A Data-Driven Stackelberg Market Strategy for Demand Response-Enabled Distribution Systems

Abstract

A data-based Stackelberg market strategy for a distribution market operator (DMO) is proposed to coordinate power dispatch among different virtual power plants (VPP), i.e., demand response aggregators (DRA). The proposed strategy has a two-stage framework. In the first stage, a data-driven method based on noisy inverse optimization estimates the complicated price-response characteristics of customer loads. The estimated load information of the DRAs is delivered to the second stage where a one-leader multiple-follower stochastic Stackelberg game is formulated to represent the practical market interaction between the DMO and the DRAs that considers the uncertainty of renewables and the operational security. The proposed data-driven game model is solved by a new penalty algorithm and a customized distributed hybrid dual decomposition-gradient descent (HDDGD) algorithm. Case studies on a practical demand response project in China and a distribution test system demonstrate the effectiveness of the proposed methodology.