# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
## 6 Simulation Results
### Scenario 1  
In Scenario 1, it is assumed that all the units generate electricity and the energy exchange with the utility is based on the demand and dynamic pricing of the utility. Every unit including the grid is assumed to operate within their power limits. The scheduling of power generation for all the generators is shown in Table 3. The optimal parameter setting of BAS algorithm is shown in Table 4. The total cost of generation is compared with various algorithms as tabulated in Table 5. The BAS algorithm shows the best result for all the scenarios. The mean simulation time for this is 5.25 s. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{2}{c}{**Parameter Setting**} & \multicolumn{2}{c}{**Best Solution**} & \multirow{2}{*}{**No. of Iteration**} \\ \cline{2-4} \cline{5-5}  & **d** & \$ & & **(Eurocent)** & \\ \hline \multirow{4}{*}{**I**} & 1.3 & 0.2 & 200.12 & 9 \\  & 1.5 & 0.2 & 200.24 & 8 \\  & 1.7 & 0.2 & 200.45 & 9 \\ \hline \multirow{4}{*}{**II**} & 2 & 0.2 & 200.74 & 9 \\  & 2 & 0.3 & 200.84 & 8 \\  & 2 & 0.4 & 201.01 & 9 \\  & **2** & **0.5** & **201.63** & **8** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Optimal parameter setting of the BAS algorithm. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Time in h**} & \multicolumn{4}{c}{**DG Sources(kW)**} & \multicolumn{2}{c}{**Battery**} & \multirow{2}{*}{**Utility**} \\ \cline{2-4} \cline{5-5}  & **MT** & **FC** & **PV** & **WT** & **(kW)** & **(kW)** \\ \hline
1 & 6 & 30 & 0 & 1.5 & \(-15.5\) & 30 \\
2 & 6 & 30 & 0 & 1.5 & \(-17.5\) & 30 \\
3 & 6 & 30 & 0 & 1.5 & \(-16.5\) & 30 \\
4 & 6 & 30 & 0 & 1.5  
The VPP experiences a low demand and the utility selling price is low for the first 6 h. Therefore, the battery is charged during these hours. From the seventh hour, the demand started to increase, which is also reflected in the selling price. Therefore, the storage battery is discharged during these hours to reduce the utility power generation and to satisfy the load. During the first hours of the day (hours 1-7) and the last 7 h, there is no PV generation as the demand is low, as is the the market price. Therefore, the VPP has taken the right decision by decreasing the PV generation during these hours. The scheduling of generation for Scenario 1 is shown in Figure 9.  
The control strategy for the BAS algorithm can be analyzed using generation of optimal parameters for the BAS algorithm, so it is necessary to carry out an empirical analysis to fine-tune the BAS algorithm. In case 1, the initialization parameter is varied while keeping the step size as constant. In case II, the step size is varied while the initialization parameter is kept constant. It is noted that the best solutions are obtained when initialization is 2 and the step size is 0.5. The control strategy for the BAS algorithm is solved using an empirical study. The convergence characteristics of the BAS algorithm are compared with other existing algorithms for the considered test system in the case of Scenario 1, as shown in Figure 10. It is clear from the figure that BAS only takes 10 iterations to converge with the best solution. Moreover, the BAS algorithm indicates a stable first convergence with the global searching ability to find the optimal cost. The computational efficiency regarding the simulation time is not compared as the simulation time for other existing algorithms is not available. The