A prioritized soft actor-critic algorithm for safe and efficient power distribution network reconfiguration

Introduction Dynamic network reconfiguration is a critical tool for enhancing the operational efficiency and security of power distribution networks. However, traditional model-based optimization methods are often computationally intensive and rely on precise system models, whereas conventional reinforcement learning (RL) approaches frequently overlook critical safety constraints. Methods To address these challenges, this paper proposes a novel safe deep reinforcement learning (SDRL) framework, Prioritized Experience Replay Safe Soft Actor-Critic for Network Reconfiguration (PER-SSAC-NR). The proposed framework balances operational cost reduction and safety constraint adherence by combining a Safe Soft Actor-Critic (SSAC) architecture with parameter-sharing twin critics and prioritized experience replay. Results Comprehensive experiments on the standard IEEE 33-bus and 69-bus test systems show that PER-SSAC-NR outperforms baseline RL methods in both learning speed and final policy quality. The agent achieves near-optimal power losses of 0.1410 MW and 0.0992 MW on the 33-bus and 69-bus systems, respectively, closely approaching the 0.1395 MW and 0.0985 MW solutions from the model-based optimization benchmark while satisfying all operational safety constraints. Discussion These results demonstrate that the proposed PER-SSAC-NR framework can provide safe, efficient, and scalable data-driven decision-making for power distribution network reconfiguration.