## 5 Conclusions  
This research presents a novel OGSA to model the best bidding approach in a dynamic energy market, taking into account the renewable energy sources and market constraints. The exploration and exploitation phases of the above optimization algorithm are modified by properly adapting them. The proposed OGSA methodology is put to the test using certain common benchmark functions in order to equate its results to that of other techniques, being the most superior one for the current business strategy shifts scenario. Furthermore, the uncertainties of the wind source are modelled using the Weibull distribution function. The bidding plan is planned with and without wind power. Normal PDF is used to find the benefit maximization of each utility when taking into account the information of competitors. The effect of wind energy on bidding strategies lowers thermal power generation and lowers the market clearing price. For the IEEE 30-bus and IEEE 57-bus systems, optimal bidding strategies have been developed. When compared to GA, PSO, and GSA techniques, the proposed OGSA technique produces the best results in terms of accuracy and viability for optimizing the benefit of the generation utilities. While the algorithms have the potential to incorporate certain regulatory limitations, the theoretical implementation has been based primarily on a price-driven economic competition model. The theory and algorithm demonstrated how a provider could decide the rates at which a given volume of generation could be offered to the grid system, taking into account competitor prediction behaviour, wind power volatility, and planned system specifications. Allowances for variability in these variables were successfully included, which is a key aspect of the model. Furthermore, the current work only considers symmetrical information, allowing the \(i\)th supplier's conclusions about the jth, and vice versa, to be symmetrical. For further studies we will extend this study for unsymmetrical information, because this model is not appropriate for unsymmetrical details, which helps some suppliers to make better estimates than others.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Takings**} & \multicolumn{4}{c}{**IEEE 30-Bus**} & \multicolumn{4}{c}{**IEEE 57-Bus**} \\ \cline{2-10}  & **GA [22]** & **PSO [23]** & \
\begin{tabular}{c} **GSA** \\ [16] \\ \end{tabular} & **OGSA** & **GA [22]** & **PSO [23]** &
\begin{tabular}{c} **GSA** \\ [16] \\ \end{tabular} & **OGSA** \\ \hline Best & 4490.02 & 4672.93 & 5212.59 & 5317.719 & 13,244.65 & 13,457.81 & 14,065.79 & 14,077.77 \\ Worst & 3941.41 & 4253.56 & 4798.86 & 4944.638 & 11,448.63 & 12,009 & 12,982.51 & 13,212.03 \\ Mean & 4187.19 & 4395.08 & 4944.70 & 5046.50 & 11,927.7 & 12,509.51 & 13,446.45 & 13,590.17 \\ SD & 155.87 & 124.91 & 109.75 & 94.66 & 415.5907 & 386.71 & 350.59 & 262.77 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparative analysis of the results of various techniques for robustness of the OGSA method for IEEE standard 30-bus and 57-bus.  
**Author Contributions:** All authors have contributed equally in technical and non-technical work. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The work reported herewith has been financially supported by the Direccion General de Universidades, Investigacion e Innovacion of Castilla-La Mancha, under Research Grant ProSeaWind project (Ref.: SBPLY/19/180501/000102).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.