# Resilience Quantification of Smart Distribution Networks--A Bird's Eye View Perspective
## 5 Literature Review
### Power Distribution Network
#### 5.2.2 Extreme Event and Time of Evaluation  
A closer inspection of Table 1 shows that in some cases the restoration model is not specified, and the explanation is given in the electric service portion in Table 2. These works do not target recovery and restoration capabilities of the distribution network, as they proactively plan for survivability [123; 132], react to an event uniquely by resilience assessment [139] and a damage minimization response [138], or even drive a post-recovery study like in [127]. This illustrates, as discussed in Section 4, how the objective of resilience quantification instructs the choice of system model. It goes without saying that planning and response cells in Table 2 include resilience assessment as a first step and enrich it by further use of the obtained metrics.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline  & **Resilience** & **Assessment** & x & x & x & x & & & & & & & & & & & &  
By steering interest toward when resilience metrics are obtained, the concentration of resilience quantification in the pre-event phase can be pointed out, corroborating the preventive nature of such studies and their contribution to planning for unseen events. However, real-time evaluation gained some interest [133; 136; 137; 138; 139] and offers valuable information used on-the-fly to monitor and enhance the distribution network resilience. Next, after recovery from a HILP hazard, works in [127] and [130] survey the network for lessons, with [127] offering more learning opportunities as empirical advanced experiments are done for moderate and heavy damage scenarios.  
Natural hazards catch most of the attention in present PDN resilience research due to various recent catastrophic events which raised awareness among the government agencies, regulators, and network operators about the damage that a distribution system may incur. Generally, a resilience study handles a single event, which makes the setting dependent on considered specific characteristics. Table 1 shows that some resilience frameworks are designed for a wider scope so as to tackle a set of these natural events [130; 131; 132; 136]. This renders anomaly modeling challenging, albeit feasible through a knapsack problem [131] or extended N-k network interdiction model [132]. Even so, the model should be readjusted whenever applied to a specific contingency. To handle multiple events simultaneously, [136] derives a code-based metric by computing network resilience several times for all possible natural hazards. Even though the approach is based on an empirical formula and more work should be done to justify the choice, it is an easy-to-understand measure and introduces an interesting concept of the \"service potential\" of the network.  
With the exception of [133], cyber or cyber-physical (CP) attacks are put aside in this portion of the literature despite increasing damage induced even in physical electrical infrastructure, but this apparent neglect remains understandable due to the focus of this section on electric service.