Distributed data-driven distributionally robust Volt/Var control for distribution network via an accelerated alternating optimization procedure

Abstract

This paper proposes a distributed data-driven distributionally robust volt/var control (DDDR-VVC) approach which schedules on-load-tap changer (OLTC), capacitor banks (CBs) and Photovoltaic (PV) inverters coordinately. With integration of distributed optimization and data-driven distributionally robust optimization, the DDDR-VVC model is constructed to reduce power losses and maintain voltage within allowable range under uncertainty. To fully address the uncertainty impacts, construction of uncertain set is improved through that the probability distribution sets and trapezoidal fuzzy functions are developed to model PV supply and load demand respectively. An accelerated alternating optimization procedure which integrates the alternating direction method of multipliers and the column-and-constraint generation algorithm is proposed. This method improves the dual information update via Nesterov’s accelerated gradient method, thus solving the DDDR-VVC model at a high convergence rate. The effectiveness of proposed method is verified through numerical simulations using IEEE 123 bus test system.