Static Transmission Network Expansion Planning Using Quantum PSO with Gaussian Attractor

Abstract

Transmission network expansion planning (TNEP) is essential for the proper functioning of an electric power system. This is a highly complex problem, characterized by its mixed-integer, nonlinear, and non-convex nature. In this context, meta-heuristic techniques stand out for producing optimal or near-optimal solutions (expansion alternatives) for combinatorial problems of this nature. In recent years, the Particle Swarm Optimization (PSO) algorithm has gained increasing attention in various fields of study. Given this scenario, this article proposes the application of the QPSO (Quantum PSO) algorithm, with a Gaussian attractor (Gaussian QPSO—GQPSO), as a method for formulating solutions for static TNEP (sTNEP). The power grid is modeled by a direct current (DC) load flow and disregards active losses in the circuits. The performance of the proposed optimizer was validated through simulations of two systems widely known in the literature: the IEEE system (24 buses/41 branches) and the equivalent Southern Brazil system (46 buses/79 branches). The results achieved demonstrate the feasibility and efficiency of the presented technique, offering new insights for the improvement of PSO.