CONICET | Buscador de Institutos y Recursos Humanos

This paper presents a dynamic model for the distribution network expansion planning problem, which aims to reduce the utility costs. The model takes into account the traditionally used alternatives (such as substations upgrade and reinforcement of feeders) and the installation of conventional and renewable distributed generation (DG). Additionally, in order to assess the economic risk and get flexible expansion plans, the main parameters affected with uncertainty (demand growth, electricity prices in the wholesale power market and wind power output) are modeled stochastically by performing Monte Carlo simulations. The model is solved through an evolutionary particle swarm optimization (EPSO), together with an optimal power flow tool (OPF). The effectiveness of the proposed model is assessed through a case study on a test network of nine nodes.