A Coordinated Bidding Strategy of Wind Power Producers and DR Aggregators Using a Cooperative Game Approach  
[PERSON]  
1 College of Automation Engineering, Shanghai University of Electric Power, Shanghai 201399, China; [EMAIL_ADDRESS] (X.D. ); [EMAIL_ADDRESS] (S.Z. ); [EMAIL_ADDRESS] (H.C.)  
[PERSON]  
1 College of Automation Engineering, Shanghai University of Electric Power, Shanghai 201399, China; [EMAIL_ADDRESS] (X.D. ); [EMAIL_ADDRESS] (S.Z. ); [EMAIL_ADDRESS] (H.C.)  
[PERSON]  
1 College of Automation Engineering, Shanghai University of Electric Power, Shanghai 201399, China; [EMAIL_ADDRESS] (X.D. ); [EMAIL_ADDRESS] (S.Z. ); [EMAIL_ADDRESS] (H.C.)  
[PERSON]  
1  
Footnote 1: email: {x.x.y.z.,x. Several studies have explored the use of wind power and demand response (DR) resources for frequency regulation (FR) services [4]. Ref. [5] developed a bidding strategy for wind power and large-scale EVs in the day-ahead energy and FR market. The Federal Energy Regulatory Commission (FERC) Order 755 encouraged DR resources to submit bids and offers in ancillary services markets [6]. Additionally, China's National Grid issued a notice promoting large industrial loads, such as steel plants, to participate in the frequency regulation market [7]. Recent research has shown that DR resources can effectively track automatic generation control (AGC) signals [8]. In [9], an optimal bidding strategy for a virtual power plant (VPP) was proposed to maximize the profits of an aggregator of renewable energy, EVs, and flexible loads in energy, reserve, and regulation markets. In [10], a set of mixed-integer linear programming (MILP) models are proposed, and a bi-objective scheduling framework is introduced for the corresponding problem to obtain alternative solutions under each strategy. Ref. [11] introduces the economic theory of conceptualizing the benefits of environmental research and provides a framework for estimating these benefits. At the same time, it discusses environmental science assessments aimed at benefiting society by providing information for policy decisions. In [12], thermal systems in buildings are considered as a source of dynamic flexibility for frequency regulation and an optimization model was deployed to model the interactions among the ISO, aggregators, and the end users. In [13], an optimal model of electric vehicle (EV) load and wind utilities is formulated for joint day-ahead energy and regulation markets. The above literature focused on evaluating the economics of wind energy and DR resources considering their participation in the FR market. However, few studies have investigated the frequency response characteristics of wind and DR resources for FR provision in detail. Notably, wind power and DR resources exhibit complementary features in frequency regulation: (a) Up-regulation means increasing generation or reducing load. For wind farms, this requires reducing their scheduled output, leading to regulation costs due to spilled power. In contrast, for DR resources, this translates into lower costs, such as reduced consumption. Hence, it is more cost-effective to dispatch DR resources for upward regulation. (b) Down-regulation means reducing generation or increasing load. For DR resources, this can lead to additional costs due to increased consumption. However, WPPs can benefit by reducing their generation, making them more suitable for downward regulation. Intuitively, cooperation between wind power and DR resources can reduce regulation costs and increase profits by leveraging their complementary characteristics in frequency regulation. Additionally, the uncertainty of wind power output can lead to discrepancies between predicted and actual scheduled values, resulting in economic penalties. Similarly, the unpredictable behavior of customers introduces uncertainty in DR participation, which can further increase financial risks for market participants. Therefore, it is essential to incorporate uncertainty into the decision-making process.