## 4 Simulation Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Hcm**} & \multicolumn{2}{c}{**Location-1**} & \multicolumn{2}{c}{**Location-2**} & \multirow{2}{*}{**Location-3**} \\  & & **Total Generating** & & **Load (MW)** & **Power Balance (MW)** \\ \hline Location-1 & 960 & 480 & +495.6 \\ Location-2 & 250 & 430 & \(-180\) \\ Location-3 & 0 & 300 & \(-300\) \\ \hline Losses & 15.6 & 15.6 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis Result of LMP, Losses, and Customer Price. the energy price in Location-3 is determined by the energy price from Location-1. At Location-2, although it imports power from Location-1, the energy price is determined by the LMP itself because the LMP of Location-2 is higher than the LMP of Location-1. The analysis results for 24-h transactions are listed in Table 7.  
In this simulation, Location-1 always has a surplus so that the excess power is exported to Location-2 and Location-3. This causes the LCP at Location-3 to be the same as the LCP at Location-1, whereas the LCP at Location-2 depends on the LMP itself. If the LMP at Location-2 is lower than the LMP at Location-1, then the LCP at Location-2 is the same as the LCP at Location-1, similar to hours 1-9 and hours 19-24. Beyond this hour, the LCP at Location 2 soared more than 4 times and reached a peak at $52.760/MWh. This was caused by the entry of \(G_{6}\) with a power quota of 92.2 MW, and the price fell to $52.1/MWh (see Table 2). This method has been tested on a large-scale electrical system, namely 118 buses, 54 generators, based on IEEE 118 Bus data. The simulation results are compared with the calculation results from [PERSON]'s Method Power Flow, which are presented in Table 8. Table 8 shows that the results of the proposed method are very close to the results of the [PERSON] method. To supply a load of 4242 MW, the power generated by the proposed method and [PERSON]'s method is 4377.59 and 4374.86 MW, respectively. In other words, the losses generated by the proposed method and [PERSON]'s method are 135.59 and 132.86 MW, respectively. Meanwhile, the cost of generating power with the proposed method and [PERSON]'s method is $62,556.216 and $62,492.967, respectively. The results of the proposed method have a deviation of 0.0624% for power generation, 2.0548% for losses, and 0.1012% for generation costs from the results of [PERSON]'s method. The large-scale electrical system (IEEE 118 Bus, 54 generators) is divided into five locations, as shown in Table 9. The results of the calculation of energy prices for each location are listed in Table 10. The simulation of this system shows the optimal energy price in each location. The price is determined by the maximum value of all optimized generators at each location (maximum LMP) and their losses. This means that the energy \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Hour** & **Location-1 (S/MWh)** & **Location-2 (S/MWh)** & **Location-3 (S/MWh)** \\ \hline  
\hline \hline \end{tabular}
\end{table} 
Table 7: LCP Analysis Results at 3 Location. price at each location will be different, as shown in Table 10. This large-scale simulation requires a very short computation time of 1.28 s.  
To realize the energy price at each location in the competition system, for the proposed method, it is fast and guarantees convergence, whereas a new method based on iteration, especially the artificial intelligence method, requires a long computational time to be applied to a large-scale system. In addition, for artificial intelligence methods, such as particle swarm optimization (PSO), there is no guarantee that the solution will fall on the global minimum point, as the PSO is very dependent on the starting point.