Nowadays, distribution network operators are urged by regulatory authorities to reduce the load disruptions due to extreme weather events, i.e., to enhance network resilience: in particular, in Italy they are required to present a yearly plan (called “resilience plans”) describing the interventions aimed to improve network resilience. To this purpose, they need new methodologies and tools to assess the network resilience and to quantify the benefits of countermeasures. This paper proposes the application of a risk-based framework and tool to assess the impacts of extreme weather events in T&D grids, which anticipate critical network situations in presence of incumbent weather threats. To do this, the forecasting of weather events is combined with the component vulnerability models in order to predict which components are more prone to fail. Based on this set of components, the set of most risky contingencies is identified and their impacts on the distribution network in terms of unsupplied load are quantified. The major advantage of the applied methodology is its generality: in fact, it is applicable to both distribution and transmission systems as well as integrated transmission and distribution (T&D) systems, considering the peculiarities of each type of grid, in terms of operation, maintenance and component vulnerabilities. In particular, the application refers to a distribution network connected to a portion of high voltage transmission system in a mountainous zone, with focus on two major threats in the area, i.e., wet snow and fall of trees induced by combined wind and snow. The methodology also quantifies the benefits brought to the system resilience by countermeasures such as reconductoring, optimal reconfiguration or new right-of-way maintenance procedures. Simulations demonstrate the ability of the methodology to support T&D operators in an operational planning context in case of different incumbent threats.
Impact of extreme weather events on urban human flow: A perspective from location-based service data