Large-scale deep reinforcement learning method for energy management of power supply units considering regulation mileage payment - Qian, Ting; Yang, Cheng (Frontiers in Energy Research, 2024)
To improve automatic generation control (AGC) performance and reduced the wastage of regulation resources in interconnected grids including high proportion renewable energy, a multi-area integrated AGC (MAI-AGC) framework is proposed to solve the coordination problem of secondary frequency regulation between different areas. In addition, a cocktail exploration multi-agent deep deterministic policy gradient (CE-MADDPG) algorithm is proposed as the framework algorithm. In this algorithm, the controller and power distributor of an area are combined into one agent, which can directly output the power generation command of different units. Moreover, the cocktail exploration strategy as well as various other techniques are introduced to improve robustness of the framework. Through centralized training and decentralized execution, the proposed method can nonlinearly and adaptively derive the optimal coordinated control strategies for multiple agents and is verified on two-area LFC model of southwest China and the four-area LFC model of the China Southern Grid (CSG). Index -Automatic generation control; multi-agent deep deterministic policy gradient algorithm; optimal coordinated control strategies; frequency regulation mileage payment.