Robust transmission network expansion planning considering non-convex operational constraints

Abstract 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. A two-stage robust optimization model for transmission network expansion planning. Solution procedure is based on a nested column-and-constraint generation algorithm. Long-term uncertainties are modeled through confidence bounds. Representative days model the daily evolution of wind production and demand. Neglecting non-convex operational constraints leads to suboptimal solutions.