Optimal Bidding Strategy of Demand Response Aggregator Based On Customers' Responsiveness Behaviors Modeling Under Different Incentives

Residential customers account for an indispensable part in the demand response (DR) program for their capability to provide flexibility when the system required. However, their available DR capacity has not been fully comprehended by the aggregator, who needs the information to bid accurately on behalf of the residential customers in the market transaction. To this end, this article devised an optimal bidding strategy for the aggregator considering the bottom-up responsiveness of residential customers. First, we attempt to establish the customers' responsiveness function in relation to different incentives, during which a home energy management system is introduced to implement load adjustment for electrical appliances. Second, the functional relation is applied to the aggregator's decision-making process to formulate the optimal bidding strategy in the day-ahead market and the optimal scheduling scheme for the energy storage system with the aim to maximize its own revenue. Finally, the validity of the proposed method is verified using the dataset from the Pecan Street experiment in Austin. The obtained outcome demonstrates the practical rationality of the proposed method.

Automated summary

This study proposed an optimal bidding strategy for aggregators in demand response programs focusing on residential customers, considering their bottom-up responsiveness. The method involves establishing customer responsiveness functions, introducing home energy management systems for load adjustment, and optimizing bidding strategies and scheduling schemes to maximize revenue. The validity of the proposed method was confirmed using data from the Pecan Street experiment in Austin, demonstrating its practical rationality.