Virtual generation alliance automatic generation control based on deep reinforcement learning

Abstract

This article proposes a distributed hierarchical automatic generation control (AGC) frame- work with multiple regulation units in the performance-based frequency regulation market, named virtual generation alliance automatic generation control (VGA-AGC), aiming to achieve the coordination of control algorithm and AGC dispatch algorithm and adapt to the development trend of AGC from centralized framework to centralized-decentralized framework. The framework also involves a multi agent distributed multiple improved deep deterministic policy gradient (MADMI-TD3) algorithm that is characterized by excellent global search capability and optimizing speed. The algorithm can help create an optimal AGC strategy in a randomization environment so as to obtain an optimal cooperative control of AGC. According to a simulation verification on the LFC model for an interconnected power grid of a province, the algorithm is superior to the current algorithms and conventional engineering methods in terms of control performance and economic benefits. In other words, the algorithm can improve control performance and reduce the regulation mileage payment.