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

Area of Science:

• Electrical Engineering
• Computer Science
• Information Security

Background:

• Economic dispatch (ED) in smart grids (SGs) faces privacy challenges for generators and consumers.
• Existing consensus-based algorithms for ED may disclose sensitive electricity usage and generation data.
• Protecting privacy is crucial to prevent potential financial losses and ensure participation in electricity markets.

Purpose of the Study:

• To propose a novel distributed and effective heterogeneous privacy-preserving consensus-based (DisEHPPC) ED scheme.
• To address the critical need for privacy in electricity generation and consumption data within SGs.
• To develop a framework ensuring both optimal ED solutions and robust data privacy.

Main Methods:

• A two-phase approach employing differential privacy for consumer demand protection (Kullback-Leibler privacy).
• Development of a privacy-preserving incremental cost consensus-based (PPICC) algorithm for generator energy and price sensitivity privacy ((ε,δ)-privacy).
• Integration within a demand response (DR)-based framework with a DR server, data manager, and local controllers.

Main Results:

• The proposed DisEHPPC scheme guarantees Kullback-Leibler privacy for consumer demand data.
• The PPICC algorithm achieves (ε,δ)-privacy for generator energy and price sensitivity.
• Theoretical analysis confirmed the convergence and optimality of the ED solution alongside the privacy degree.
• Numerical experiments validated the effectiveness and correctness of the proposed scheme.

Conclusions:

• The DisEHPPC scheme successfully achieves simultaneous optimal economic dispatch and privacy preservation for both supply and demand sides in smart grids.
• The developed privacy-preserving techniques effectively safeguard sensitive electricity data.
• The study demonstrates a viable solution for secure and efficient smart grid operations.