Shapley Value Estimation for Compensation of Participants in Demand Response Programs

Designing fair compensation mechanisms for demand response (DR) is challenging. This paper models the problem in a game theoretic setting and designs a payment distribution mechanism based on the Shapley value (SV). As exact computation of the SV is in general intractable, we propose estimating it using a reinforcement learning algorithm that approximates optimal stratified sampling. We apply this algorithm to a DR program that utilizes the SV for payments and quantify the accuracy of the resulting estimates.