Diseño y simulación de un algoritmo de control inteligente utilizando Model Predictive Control para la gestión del autoconsumo energético a nivel doméstico.

Abstract

The objective of the graduate research project was to design and simulate a smart control algorithm that allows managing self-consumption through tools that automate the energy balance of a house using predictive control based on models. The algorithm has historical data, capable of predicting the states of consumption of the equipment (home appliances) to detect variables allowing to develop and optimize the consumption and generation of electrical energy through renewable energies (photovoltaic panels). The proposed solution by the tool depends on the capacity to perform a good load prediction by analyzing its behavior and effectiveness based on different variables. The methodology to be followed was based on the choice of solar panels, batteries, and typical loads of a home network to determine its posture. This path is used to mathematically model the plant's dynamics and develop the necessary predictive control algorithms to reach the energy efficiency target. All this process was carried out using the Matlab/Simulink software. In order to prove the proper system functioning, comparisons between the Model Predictive Control algorithm and the Rule-Based Control algorithm were carried out with three different loads, simulating the energy consumption, where the MPC algorithm evidenced a better performance. It is concluded that photovoltaic energy generation methods are essential to work together with the MPC, MPPT, and PLL algorithms to keep the system in optimal energy management and operate at its maximum generated power in perfect synchronization with the network, ensuring a power factor be as close as to the unit. It is recommended in future research, that the inductive loads should be considered, as the resistive loads were tested in the present investigation.