Competitive Incentive Mechanism for Multi-Agents in Demand Response via a Hierarchical Game Considering Joint Uncertainties

The emergence of load aggregators (LAs) has significantly elevated the trading position of residential users in demand response (DR). Effective incentive designs can greatly enhance the responsiveness of residential users. However, a key but often overlooked issue in practical implementation is strengthening the connection between pricing and actual responses to ensure the executability of DR scheduling results. To address this issue, this paper presents a competitive incentive mechanism for multi-agents in DR through a hierarchical game that considers joint uncertainties, and examines the rationality and interactive behaviors of different agents to ensure the efficient implementation of scheduling. The agents are the independent system operator (ISO), LAs, and residential users. First, the rationality levels of the various agents are analyzed, and corresponding utility functions are developed to target each agent specifically. Then, the interactive relationships among the various agents are examined, and a bi-level game model that incorporates a “pricing-response” approach is developed to enhance the feasibility of scheduling results, utilizing both Stackelberg and evolutionary game theories. Finally, uncertain factors are introduced, and the correlations between them are analyzed using copula theory, which results in model refinements that improve accuracy and pertinence. Simulations verify the effectiveness and economic efficiency of the proposed competitive incentive mechanism in terms of achieving a win-win situation for all agents.