FRRQ - Producción de Investigación

Permanent URI for this communityhttp://48.217.138.120/handle/20.500.12272/2846

Browse

Author: search.filters.author.Alarcón, Martín AlejandroSubject: search.filters.subject.microgrid

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Economic model predictive control for energy management in a hybrid storage microgrid
    (IEEE Xplore, 2021-12-24) Alarcón, Martín Alejandro; Alarcón, Rodrigo Germán; González, Alejandro H.; Ferramosca, Antonio
    This paper proposes a Model Predictive Control (MPC) strategy for energy resources management in a microgrid. A state-space discrete-time linear model is presented, characterized by an hybrid storage system, consisting of lithium ion battery banks and ultracapacitors. The renewable resource is composed by an array of solar panels and the microgrid is considered to be connected to the main electricity grid, having the possibility to interact with it, under certain restrictions. Simulation results are presented under different generation and consumption scenarios.
  • Thumbnail Image
    Item
    Artificial neural networks for energy demand prediction in an economic MPC-Based energy management system
    (International Journal of Robust and Nonlinear Control, 2024-10-20) Alarcón, Rodrigo Germán; Alarcón, Martín Alejandro; González, Alejandro H.; Ferramosca, Antonio
    ABSTRACT Microgrids are a development trend and have attracted a lot of attention worldwide. The control system plays a crucial role in implementing these systems and, due to their complexity, artificial intelligence techniques represent some enabling technologies for their future development and success. In this paper, we propose a novel formulation of an economic model predictive control (economic MPC) applied to a microgrid designed for a faculty building with the inclusion of a predictive model to deal with the energy demand disturbance using a recurrent neural network of the long short-term memory (RNN-LSTM). First, we develop a framework to identify an RNN-LSTM using historical data registered by a smart three-phase power quality analyzer to provide feedforward power demand predictions. Next, we present an economic MPC formulation that includes the prediction model for the disturbance within the optimization problem to be solved by the MPC strategy. We carried out simulations with different scenarios of energy consumption, available resources, and simulation times to highlight the results obtained and analyze the performance of the energy management system. In all cases, we observed the correct operation of the proposed control scheme, complying at all times with the objectives and operational restrictions imposed on the system.