## 4 Results and Discussion  
### Study System  
In this section, the proposed optimal model is implemented on a CVPP test system including RESs, battery storage systems, and residential consumers. Assuming that the CVPP test system is located in the Vietnamese power grid, this system uses the data collected from Vietnam's electricity market, including wholesale tariff and typical wind, solar, and load profiles. In fact, the VPP model and a BC market have not been applied in Vietnam. However, with the rapid increase in RES sources, it is expected that this model will soon become relevant.  
Until now, the Vietnamese government has had many policies to encourage renewable energy sources, especially solar energy. Accordingly, RESs are allowed to participate in the DA market at a preferential price of 83.8 USD per MWh [41]. In contrast, the electricity buying price in the wholesale market follows a three-tier tariff with peak-load, regular, and off-peak prices at 129.6 USD, 70.44 USD, and 45.65 USD per MWh, respectively (Figure 3) [42]. This tariff will be used in the proposed scheduling model. When a VPP enters the BC market, we assume that they can earn $8.38 for 1 MW of reserve capacity. Meanwhile, the price of each MWh of the activated reserve is 30% higher than the electricity selling price, i.e., 108.9 USD/MWh. The VPP must be able to generate a reserve power of at least 1 MW and maintain it for at least 3 h continuously.  
In the VPP test system, the RESs are assumed to be wind power plants with the forecasted power curves of a wind generator (in p.u.) illustrated in Figure 4. Meanwhile, the battery storage systems can be treated as a large-scale storage system with technical data shown in Table 2. The residential consumers have aggregated daily consumption equal to 510 MWh while their peak-load consumption can reach approximately 29 MW. Figure 5 describes the typical daily load considered as the long-term predictive data of consumers in this study.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Maximum/Minimum Energy Storage Limit (MWh)** & 20 \\ \hline
**Discharging/Charging Power (MW)** & 10 \\ \hline
**Charging Efficiency** & 90\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Battery Storage System’s Technical Data.  
Figure 4: The forecasted wind power curve in p.u.  
Figure 3: The selling/buying price in the DA market.  
To account for the uncertainty in demand and RES power output, the forecast errors are assumed as a normal distribution with a mean of zero and a standard deviation of 0.033 and 0.05, respectively, for demand and RESs power output. This means that the maximum errors described by the error bars in Figures 4 and 5 are approximately 10% for demand and 15% for RESs power output. The risk level of probability constraints is assumed to be 5%.  
The scheduling model is performed with the reserve activation probability in each hour generated from the uniform distribution function \(U(0,0.05)\), so that the highest probability of reserve activation in every hour is 0.05. Furthermore, the impact of different aspects such as RESs power rating and ESSs capacity is also evaluated. The optimization problems are solved using CPLEX version 12.6 and the YALMIP toolbox [43] on a 64-bit core i5 1.9 GHz personal computer with 16 GB RAM.