## 1 Introduction  
Nowadays, the electric industries are reforming and implementing a deregulated business policy, which generates competition among power trading firms. The electric companies are more concerned with profit maximization in this dynamic electricity market. The electric utility companies have engaged in competitive bidding in order to achieve a strong profit margin. The benefit of electricity producing companies is fully reliant on the price of marginal cost (MCP), playing an important role in the dynamic energy market. The value of MCP is determined by the system operator (SO) using curves of supply and demand. To calculate the value of the next unit of power produced, MCP uses the process used in market clearing strategy [1]. A feasible approach to the optimum bidding problem can be achieved by using this mechanism. Renewable electricity sources, such as wind energy, have been an inseparable component of the current power grid. In contrast to other non-renewable energy sources, wind energy sources are an option prior to power generation. Due to the unpredictability of this renewable energy supply, several restrictions exist in the market in support of power dealings [2]. The key objective of this situation is to create a bidding approach representation for optimizing the benefit of producing utilities in a deregulated setting, using a single-sided bidding setup. The effect of the wind source on the bidding model is being investigated using uncertainty modelling. First, in a restructured power market setting, for traditional power traders, the optimum bidding problem was solved with the help of a complex programming approach [3]. Furthermore, for the purpose of resolving power sector bidding strategy considering variables that influence the bidding strategy challenge, such as generation costs, market restrictions, and competitor bidding behavior, many researchers have used numerous mathematical approaches, e.g., Monte Carlo simulation [4], particle swarm optimization algorithms (PSO) [5], genetic algorithms (GA) [6], bat inspired techniques [7], krill herd algorithms (KHA) [8], gravitational search algorithms [9], etc. In the above-mentioned works, the action of competitors is first explained using the normal probability distribution function (PDF), followed by the solution of the benefit maximization problem. Moreover, the issue was only addressed for traditional power sources. In open bidding procedures, traditional renewable generation (RES) companies participate in the bidding procedures. In the deregulated electric industry, it ensures market fairness and improved RES use [10]. Of all forms of renewable energy sources, the most popular source of renewable energy is wind because of its low cost, and the use of wind energy is rapidly increasing these days [11]. The development of strategic bidding when considering the involvement of wind power suppliers has been the subject of extensive research. The biggest drawback of wind energy generation is the unpredictability and volatility of wind speed, which always results in deviations from the actual power output in real time [12]. The existence of volatility makes bidding more difficult for market participants in the restructured energy marketplace [13]. There has been a concerted effort in previous decades to address issues that have arisen in the field of wind energy sources in a global environment. The bidding issue is solved, with the aim of minimizing running costs by only considering wind sources [10; 11; 12; 13], whereas the problem has been addressed with REs for dispatching and scheduling of generating services by taking into account the overestimation and underestimation costs that are correlated with RE production surpluses and shortfalls [14; 15]. Uncertain wind power generation raises the cost of imbalance and the fines that come with wind turbines. As a result, income for wind energy providers is reduced. Therefore, accurate wind power modelling is needed to reduce the uncertainty and maximize benefit [16].