SDecPP-DC: State Decomposition Privacy-Preserving Optimization Algorithm for Incentive-Based Demand Response in Smart Grid

This paper considers the incentive-based demand response (IDR) economic dispatch problem (EDP) in smart grid while preserving the privacy of sensitive information, where the sensitive information is the consumers’ electricity consumption. The incentive-based demand response (IDR) optimization objective function as an EDP is established. A novel state decomposition-based privacy-preserving distributed consensus algorithm (SDecPP-DC) is designed to address this EDP, where the state decomposition mechanism is proposed to preserve the privacy of sensitive information. The feedback gains in the SDecPP-DC algorithm for the mismatch variables are non-coordinated and constant. The convergence of the proposed SDecPP-DC algorithm is theoretically proved by using multi-parameter perturbation theory. It is shown that the SDecPP-DC algorithm can deal with the directed network topology with a row-stochastic matrix, and the convergence point is the optimal solution of EDP. Finally, the correctness and effectiveness of SDecPP-DC are confirmed by the experiments.