagent control systems represent control schemes that are inherently distributed and consist of multiple entities/agents. The chapter discusses general frameworks for agent-based control systems based upon the foundations of agent theory. It examines the capability of reinforcement learning in the proposed AGC strategy and also discusses the application of genetic algorithm to determine actions and states during the learning process. One of the adaptive and nonlinear intelligent control techniques that can be effectively applicable in the power system AGC design is reinforcement learning. The chapter explains the possibility for building of more agents such as estimator agents to cope with real-world AGC systems.
