Distributed Robust Optimization Method for AC/MTDC Hybrid Power Systems with DC Network Cognizance

TL;DR

This paper introduces a distributed robust optimization approach for AC/MTDC hybrid power systems that incorporates DC network reconfiguration and RES uncertainties, enhancing operational flexibility and computational efficiency.

Contribution

It develops a novel AC/DC OPF model with DC topology reconfiguration and integrates a distributed robust optimization method using GBD and ESM for RES uncertainty mitigation.

Findings

Validated effectiveness through numerical simulations

Achieved parallel computation and asynchronous updates

Enhanced robustness against RES uncertainties

Abstract

AC/multi-terminal DC (MTDC) hybrid power systems have emerged as a solution for the large-scale and longdistance accommodation of power produced by renewable energy systems (RESs). To ensure the optimal operation of such hybrid power systems, this paper addresses three key issues: system operational flexibility, centralized communication limitations, and RES uncertainties. Accordingly, a specific AC/DC optimal power flow (OPF) model and a distributed robust optimization method are proposed. Firstly, we apply a set of linear approximation and convex relaxation techniques to formulate the mixed-integer convex AC/DC OPF model. This model incorporates the DC network-cognizant constraint and enables DC topology reconfiguration. Next, generalized Benders decomposition (GBD) is employed to provide distributed optimization. Enhanced approaches are incorporated into GBD to achieve parallel computation and asynchronous updating. Additionally, the extreme scenario method (ESM) is embedded into the AC/DC OPF model to provide robust decisions to hedge against RES uncertainties. ESM is further extended to align the GBD procedure. Numerical results are finally presented to validate the effectiveness of our proposed method.