Coordinated automatic generation control of interconnected power system with imitation guided exploration multi-agent deep reinforcement learning

An intelligent automatic generation control (IAGC) framework is proposed to address the coordination problems between AGC controllers in multi-area power systems. In this framework, every area of the power system consists of an adaptive proportional-integral (PI) controller that employs a tuner to regulate coefficients in real time. The tuner of each adaptive proportional-integral (PI) controller adopts an imitation guided-exploration multi-agent twin-delayed deep deterministic policy gradient (IGE-MATD3) algorithm, thereby realizing a multiarea coordinated AGC. To improve the robustness and adaptability of the IAGC framework, the proposed algorithm incorporates imitation learning and outputs the optimal coordinate control strategy of several controllers. As demonstrated by a simulation of the China Southern Grid four-area power system model, an IAGC framework can improve dynamic control performance and reduce the regulation mileage payment of the operator in every area.

Automated summary

The research proposes an intelligent automatic generation control (IAGC) framework that utilizes adaptive proportional-integral (PI) controllers and imitation guided-exploration multi-agent twin-delayed deep deterministic policy gradient (IGE-MATD3) algorithm to achieve multi-area coordinated AGC, improving control performance.