Privacy-preserving-based distributed state-decomposition convex optimization for multi-DESS operations

Abstract

The distributed convex optimization strategies are widely used for voltage regulation and current distribution among distributed energy storage systems (DESSs). Meanwhile, consensus-based secondary control is commonly employed but risks leaking initial state information when exchanged explicitly. To address this issue, a distributed state-decomposition convex optimization (DSDCO) approach is proposed for current distribution in DC networks, ensuring the protection of DESSs' initial states. In DSDCO, a state-decomposition strategy is implemented, where the state variable of each DESS is divided into two sub-state variables with random initial values. One sub-variable is dedicated to external consensus control, safeguarding the node's true initial state, while the other manages internal dynamics to achieve global consensus. The theoretical analysis confirms that DSDCO can achieve state consensus in finite time and effectively maintain privacy. Subsequently, the first state variable that incorporates consensus control is used for current allocation and loss optimization. Finally, the privacy-preserving effectiveness of DSDCO is validated through both simulation and experimental case studies.