Adaptive scheduling of distribution networks with massive DERs and EVs: a constraint-guided deep reinforcement learning approach

The high penetration of distributed energy resources (DERs) and electric vehicles (EVs) in distribution networks introduces significant uncertainty and complexity to grid optimal scheduling. Therefore, there is an urgent need to develop an adaptive scheduling framework that simultaneously balances computational efficiency, operational safety, and economic performance. In this paper, a constraint-guided deep reinforcement learning (CG-DRL) framework is proposed to solve adaptive scheduling problem of distribution networks with high DER and EV penetration. First, the real-time scheduling model of distribution network with massive DERs and EV is established. Second, to mitigate potential violations of massive physical constraints arising, a hierarchical active constraint identification (H-ACI) model is proposed, featuring a two-layer architecture. A robust, model-based upper layer uses the umbrella constraint method to establish a foundational safety manifold, while a faster, data-driven lower layer employs a stacked denoising autoencoder (SDAE) to provide real-time refinement. The synergistic fusion offers safety assurances while achieving the computational efficiency required for adaptive scheduling. Subsequently, to address the high dimensional action space, a modified deep deterministic policy gradient (DDPG) architecture is employed is employed, which integrates guidance from the H-ACI manifold to enable feasible exploration. Finally, Case studies on modified IEEE 33-bus networks demonstrate improved efficiency and constraint satisfaction in both normal and extreme circumstances, validating the practicality of the proposed approach.