Privacy-Preserving Distributed Optimization for Economic Dispatch with Event-Triggered Mechanisms: A Linear Time-Varying Transformation Approach

Abstract

Dear Editor, This letter addresses the privacy-preserving distributed optimization for economic dispatch (ED) problem. Firstly, the gradient descent method is introduced. Then, an event-triggered mechanism (ETM) and a linear time-varying transformation (LTVT) rule are introduced to alleviate communication pressure and safeguard privacy, respectively. Sufficiency conditions for the convergence of the distributed ED (DED) algorithm are established based on the property of monotonic convergence of sequences. Furthermore, the privacy of the LTVT rule is rigorously proven for both honest-but-curious devices and external eavesdroppers. Moreover, the convergence and privacy aspects of the DED algorithm, integrating general LTVT rules, are extensively discussed. Finally, the effectiveness of the DED algorithm is validated through the IEEE 39-bus system.