Economic dispatching strategy of active distribution network with high proportion of renewable energy consumption constraints for GCN and DRL under dual carbon background
To achieve the dual carbon goals, improve the integration of renewable resources in Active Distribution Networks (ADN) and enhance the economic efficiency of distribution system scheduling, this paper introduces a GCN-DDPG strategy that combines Graph Convolutional Networks (GCN) with Deep Deterministic Policy Gradient (DDPG) algorithms. The strategy aims to minimize the ADN""s daily operating costs by developing aneconomicdispatch model for photovoltaic systems, loads, and energy storage systems. Subsequently, the ADN scheduling problem is discribed as a Markov Decision Process (MDP) within the framework of Deep Reinforcement Learning (DRL), defining the state space, action space, and reward function of the system. Additionally, it applies the DDPG algorithm in DRL, To address the limitations of DDPG, namely its data-driven nature and disregard for ADN topological structure, it integrates GCN to improve the DRL agent""s ability to represent and process graph data of the distribution network. Finally, the proposed model""s effectiveness and robustness are validated through simulations using an enhanced IEEE 33 benchmark system.