## 4 Case Study and Simulation Results  
We assume that, because of the high temperature of the summer in a specific area, the increased ACL leads to a shortage of electric power. To ensure the stable and secure operation of the power system, the electronic company plans to adopt DR measures for the ACL to shift the peak load. The case study adopted a 10-generator test system. The 24 h load data and wind power data from a specific area were used to conduct a simulation analysis with CPLEX [34] for MATLAB. The parameters of the 10-unit test system can be found in [35]. Figure 3 illustrates the initial system load, and Figure 4 shows the wind power of different scenarios as well as the baseline scenario. The methods of generating wind power scenarios and scenario reduction have been described in [36]. Our paper adopted the data of wind power scenarios from [36]. In the high peak period, it is assumed that ACLs account for 30% of the total loads and 15% of the ACL can be included in the power company's DR program. Thus, 4.5% of the peak load was included in the DR program. In order to ensure the customers' comfort, we assumed that the indoor temperature was in the range [22 \({}^{\circ}\)C, 27 \({}^{\circ}\)C]. Figure 5 illustrates the 24 h temperature in summer. Table 1 shows the parameters of the equivalent thermal parameter model. On the basis of the methods in Section 2, the greatest ACL reduction can be calculated. As shown by Equation (11), the largest ACL reduction will change when the ambient temperature changes. In order to decrease the peak-load demand in Figure 3, we implemented DR for the ACL's user during peak-load periods. It is assumed that the peak-load periods are during the hours 9-14 h and 19-24 h in the day [35]. To investigate the impact of ACLs on UC, a comparison of two modes was simulated. In operation Mode 1, the UT for the wind-integrated power system was optimized without considering the ACL in the DR program. In Mode 2, the UC for the wind-integrated power system was optimized with the ACL. The simulation results of UC in the two modes are shown in Table 3. It can be seen that in Mode 2, the number of units committed was less than that in Mode 1 during the peak periods. In addition, the status of unit 4 at 23 h and unit 6 during the periods 12-14 h and 19-20 h changed from starting to stopping, because of the reduction of the ACL during peak-load periods. Therefore, the model proposed by this study can effectively control the usage of ACL customers during peak-load periods in the afternoon and night, reducing the frequency of unit start-stops and peak loads. Table 4 provides the cost analysis of the two modes. Both the power generation and start-up costs in Mode 2 were decreased compared to Mode 1, as the participation of ACL users in the DR project decreased the electricity usage during peak-load periods. Hence, the ACL can be regarded as the \"virtual power plant\" to replace a part of generators. In conclusion, the DR program effectively relieves the excessive peak-load requirement and saves costs for the power company. Figure 6 illustrates load reduction in Mode 2. The total reduction of the load was 583 MW, accounting for 4% of the peak loads. According to Equation (15), the compensation cost of the ACL is primarily influenced by \(K_{1}\). We also changed the values of \(K_{1}\) under two types of compensation  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cost/(5)** & **Mode 1** & **Mode 2** \\ \hline Power generation cost & 419,422.71 & 401,847.44 \\ Cost of start-up & 2920 & 2638 \\ Cost of  
costs of the ACL and compared the impacts of different compensation costs on the load reduction. Figure 7 shows the load reduction results when the values of \(K_{1}\) in the first- and second-type ACL were 1 and 0.9, respectively. Compared to Figure 6, Figure 7 illustrates that the load reduction decreases when the compensation cost is very high.