Modelo multiobjetivo para la gestión de energía en generadores fotovoltaicos conectados en redes de distribución considerando indicadores técnicos, económicos y ambientales

Abstract

This research addresses the problem of optimizing the operation of photovoltaic (PV) generation sources within single-phase alternating current distribution networks, considering various objective functions. The problem is posed as a multi-period optimal power flow applied to alternating current distribution networks, resulting in a non-linear programming (NLP) model with a non-convex structure. Three different objective functions are incorporated into the optimization model, each individually optimized. These objectives include: (i) operating costs, which cover the costs of purchasing energy at the substation bus or slack node along with the maintenance costs of the photovoltaic panels; (ii) costs for energy loss given the resistive and inductive characteristics of the conductors used in the distribution of electrical energy; and (iii) total CO2 emissions on the substation bus. The objective is to minimize these three functions within a 24-hour operational framework, that is, in an operational environment with one day in advance taking into account the hourly dispatch method used in Colombia. The specialized software Julia and its solvers JuMP and Ipopt are used to solve the NLP model that represents the problem. Numerical validations are carried out using two real and different test feeders: one designed to imitate the characteristics of urban operation in the Metropolitan Area of Medellín, which comprises 33 nodes, and the other adapted to isolated rural operation conditions in the department of Chocó, Colombia (specifically the municipality of Capurganá), with 27 nodes. Comparative numerical analyzes involving various combinatorial optimization methods from the literature are performed to demonstrate the effectiveness of the Julia software in achieving optimal one-day advance distribution of PV sources in both distribution networks.