## 1 Introduction
According to the literature review, there are many optimization strategies that can be applied to the issue of bidding approach and optimize supplier benefit. Heuristic optimization techniques such as the particle swarm optimization algorithm (PSO) [5], genetic algorithm (GA) [6], bat inspired technique (BIT) [7], krill herd algorithm (KHA) [8], gravitational search algorithm [9] have main limitations in their sensitivity to the choice of parameters, such as the inertia weight and learning factors in PSO, crossover and mutation probabilities in GA, the requirement of parameter tuning, a poor control strategy the and lack of exploration capability in BIT, poor exploitation capability in KHM, and population initialization in GSA. Tuning of parameters determines the effectiveness of heuristic tailored strategies. The methods with fewer parameters tuned have the highest precision. Thereby, [PERSON] et al. [17] propose a new heuristic solution a called gravitational search algorithm (GSA) that focuses on the gravitational law and mass exchanges. GSA is based on [PERSON]'s theorem, which states that any particle in the space attracts every other particle with a force equal to the product of their masses and inversely proportional to the square of their separation. Moreover, GSA is straightforward, scalable, and fast to adopt as compared to other evolutionary methods, and it can also find global optimal solutions. However, GSA has the downside of premature convergence, which will greatly reduce the algorithm's global search ability, and hence GSA's performance needs to be improved. In [18], [PERSON] proposed the principle of opposition-based learning (OBL). The key theory behind OBL is to consider estimation and its inverse estimate (i.e., guess and opposite guess) at the same time in order to improve the calculation for the current candidate explanation. The population initialization in GSA is random, and the operation approach with various parameters is also random. Convergence can be reached easily when the guess made at random is not that far off the desired outcome. On the other hand, the random guess could be far from the best outcome. This bad scenario will result in more time spent looking for the best solution, or, in the worst-case scenario, a non-optimal solution. Without possessing some prior knowledge of the case, it is difficult to make the right initial estimate. Therefore, the method should logically be searching at all potential alternatives or, to be more precise, it should also search in the opposite direction. Therefore, the OBL has been used to speed up the convergence rate of the GSA in this article. As a result, our suggested method is known as the opposition-based gravitational search algorithm (OGSA). Is it possible to integrate the opposite number concept during population initialization as well as the generation of new populations during the GSA evolutionary phase described in [17]? Is it possible to draw a rational conclusion based on the proposed algorithm's performance on a set of power system optimization problems, such as strategic bidding? In light of the above, the aim of this study is to test the proposed algorithm's optimizing efficiency on some real-world power system optimization problems, such as the solution of strategic bidding for optimizing generating utilities' benefit in a dynamic electricity sector. To optimize benefit value for generating utilities, a considered bidding technique is devised that includes wind power. In order to quantify the total benefit, a composite market clearing price for traditional and wind power supplies is taken into account. An actual modeling of wind uncertainty was developed using Weibull pdf in order to reduce forecasted error while retaining the benefit. Furthermore, the wind power probabilities were normalized to represent real-world scenarios. In addition, the Weibull pdf provided wind power scenarios are reduced using the forward-reduction algorithm.