DisEHPPC: Enabling Heterogeneous Privacy-Preserving Consensus-Based Scheme for Economic Dispatch in Smart Grids

These days, the increasing incremental cost consensus-based algorithms are designed to tackle the economic dispatch (ED) problem in smart grids (SGs). However, one principal obstruction lies in privacy disclosure for generators and consumers in electricity activities between supply and demand sides, which may bring great losses to them. Hence, it is extraordinarily essential to design effective privacy-preserving approaches for ED problems. In this article, we propose a two-phase distributed and effective heterogeneous privacy-preserving consensus-based (DisEHPPC) ED scheme, where a demand response (DR)-based framework is constructed, including a DR server, data manager, and a set of local controllers. The first phase is that Kullback–Leibler (KL) privacy is guaranteed for the privacy of consumers’ demand by the differential privacy method. The second phase is that $({\varepsilon,\delta })$ -privacy is, respectively, achieved for the generation energy of generators and the sensitivity of electricity consumption to electricity price by designing the privacy-preserving incremental cost consensus-based (PPICC) algorithm. Meanwhile, the proposed PPICC algorithm tackles the formulated ED problem. Subsequently, we further carry out the detailed theoretical analysis on its convergence, optimality of final solution, and privacy degree. It is found that the optimal solution for the ED problem and the privacy preservation of both supply and demand sides can be guaranteed simultaneously. By evaluation of a numerical experiment, the correctness and effectiveness of the DisEHPPC scheme are confirmed.