Robust transmission network expansion planning considering non-convex operational constraints

This paper proposes a two-stage robust optimization model for the transmission network expansion planning problem. Long-term uncertainties in the peak demand and generation capacity are modeled using confidence bounds, while the short-term variability of demand and renewable production is modeled using a set of representative days. As a distinctive feature, this work takes into account the non-convex operation of conventional generating units and storage facilities, which results in a two-stage robust optimization model with a discrete recourse problem. The resulting problem is solved using a nested column-and-constraint generation algorithm that guarantees convergence to the global optimum in a finite number of iterations. An illustrative example and a case study are used to show the performance of the proposed approach. Numerical results show that neglecting the non-convex operation of conventional generating units and storage facilities leads to suboptimal expansion decisions. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper presents a two-stage robust optimization model for the transmission network expansion planning problem, taking into account both long-term and short-term uncertainties, as well as non-convex operation. The proposed approach shows improved performance compared to neglecting non-convex operation of traditional generating units and storage facilities.