# Quantification of the Benefits for Power System of Resilience Boosting Measures
## 1 Introduction  
Extreme events are affecting power systems more and more frequently, with consequent infrastructure disruption and loss of load supply. Transmission System Operators (TSOs) are thus pushed to evaluate the impact of multiple, weather-dependent outages of components, potentially leading to widespread blackouts, with the final aim to set up preventive and/or corrective measures to absorb the effects of such disruptive events and to recover fast. This objective can be concisely stated as increasing system resilience [1; 2], and it is consistent with the more stringent requirements posed by regulatory entities [3]. Different types of measures can be considered to boost system resilience, in different time frames from long term planning to operational planning and real time operation. Long term measures may require months to years to be implemented, and typically aim to make components more robust (hardening measures). Operational planning and real time measures include systems for enhancing situational awareness and actions that can be activated within short time frames, e.g., changing the operating conditions or alerting repair crews (active measures). In recent literature, some tools have been proposed to evaluate the benefits deriving from the deployment of hardening measures: For example, in [1] the authors evaluate the benefits from hardening solutions to boost UK grid resilience to storms by computing long term indicators typically used for reliability. However, little attention is paid to the modeling of operational measures: For example, in [4] the authors present a tool aimed at proposing optimal generation redispatching and grid reconfiguration actions to avoid damages of overhead lines (OHLs) due to wet snow sleeves. Reference [5] describes a tool which forecasts the potential outages due to tree falls on a distribution network by elaborating bigamounts of data from different informative sources (historical series of failures, weather monitoring systems, etc.). Operational measures for resilience enhancement can greatly benefit from big data analytics: In fact, big data applications can provide reasonable probabilistic models of a weather hazard for both long- and medium/short term analyses, respectively, through historical data series and through forecasts/quasi-real time data acquisition [5], which can support operators both in long term planning and in operation. Big data techniques are gaining importance in particular in the context of weather event alarms [6,7]: In [8] the authors propose a data driven tool for disaster management to support operators' decisions in emergency conditions. Reference [9] describes a risk warning system also exploiting big data. In [10] a fuzzy logic system for decision making is proposed to help utility operators optimize real time operation and maintenance programs, with the final aim of mitigating weather impacts on distribution networks. This support from data analytics is even more precious if one considers the different threats to which power systems have been increasingly exposed in the last decades due to climate changes. In particular, wet snow events are common in many countries, e.g., Italy, and studies have focused on them for a lot of years [11]. The analysis of the benefits to resilience coming from different measures require a general framework rooted in an agreed resilience definition. In particular the choice of the best combination of grid hardening and operational measures to enhance resilience requires methods and tools able to quantify resilience by suitable metrics, to model the effect of the different measures to face threats of various intensities, and to assess the resilience improvement associated to the deployment of the measures. A first step towards a techno-economic assessment of the best portfolio of hardening and operational measures consists in a methodology able to quantify the technical benefits of these measures in terms of reduction of load disruption risk.