## 5 Discussion of Simulations and Results
### Comparative Study
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Agent**} & \multicolumn{2}{c}{**Scenario 1**} & \multicolumn{2}{c}{**Scenario 2**} & \multicolumn{2}{c}{**Scenario 3**} \\ \cline{2-5}  & **Profit** & **Social Welfare** & **Profit** & **Social Welfare** & **Profit** & **Social Welfare** \\ \hline NRGenCO1 & 484.2735 & 5172.2 & 408.0777 & 5123.6 & 465.3822 & 5102.5 \\ NRGenCO2 & 771.1077 & 895.7815 & 927.0036 & \\ NRGenCO3 & 762.2319 & 494.1197 & 520.4793 & \\ NRGenCO4 & 118.5687 & 139.4572 & 188.8447 & \\ NRGenCO5 & 87.5541 & 114.0872 & 138.0355 & \\ NRGenCO6 & 77.6412 & 104.6911 & 117.8824 & \\ DisCO1 & 156.9877 & 137.7368 & 120.1286 & \\ DisCO2 & 49.7564 & 41.2381 & 33.4356 & \\ DisCO3 & 106.1371 & 91.6362 & 78.3510 & \\ DisCO4 & 251.4634 & 230.9143 & 212.1463 & \\ DisCO5 & 58.1042 & 29.3178 & 12.9882 & \\ DisCO6 & 64.6293 & 51.0053 & 35.15755 & \\ DisCO7 & 315.8062 & 297.6379 & 280.5569 & \\ DisCO8 & 120.0745 & 107.7744 & 95.9683 & \\ DisCO9 & 161.0768 & 146.8198 & 132.0919 & \\ DisCO10 & 55.0966 & 45.0306 & 34.4523 & \\ DisCO11 & 184.2311 & 166.9180 & 152.1566 & \\ DisCO12 & 53.4331 & 44.0562 & 35.4103 & \\ DisCO13 & 29.1544 & 23.8397 & 19.0950 & \\ DisCO14 & 157.17292 & 148.4835 & 140.8920 & \\ DisCO15 & 74.9662 & 53.7214 & 40.5416 & \\ DisCO16 & 92.7551 & 85.0562 & 76.4103 & \\ DisCO17 & 15.7171 & 12.9415 & 10.0403 & \\ DisCO18 & 154.5603 & 147.0306 & 138.0683 & \\ DisCO19 & 41.0299 & 33.8381 & 24.8451 & \\ DisCO20 & 148.1155 & 132.9543 & 116.5059 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Profit of every agent and SW results of three scenarios in sample state (10, 15) MW. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Agent**} & \multicolumn{2}{c}{**Scenario 1**} & \multicolumn{2}{c}{**Scenario 2**} & \multicolumn{2}{c}{**Scenario 3**} \\ \cline{2-5}  & **Profit** & **Social Welfare** & **Profit** & **Social Welfare** & **Profit** & **Social Welfare** \\ \hline NRGenCO1 & 326.8942 & 4639.1 & 304.4191 & 4610.7 & 388.4828 & 4562.7 \\ NRGenCO2 & 743.4178 & 888.2880 & 1005.7694 & \\ NRGenCO3 & 810.7907 & 517.9187 & 523.6115 & \\ NRGenCO4 & 286.2695 & 188.4972 & 199.8730 & \\ NRGenCO5 & 137.7734 & 135.8852 & 120.2483 & \\ NRGenCO6 & 146.1923 & 147.0384 & 125.5433 & \\ DisCO1 & 195.2403 & 166.0144 & 135.6917 & \\ DisCO2 & 48.5420 & 39.9934 & 26.2108 & \\ DisCO3 & 99.2394 & 83.8855 & 78.4685 & \\ DisCO4 & 237.9733 & 216.6610 & 210.9919 & \\ DisCO5 & 18.9294 & 14.9156 & 7.1436 & \\ DisCO6 & 43.5275 & 30.8638 & 28.4429 & \\ DisCO7 & 297.9888 & 282.2162 & 278.5722 & \\ DisCO8 & 105.9517 & 96.6536 & 94.3787 & \\ DisCO9 & 142.7742 & 133.2911 & 130.8633 & \\ DisCO10 & 43.456 & 33.1210 & 26.7329 & \\ DisCO11 & 172.4945 & 150.3188 & 140.9950 & \\ DisCO12 & 43.7976 & 35.0465 & 30.7810 & \\ DisCO13 & 24.4293 & 16.6998 & 18.1886 & \\ DisCO14 & 150.0430 & 140.0926 & 139.3757 & \\ DisCO15 & 91.3468 & 38.3446 & 37.5857 & \\final profit in Scenario 1 is 484.2735 ($/h), which is more than that in Scenario 2 (408.0777 ($/h)). In Table 7, NRGenCO1's final profit in Scenario 1 is 326.8942 ($/h), which is more than that in Scenario 2 (304.4191 ($/h)). Moreover, although we cannot compare NRGenCO1's final profits in Scenarios 1 and 2 under every state point due to the continuous state space, the phenomenon that NRGenCO1's final profit in Scenario 1 is more than its final profit in Scenario 2 can actually be found under every state point of the \(21\times 31\) sample states in \(\mathbf{Sap}\). As mentioned in Section 5.1, there is only one difference between Scenarios 1 and 2, which is that NRGenCO1 in Scenario 1 is a GDCAC-based agent that makes its bidding decisions based on our proposed GDCAC approach, while NRGenCO1 in Scenario 2 is a fuzzy Q-learning-based agent that makes its bidding decisions based on the fuzzy Q-learning approach. Therefore, it can, to some extent, be verified that a specific participant can get more profit with our proposed GDCAC approach in market competition than with the fuzzy Q-learning one proposed in [2]. 2.