## 3 Illustration of the Methodology
### Obtaining the Integrated Interruption Cost Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Actions**} & **No** & **Provide** & **Provide Full Lighting** & **Lighting, TVs and** & **Full** \\  & **Action** & **Minimal Lighting** & **and LED TVs** & **Small Appliances (Fan)** & **Household Load** \\ \hline Cost (CAD/h.) & 0.0 & 1.0 & 6.0 & 12.0 & 25.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Industrial DNC, CAD/KW. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Key**} & **Residential** & **Farm** & **Commercial** & **Industrial** \\ \hline Sector peak (kW) & 2.0461 & 2.7000 & 8.1000 & 99,780.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sector annual peak load, KW. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Key**} & **Residential** & **Farm** & **Commercial** & **Industrial** \\ \hline Sector peak (kW) & 2.0461 & 2.7000 & 8.1000 & 99,780.0000 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Sector annual peak load, KW. listed in the first column of Table 7 are estimated by the weighted average of the preparatory actions cost per hour by the undesirability rating of the customer responses. The last column of Table 7 shows the interruption costs estimated using the approximate method. Equation (17) shows the calculation of the interruption cost of a 4 h outage occurring monthly during the winter peak period. It can be seen that this value is close to the 4 h interruption cost for the residential sector shown in Table 2 and is therefore used as the interruption data for the winter peak period of the proposed model. \[\text{Cost }(\$/\text{int})=\frac{4\text{h}}{1\text{h}}\times\frac{(11\times 0+22 \times 1+(39+41)\times 6+53\times 12+57\times 25)}{11+22+(39+41)+53+57}=45.97 \tag{17}\]  
The Table 7 data shows that the monthly occurring outage cost is 56% of the weekly outage cost. This ratio can be used to estimate the 4 h outage cost data shown in Table 8 for the remaining peak periods of the proposed residential SPD model. Similarly, Table 7 shows that the winter off-peak cost is 80% of the winter peak data. The outage during the off-peak period is therefore estimated to be 80% of the peak period. Since all the off-periods are merged together in the SPD model, the average sum of the off-peak costs as calculated in (18) is used as the 4 h outage cost for the off-period in the SPD model. \[\text{Off-peak }(\$/\text{int})=\frac{45.97+26.71+24.25+27.40}{4}\times 80 \%=24.87 \tag{18}\]  
The next step is to calculate the interruption cost for outage durations other than 4 h. Table 2 shows the interruption costs for outage durations of 1 min, 20 min, 1 h, 8 h, and 24 h are 0.0%, 1.2%, 8.9%, 203.8%, and 773.5%, respectively, of a 4 h duration for the residential sector. The calculated outage costs as expressed by (11) during the periods of the residential SPD model are shown in Table 9.  
A similar approach was used to obtain the interruption costs shown in Table 10 for the farm and commercial sectors. Since the only data available from the survey for the farm and commercial sectors are the interruption cost data shown in Table 2, these cost data are taken as the base values to approximate the costs in Table 10 using a linear approximation from the residential cost data shown in Table 8. \begin{table}
\begin{tabular}{c c} \hline
**Period** & **(2023, CAD/int)** \\ \hline Winter peak & 45.97 \\ \hline Spring peak & 26.71 \\ \hline Summer peak & 24.25 \\ \hline Fall peak & 27.40 \\ \hline Off-peak & 24.87 \\ \hline \end{tabular}
\end{table} 
Table 8: Interruption cost of 4 h outage during the peak periods of the residential sector.