## 4 Results and Discussion
As a result, the proposed OGSA methodology outperforms the algorithms described above. To determine the impact of RESs on the bidding strategy model, wind energy was studied along with thermal power plants. Due to the presence of alternative energy sources, the MO modifies the current demand of the system by eliminating wind power generation. The MCP value and bidding parameters are adapted for the adjusted system demand for wind. The cost of a wind energy source is measured by taking into account the uncertainty of the source. The coefficients \(\text{k}_{\text{o}}\) and \(\text{k}_{\text{u}}\) were taken from reference [16] to calculate the cost of overestimation and underestimation. Table 5 shows the optimal strategic bidding outcomes using GSA [16], GA [22], PSO [23], and OGSA on the standard test scheme with wind power. OGSA outperforms the other algorithms in this case as well. Table 5 shows that MCP in the presence of a wind source is USD 12.80/MW. This MCP value is lower than the previous value of USD 14.15/MW obtained from Table 4, which was calculated using OGSA without a wind source. The MCP, net benefit for TPS, and WPS using OGSA are USD 12.80/MW, USD 4256.5, and USD 250.3035, respectively. Table 5 shows that when, wind energy is taken into account, the amount of electricity that needs to be dispatched from thermal generation sources decreased to 448.05 MW. Taking into account the uncertainty, the cost of a wind supply is estimated to be USD 414.6565. Since the cost of overestimation USD 42.2995 is smaller than the cost of underestimation USD 372.3570, utilities that have wind energy sources would be allowed to compete for more electricity. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Concentrated Scenario** & **Wind Power (MW)** & **Probability of Incidence** \\ \hline
1 & 10.56 & 0.142 \\
2 & 28.65 & 0.283 \\
3 & 54.86 & 0.325 \\
4 & 81.43 & 0.144 \\
5 & 103.5 & 0.049 \\
6 & 121.7 & 0.029 \\
7 & 136.7 & 0.013 \\
8 & 155.8 & 0.01 \\
9 & 178.3 & 0.002 \\
10 & 191.2 & 0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Power outputs of wind and their cooresponding probabilities for a smaller number of 10 scenarios. After that, the usefulness of the suggested technique was put to the test on a standard IEEE 57-bus system. The data for the IEEE-57 bus system used were taken from reference [16]. The bidding strategy was simulated with and without a wind source. In this case, the MCP is determined first in a dynamic power market using an optimal bidding parameter. The net benefit of generating electricity, as well as the dispatching of overall capacity, are then calculated and input into Table 6. Table 6 shows that the demand finished at an MCP value of USD 12.97/MW, generating utilities benefit is USD 14,077.77 when using OGSA without wind, which are the largest as compared to GSA [16], GA [22], and PSO [23]. When a wind supplier is included in the IEEE 57-bus system to model a bidding plan, SO modifies the current demand of the system by subtracting wind power output from real demand. Changed demand is used to calculate bidding coefficients and MCP value. The cost of a wind energy source is determined by estimating overestimation and underestimation costs. Table 7 shows that, when the wind power is mixed with thermal power, the MCP value drops to USD 12.61 per MW, relative to USD 12.97 per MW without wind power and the corresponding net benefits for TPS and WPS using OGSA are USD 13,417.84, and USD 246.5881, respectively. Moreover, the estimated overestimation expense is USD 48.78, which is less than the underestimation cost of USD 356.54 for wind. It is evident from Table 7 that OGSA provides better results for the IEEE 57-bus system compared to GSA [16], GA [22], and PSO [23].