Credit-based demand side incentive mechanism optimization for load aggregator

Abstract

Demand side resources play an important role in dealing with the seasonal and intermittent demand–supply mismatch problem in the power system under the global goal of carbon neutrality. By integrating the uncertain load reduction capacity of small-scale demand resources, load aggregator (LA) participates in demand response (DR) programs to obtain profits, and the deviation between the actual and its scheduled load reduction quantity leads to the loss of revenue. In order to guide users to declare reasonable demand side resource capacity, a credit-based incentive mechanism (CBIM) is designed for LA in this paper, and the corresponding bi-level optimization model of demand side incentive price is established. In the upper-level model, the optimization objective is to maximize the DR revenue of a specified LA, in which the compensation price associated with the response credit is provided to users. In the lower-level model, the expected load reduction is declared by users whose goal is to maximize their revenues considering the comfort loss as well as the compensation price. Simulations illustrate that by properly determining the credit-based demand side incentive price, users are encouraged to feedback load reduction according to their actual capacity, thus improving the revenue of LA in integrating demand side resources.