## 5 Case Study
### Wind and Smart House Penetration Level  
In the basic simulation, it was supposed that the wind penetration percentage is 20% and the DR percentage is 15%. However, this may not be considerate in practice. Therefore, it is imperative to evaluate the impact of the wind penetration and smart houses enrollment level on the wind energy curtailment and load shedding. There are several assumptions for the wind percentage and DR percentage, which are shown as follows:  
1. The wind penetration percentage equals the installed capacity of wind generation divided by the maximum total load. 2. The maximum power of a house in one day is 12 kW. The total number of houses is maximum total load divided by 12 kW. 3. All the smart houses contain three kinds of smart appliances, whose working parameters are indicated in Table 4.
4. The DR percentage is a number of smart houses expressed as a fraction of all houses. Quite a few simulations have been performed by changing the wind and smart house penetration percentages. Based on the mentioned assumptions, the simulation data are shown as follows: If the maximum load is 2658 MW, then the installed wind capacities are approximately 265 MW, 530 MW, and 795 MW when the wind penetration percentages are 10%, 20%, and 30%, respectively, and the total number of smart houses in the studied system are roughly 11,050, 22,100, 33,150, and 44,200 when the DR percentages are 5%, 10%, 15%, and 20%. The wind generation curtailment and load shedding are listed in Table 5 with different wind and DR percentages. Here, the comfort ranges of smart appliances are fixed. The wind energy curtailment decreases from 645.71 MW to 158.4 MW when the wind percentage is 30% and the DR percentage is from 0% to 20%. From the relation between DR percentage and wind energy curtailment with 10% and 20% wind percentage, it is easily derived that the wind curtailment could reduce to 0 with the DR percentage increasing as the wind percentage is 30%. There is a similar variation tendency for load shedding as the wind and DR percentage change. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Cooling set point & 22 & \({}^{\circ}\)C \\ AC thermostat dead band & 1 & \({}^{\circ}\)C \\ AC power consumption & 3 & kW \\ WH temperature set point & 47 & \({}^{\circ}\)C \\ WH thermostat dead band & 6 & \({}^{\circ}\)C \\ WH power consumption & 4 & kW \\ EV power consumption & 3.3 & kW \\ EV efficiency of driving & 3.846 & mile/kWh \\ EV charging efficiency & 0.9 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of smart appliances. EV: electric vehicle. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Transmission Constraint** & **Generator Power (MW)** & **Load Fluctuation (MW)** & **DR Power (MW)** \\ \hline with & 2390.58 & 0.92 & \(-\)4.06 \\ without & 2405.53 & 11.81 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results without/with transmission constraint. This result will serve as a strong motivation to provide an incentive to distribution companies to utilize more DR. Figure 12 shows the impact of DR and wind percentage on wind curtailment and load shedding using the interpolation method based on the information in Table 5.  
Figure 12 shows the correlation among wind percentage, DR percentage, and wind power curtailment/load shedding. The wind curtailment or load shedding reduce as the number of enrolled smart houses increases and wind power decreases.