A Stochastic Model for Distribution Network Switching Under Temperature Variations and Uncertainty in Demand

Traditional network switching involves modifying the distribution network’s topology to minimize the dissipated power based on consumer demand. Multiple kinds of loads and their stochastic nature introduce variations in power dissipation owing to the uncertain reliance on the precision of the load forecast. Despite this, some existing models overlook consumer diversity and uncertainty. Even studies that account for these factors often neglect the impact of temperature fluctuations on power usage and network structure. To address this gap, this present article proposes a novel network reconfiguration approach that simultaneously considers load type, stochastic behavior, ambient temperature, linear modeling, and line limit calculations. By incorporating ambient temperature effects into stochastic load modeling, the proposed method enhances analysis accuracy. Additionally, the use of a linear model ensures computational efficiency and precise results, while explicit line limit calculations guarantee reliable network operation. The findings reveal that small systems maintain stable configurations with temperature increases up to 50%, but experience significant power losses and voltage drops when temperatures rise by 60%–100%, leading to topology adjustments. In larger networks, reconfiguration becomes ineffective for temperature increases beyond 20%, requiring line capacity upgrades. Furthermore, while some actual grids remain stable under temperature variations up to 70%, others show topology changes with only a 10% temperature increase, highlighting the substantial impact of temperature fluctuations on system performance.