One common method to solve this problem is using stochastic programming to manage the uncertainties. Stochastic optimization has been employed in various studies to determine optimal bidding strategies under uncertain conditions. For example, [PERSON]. [14] proposed a novel multi-objective optimization framework for uncertain integrated energy systems planning that includes demand response, where the uncertainty of regulation signals was addressed using a stochastic algorithm. Following this approach, we tackle the uncertainties associated with wind power and DR customers by utilizing a scenario-based stochastic optimization method. Though numerous studies focus on the coordinated bidding strategy of wind power producers and DR aggregators, the following aspects deserve further investigation. (1) The recent literature does not comprehensively consider the complementary characteristics of wind energy and DR resources in the application of frequency regulation. (2) There is a scarcity of studies examining the incremental benefits derived from the collaboration between wind and DR resources, which leverages their complementary nature. Furthermore, limited research has been conducted on revenue allocation among different market participants, with previous studies often neglecting payoff distributions. In view of the above, to better investigate the complementary characteristics of wind and DR resources, and enhance their performance in the market, an optimal coordinated bidding strategy and a fair allocation scheme are essential. In this paper, a coordinated framework for WPP and DR aggregators through the cooperative game theory method is developed. Within this framework, WPPs and DR aggregators form a coalition to participate in the combined day-ahead and real-time energy and frequency regulation markets, leveraging their complementary characteristics. Overall, the main contributions of the paper are shown below:  
1. Considering the complementary characteristics of wind power and DR resources, an appropriate coordinated bidding strategy using cooperative game theory is developed. Compared to existing studies on coordinated trading strategy for wind power and other resources, as proposed in this paper, wind power and DR resources can strategically perform regulation provision by taking advantage of their complementary characteristics. 2. Based on cooperative game theory, WPPs and DRAs form a coalition and strategically bid in both energy and regulation markets to achieve maximum benefits. Then, two different profit allocation methods are deployed to reasonably allocate the additional profits of the coalition to each player. 3. Uncertainties associated with wind outputs and DR are considered and represented by multiple scenarios. The conditional value-at-risk (CVaR) is employed to quantify the risks attributed to collaborative bidding decisions. The rest of the paper is organized as follows. Section 2 presents a detailed description of the problem, including DR and uncertainty modeling and analysis of the complementary characteristics of wind and DR resources. The optimal bidding strategies are formulated in Section 3. Case studies and relevant results are performed in Section 4 and in Section 5, some conclusions are drawn.