3.2.1. Effective Damage Cost (EDC)

To monetize the potential damages caused to the electrical assets, a damage curve adapted from (FEMA-HAZUS) has been applied (Figure 6). The curve initially was given for a 3m depth, but it has been interpolated from the original one up to a water depth of 9m according to the maximum water depths obtained in the flooding maps. In Figure 6, the original curve is shown in blue, and the one that was used for the analysis in green.

**Figure 6.** Damage curve used for Effective Damage Cost calculation (Adapted and interpolated from FEMA-HAZUS [10]).

After introducing the WDA (obtained by Equation (2)) in the damage curve, a percentage of damage (*Dn*) in each DC analyzed was obtained. After that, the effective damage cost (EDC) was calculated according to Equation (4) by multiplying the damage ratio (*DES*), by the failure probability (*PF*) and by the price of the corresponding DC (*pSC*) that has been estimated based on the substation voltage given in Table 3. The price of a DC assumed for the cost calculation corresponds to an 11 kV substation.

$$EDC = P\_F \times D\_{ES} \times p\_S \tag{4}$$

**Table 3.** Cost of the different substations analyzed based on the voltage (pS) (Adapted from "Climate change and critical infrastructure-floods" [12]).


3.2.2. Cost Associated with Businesses Losses (BC)

First, a GIS consumer layer based on the ward's population of each city was created, by using databases of 2018 obtained from the open data portal of both cities studied [20,21].

A cloud of random points based on the ward population was then generated, therefore representing the potential consumers distributed along all the study extent. After that, a Thiessen polygons layer was generated for each electrical asset, representing the supply coverage area of each DC (Figure 7). The next step was the association of the number of consumers of each random point to the overlapping Thiessen polygon, getting a total of associated consumers for each DC. It is important to indicate that such polygons provide an averaged estimation of the population that for global risk assessment can be accepted as a reasonable result, allowing its application to larger areas.

**Figure 7.** (**a**) Consumer layer based on the different wards of the area studied; (**b**) Thiessen polygon layer representing the area of influence of each DC and colored by the total power distributed.

Equations (5) and (6) are taken from a European Commission Joint Research Center (JRC) technical report [13] analyzing climate change and critical infrastructure. These equations evaluate the losses accounted by city businesses provoked by electrical shortages. Equation (5) uses the Gross Domestic Product (GDP) of the city and multiplies it by the probability of failure of the DC (*PF*), by a ratio of the part of population affected (number of people affected by the shortage (*nP*) divided by the total population (*ntot*)), by the fraction of the year that the shortage takes place (shortage duration in days (*tWE*) divided by 365). In this way, the previous DC failure analysis allows the calculation of this formula that will estimate the cost of the shortage to the businesses.

$$\text{Business Cost} \left( \text{BC} \right) = \text{GDP} \times P\_{\text{F}} \times \frac{n\_{\text{P}}}{P\_{\text{tot}}} \times \frac{t\_{\text{WE}}}{365} \tag{5}$$

Equation (6) will give the total cost of the losses associated with local businesses by adding the values previously calculated in Equation (5).

$$\text{Total Bussiness Cost} \ (TBC) = \sum\_{k} B\mathbf{C}\_{i} \tag{6}$$
