# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 4 Resilience Quantification Objectives
Priority is first given to service restoration where all alternative (even temporary) ways to provide services are explored and deployed allowing to regain an intermediate level of performance. Complete recovery will take more time and effort as it involves mostly infrastructure catering which turns out to be very challenging. * Learning phase (phase IV): This phase is less considered than the two previous phases in quantitative resilience frameworks, generally with the argument that resilience is best examined in face of exogenous threats [120]. The post-recovery phase should still be looked at closely in order to draw conclusions about damages experienced by the network and how various implemented policies helped to alleviate consequences. Data collection through field surveys and supervisory management tools enable improvement in system performance and enhancement in preparation for upcoming extreme events backing the vision for a sustainable network. Figure 3: Performance curve for resilience quantification. Many works [13; 27; 45; 63] explore each of these phases with slightly different denominations. Here a generalizing description is adopted where the four above-mentioned phases are considered, with mitigation and recovery divided each into two sub-phases in order to better explain all involved mechanisms. Resilience quantitative frameworks can be assessed based on phases they handle [64]. The more phases taken into consideration, the better the insight into system operation during extreme events. Furthermore, the layout can be used to seek answers for the following questions: When is resilience evaluation conducted and for which reason? Figure 4 distinguishes time instants at which resilience quantification can be conducted, and objectives of this evaluation. The former here orients/guides/steers the latter, because for example, an operator who aims to plan investments for his network will most likely opt for pre-event evaluation, while another who only wants to see the impact induced by a contingency in his network may adopt post-recovery damage evaluation. Knowing \"why\" resilience is to be evaluated serves as a guideline to choose \"when\" it should be done. Without loss of generality, resilience evaluation can be induced from the performance curve in Figure 3; so it is important to know when system operators can get such a representation. Three options are available:  
* Proactive evaluation: The procedure in this case is to drive pre-event studies with the goal of obtaining resilience indicators before contingency happens. The outbuilding is in prediction data, recommendations of experts, supervision alerts, and historical records. However, for HILP anomalies, little information is available, then designing preventive measures appeals for simulation tools, emulation, and analytical models which help to make projections for the impact that will be borne by the network in face of uncertain events. Once metrics are computed, they can be used to make informed decisions about resilience strategies to implement in order to minimize the impact and speed up recovery. In other words, the output of this phase is planning schemes which enhance robustness, survivability, restoration, and recovery of the system that can be summarized in the concept of resilience. The prominent advantage of a proactive evaluation is the ability to look-forward that allows foreseeing what is coming. On the other hand, the large number of possible contingency scenarios and little relevant data cause low-confidence results. * Reactive evaluation: Quantification is carried out as the event happens, meaning that resilience metrics are computed on-the-fly, and policies adopted to cope with severe hazards are taken from the inherent reaction capacity of the system without support from pre-event recommendations. Metrics are calculated as the event goes for the two broad phases of robustness and recovery. In such real-time setup, information that can be gathered is realistic and narrows down failure modes space.