Probabilistic decomposition‐based security constrained transmission expansion planning incorporating distributed series reactor

TLDR

The study proposes a probabilistic transmission expansion planning model that incorporates distributed series reactors to enhance network flexibility. The model is solved via a Benders decomposition approach that linearises the mixed‑integer nonlinear problem, uses Monte Carlo simulation to capture wind and demand uncertainty, and iteratively adds Benders cuts to reduce the optimality gap. Case‑study results on three test systems demonstrate the effectiveness of the proposed approach.

Abstract

This study presents a probabilistic transmission expansion planning model incorporating distributed series reactors, which are aimed at improving network flexibility. Although the whole problem is a mixed‐integer non‐linear programming problem, this study proposes an approximation method to linearise it in the structure of the Benders decomposition (BD) algorithm. In the first stage of the BD algorithm, optimal number of new transmission lines and distributed series reactors are determined. In the second stage, the developed optimal power flow problem, as a linear sub‐problem, is performed for different scenarios of uncertainties and a set of probable contingencies. The Benders cuts are iteratively added to the first stage problem to decrease the optimality gap below a given threshold. The proposed model utilises the Monte Carlo simulation method to take into account uncertainty of wind generations and demands. Several case studies on three test systems are presented to validate the efficacy of the proposed approach.