## 4 Results and Discussion  
The IEEE- 30 bus [16] and IEEE-57 bus [16] have been used to model the bidding strategy in an emerging power market. The load demands for 30-bus and 57-bus networks are 500 and 1500 MW, respectively. The bidding technique is first devised on a standard evaluation bus scheme. Second, it is analyzed using an updated framework that takes into account one renewable energy source, wind power, with a capacity of 200 MW. The suggested strategy was tested in a MATLAB R-2014a environment with 4 GB of RAM and an i5 Core Processor. The OGSA is put to the test with a search agent that prefers 1000 iterations. The number of individual runs was set to one hundred. An average wind speed for 1 (12:00-13:00) hour in August 2005 in Barnstable city, USA [20], was used to estimate renewable wind supply, with a hub height of 39 m. In this proposed work, the average wind speed capacity curve for a VENSIS-100 wind turbine with 2.50 MW and 100 m hub height [21] was used to calculate wind power. The density of the air was 1.242 kg/m\({}^{2}\). Using past data of a specified wind speed, demonstration of wind speed is possible with various potential PDFs (Normal, Weibull, and Rayleigh), as seen in Figure 2, and the Weibull PDF is considered to be the most suitable since the statistics are better suited to the distribution, as seen in Table 1. The shape and scale parameters for 100 m hub height were found to be 3.49 and 8.13, respectively, and are given in Table 2, having calculated the mean and variance of the wind speed. For precision, 1000 wind speed scenarios were developed and translated to power scenarios at the appropriate hub height. Weibull probability densities for the generated power scenarios are shown in Figure 3. Following scenario generation, scenario reduction was carried out using the likelihood distance method known as the Kantorovich distance [19] to construct a wind power model that takes into account uncertainty. Initially, 1000 scenarios were developed, from which 10 reduced scenarios were derived using likelihood distance [19]. Table 3 shows the associated diminished scenario performance as well as the likelihood of its occurrence. Equation (25) is used to calculate the possible wind power from this decreased situation, which is 51.95 MW. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **[PERSON] PDF** & **[PERSON] PDF** & **Normal PDF** \\ \hline Variance & 6.4984 & 2.84011 & 2.7568 \\ Mean & 4.87676 & 5.25439 & 5.25484 \\ Log Likelihood & \(-65.3438\) & \(-59.1945\) & \(-59.2052\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variance, mean, and log likelihood values for different probability distribution functions of wind speed. Figure 3: Probability densities calculated by [PERSON] for the generated power scenarios. Figure 2: Different probability distribution functions of wind speed. For the first case, input data of the IEEE 30-bus system were taken from reference [16]. In a dynamic power market, bidding criteria are primarily used to develop bidding strategies. As a result, it is measured with the help of a joint PDF (Equation (13)) and optimized by the proposed OGSA methodology. For generating utilities, these bidding coefficients cannot be chosen individually in order to maximize profits. Therefore, each utility was given \(\pi_{m}\) of bidding, and \(\phi_{m}\) is estimated using OGSA in the range (\(\text{b}_{\text{m}}\), \(10\text{b}_{\text{m}}\)). Then, using the optimized bidding parameters, MCP is calculated. The net benefit of generating electricity and overall power dispatch for this case is estimated using the calculated MCP. Table 4 summarizes the results of various optimization techniques, such as OGA, GSA [16], GA [22], and PSO [23]. In addition to other strategies, Table 4 indicates that the overall benefit of the method has risen to USD 5317.72 for OGSA. As opposed to MCP prices of USD 12.55/MW, USD 12.89/MW, and USD 13.94/MW achieved by GA, PSO and GSA, the market is now open at MCP value USD 14.15/MW by OGSA, which is the largest value.