Distributed Robust Optimization Method for AC/MTDC Hybrid Power Systems with DC Network Cognizant
Haixiao Li (TU Delft - Intelligent Electrical Power Grids, Chongqing University of Technology)
A. Lekic (TU Delft - Intelligent Electrical Power Grids)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
AC/multi-terminal DC (MTDC) hybrid power systems have emerged as a solution for the large-scale and long-distance 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, enabling 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 constructed 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 optimization method.