Network-Constrained Robust Unit Commitment for Hybrid AC/DC Transmission Grids

TL;DR

This paper presents a scalable, joint energy and reserve scheduling method for hybrid AC/DC power grids, utilizing a two-stage optimization with convex relaxation to enhance reliability and capacity utilization amid renewable energy integration.

Contribution

It introduces a novel two-stage iterative optimization algorithm for hybrid AC/DC grids that ensures AC network feasibility and supports renewable energy integration.

Findings

Convex relaxation supports convergence and schedule validity.

Hybrid AC/DC topology improves grid capacity utilization.

Simulation demonstrates effectiveness on large-scale networks.

Abstract

The day-ahead energy and reserve management with transmission restrictions and voltage security limits is a challenging task for large-scale power systems in the presence of real-time variations caused by the uncertain demand and the fluctuating power output of renewable energy sources (RESs). The proposed formulation in this work supports joint scheduling of energy and reserve to promote an economic and reliable operation. To improve its scalability, a two-stage iterative optimization algorithm is proposed based on Bender's decomposition framework. Therewith, the optimal schedule is computed subject to the feasibility of AC network constraints (AC-NCs) at predetermined uncertain realizations. A convex relaxation is applied to AC-NCs to support the convergence of the algorithm. Moreover, the integration of RESs is often limited by transmission congestion issues in existing grids. For such grids, AC to DC conversion schemes can be viable and attractive options for capacity expansion. In this study, we adopt a hybrid AC/DC transmission grid (HTG) architecture with a specific topology to improve the effective utilization of the grid capacity and accommodate more demand and RESs, which is showcased via simulations on a large-scale network. Moreover, HTG induces exactness of the convex relaxation of AC-NCs, supporting the validity of the optimal schedule.