Sensors-Aided Distribution System Resilience Enhancement by Using Meteorological Data

With the change of climate, the influence of extreme weather on the distribution system is increasingly serious. However, the existing work fails to explicitly consider the influence mechanism of meteorological factors on the operation of distribution systems and thus ignores a shortcut to resilience enhancement. To this end, this paper proposes a meteorological data-based resilience enhancement method to minimize the impact of extreme weather, while a set of sensors are deployed for monitoring the evolving meteorological factors. In virtue of real-time meteorological data collected by those sensors, the proposed model is aimed at minimizing the total amount of unsupplied loads during the extreme weather by integrating dynamic line rating (DLR) techniques. A novel scenario-cutting method is then developed to merge similar heat-blocking scenarios in order to reduce the computational complexity with a slight sacrifice of decision accuracy. The problem is finally transformed into a mixed integer second-order cone programming (MISOCP) that can be efficiently solved by commercial solvers. Case studies on the IEEE 33-bus system verify that the proposed method can enhance the distribution system resilience more cost-effective compared with the existing enhancement methods.