Adaptive SAC-Based Correction of Sectionalized Operating Configurations in Medium-Voltage Distribution Systems

In medium-voltage distribution systems, switch states determine how sections are grouped and supplied by feeders, defining the sectionalized operating configuration. Changes in load demand and distributed generation output can increase losses and cause voltage violations, making an existing configuration unsuitable for the new conditions. Switching corrections can redistribute power flows to reduce losses and voltage violations, but their benefits must be balanced against switching costs. Variations in loss costs also change return scales, which can impair reinforcement learning (RL) methods that use a fixed value-learning scale. To address these challenges, we propose an adaptive soft actor-critic (A-SAC) algorithm to correct sectionalized operating configurations and minimize cumulative operating cost. The dynamic adaptation mechanism adjusts the value-learning scale as operating conditions change, guiding the agent to learn lower-cost configuration correction policies while preserving the original cost objective. A masked softmax output layer excludes infeasible switching actions. Tests on a 37-bus test system under normal, high photovoltaic (PV) output, and changing source/load conditions show cost reductions of 3.2–6.2% relative to retaining the initial configuration and lower costs than other advanced RL algorithms. In the changing source/load scenario, the mean cost is 3.2% lower than that of soft actor-critic (SAC).