Facultad Regional Reconquista

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Author: search.filters.author.Ferramosca, Antonio

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    State-space modelling of a commercial lithium-ion battery
    (IEEE Xplore, 2021-12-24) Alarcón, Rodrigo Germán; Alarcón, Martín Alejandro; González, Alejandro H.; Ferramosca, Antonio
    In this paper, a continuous and discrete-time dynamical model of a commercial lithium-ion battery is proposed. It is modelled through an electrical equivalent circuit, applying the method of parameter extraction in the time domain to determine its values. As parameters vary according to the state of charge of the battery, a procedure for parametric identification based on the least-squares method is presented, using the Simulink ® Design Optimization™.
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    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.
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    Economic model predictive control for energy management of a microgrid connected to the main electrical grid
    (ScienceDirect, 2022-09) Alarcón, Martín Alejandro; Alarcón, Rodrigo Germán; González, Alejandro H.; Ferramosca, Antonio
    Electric microgrids have become an interesting tool to facilitate the inclusion of renewable energies. Its architecture and control system plays a fundamental role in the implementation of these systems. This paper proposes a control strategy for the management of energy resources in a residential microgrid. The system is made up of a set of solar panels as renewable resource, a storage system formed by a lithium-ion battery bank and a consumption profile according to a residence. The microgrid will be connected to the main electrical grid and the proposed management strategy consists in the implementation of a suitable Economic Model Predictive Control, where it considers the costs of use for the different components of the microgrid, thus contemplating the participation of the system as an active agent in the electricity market. Simulations were carried out with different scenarios of available resources and prediction times. In all cases, the objectives fulfilling by satisfying the restrictions operational and technical imposed on the system.
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    Approximating the solution of an economic MPC using artificial neural networks
    (IEEE Xplore, 2024-05-21) Alarcón, Rodrigo Germán; Alarcón, Martín Alejandro; González, Alejandro H.; Ferramosca, Antonio
    Abstract: Economic model predictive control is a recognized advanced control strategy which calculates control actions by solving an optimization problem in real time. The issue of numerical computation is the main barrier to implementing this type of controller. Deep learning has emerged as a promising solution to reduce the computational cost. This paper proposes a deep learning approximation of an Economic MPC, particularly with artificial neural networks, of the control strategy for managing energy resources in a residential microgrid. Operational data were generated from the solution established by the controller to train, validate and test the neural network using Matlab. Simulation results showed that the proposed approach can approximate the control strategy correctly.
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    A scenario-based economic-stochastic model predictive control for the management of microgrids
    (ScienceDirect, 2023-12) Alarcón, Martín Alejandro; Alarcón, Rodrigo Germán; González, Alejandro H.; Ferramosca, Antonio
    Abstract The world’s electricity generation is heavily dependent on the consumption of fossil fuels. Electric generation from renewable resources is necessary due to the imperative need to reduce greenhouse gases to avoid a climate crisis. These resources exhibit random and intermittent behaviour. Therefore, there is a need to develop new management and control tools for these insertions into the current electricity system. Microgrids have become an effective tool to solve this problem, where these control systems play a principal role. For this reason, an optimal control structure consisting of two Model Predictive Control strategies is proposed for a microgrid Energy Management System. The first controller aims to optimise the microgrid’s economic performance under an established criterion, using nominal forecasts of the disturbances on the system, such as the energy generated by renewable resources. The second is a stochastic approach using scenario-based methods to consider forecast errors in the nominal predictions used for the disturbances. The simulations were carried out on a microgrid model corresponding to the National Technological University, Reconquista Regional Faculty, highlighting that actual samples of energy consumption are available. It is worth noting that with the proposed structure, optimal solutions are obtained considering the random behaviour of the disturbances, without making assumptions about the distribution functions of the random variables. Moreover, it applies to different scales of microgrids.
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    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.
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    Diseño de algoritmos de inteligencia artificial para reconocimiento de imágenes, con aplicación al pastoreo racional.
    (2021-12) Ferramosca, Antonio; Capozzolo, María Cecilia; Talijancic, Iván; Franzoi, Santiago; Peresón, Marcos Nahuel; Aguilar, Rubén Daniel; Marcón, Juan Pablo
    OBJETIVOS DE LA INVESTIGACIÓN: Desarrollar una estrategia de control avanzado para el vuelo autónomo en grandes extensiones de campos, de vehículos aéreos no tripulados (UAV), comunalmente conocidos como drones, del tipo quadrotor. Estos sistemas son muy difíciles de controlar, tratándose de sistemas no lineales multivariables, con dinámicas rápidas, y por lo tanto con tiempos de muestreos cortos, sujetos a restricciones y perturbaciones. La estrategia de control deberá además mejorar la autonomía del dron, que suele ser baja. Por esa razón, el objetivo será desarrollar una formulación de MPC con garantía de estabilidad y factibilidad recursiva que permita la incorporación de objetivos económicos (eso es maximizar la autonomía reduciendo el consumo), además de los objetivos dinámicos típicos (seguir una referencia espacial). Estos objetivos - dinámicos y económicos - suelen aparecer como contrapuestos cuando se los busca resumir en un solo costo de optimización. Además, será un objetivo fundamental explorar formas de garantizar la robustez estocástica de los controladores MPC económicos, esos es la robustez frente a perturbaciones y/o ruidos aditivos de tipo estocástico con distribución de probabilidad conocida (es decir señales de ruidos aleatorios con valor medio y varianza conocida). El UAV tendrá que completar una cierta tarea recurriendo amplias extensiones de campo (objetivo dinámico: seguimiento de referencia) sin correr el riesgo de quedarse sin batería (objetivo económico: autonomía). El otro objetivo, perseguido de manera simultánea y complementaria al anteriormente descripto, es el de desarrollar algoritmos de inteligencia artificial para el reconocimiento de imágenes en tiempo real, que sean implementable sobre la plataforma embebida de cómputo paralelo utilizada en él proyecto (NVIDIA JetsonTX1).
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    Modelado de una Microgrid residencial. Estudio de factibilidad y diseño de estrategias de control automático.
    (2021-12) Ferramosca, Antonio; Talijancic, Iván; Alarcón, Martín Alejandro; Alarcón, Rodrigo Germán
    OBJETIVOS DE LA INVESTIGACIÓN: Los objetivos de la investigación son los siguientes: 1. Modelado de una microrred residencial. Se comenzará con un estudio de factibilidad de la microrred, para su dimensionamiento. Desde ahí, se pasará al modelado matemático de la misma, utilizando ecuaciones fundamentales, y técnicas de identificación basadas en datos de planta. La naturaleza predictiva del controlador nos permite considerar cualquier información de la planta sobre la evolución futura de la misma. Esto hace que el controlador pueda operar la planta de manera más eficiente y segura. Con este fin, los datos históricos de la planta pueden ser explotados para obtener estimaciones de su comportamiento futuro. 2. Simular el comportamiento dinámico de la microrred. Se realizarán simulaciones en ambiente Matlab/Simulink. Para ello, habrá que programar el modelo obtenido en el punto anterior en el lenguaje apropiado, y se diseñaran test de ensayo específico para estudiar el comportamiento dinámico del sistema. 3. Estudiar técnicas de control avanzado, como ser Control Predictivo basado en modelos. Se comenzará, como es de rigor en estos casos, por una exhaustiva revisión bibliográfica, tanto en lo referente al control predictivo económico, como en las formulaciones existentes en la literatura de controladores predictivos para el control de microgrids y smartgrids. Se estudiarán también formulaciones distribuidas y estocásticas de MPC que, por la naturaleza propia del sistema en estudio, resultan de particular interés. 4. Diseño de algoritmos de MPC económico y su implementación distribuida para el control económicamente óptimo de los componentes de una microrred. El cálculo de la ley de control requiere la solución de un problema de optimización en línea. La naturaleza compleja de las redes inteligentes y su escala de tiempo hacen que la implementación de MPC sea una tarea desafiante. Se estudiarán algoritmos de optimización especializados adaptados a este problema para implementarse en sistemas integrados distribuidos. Se estudiará el concepto de control predictivo periódico, dada la naturaleza periódica del sistema. Se utilizará la teoría de juego para el estudio del control distribuido de los componentes de la mircrored, con el fin de evaluar el tipo de diseño más adecuado. (cooperativo, no cooperativo, coalicional). 5. Capacitar y formar recursos humanos en el manejo de software adecuados para el modelado, simulación de la microrred y en el manejo y aplicación de estrategias de control avanzado. En el marco de este proyecto se realizará un Proyecto Final de Carrera, (de la Carrera de Ing. Electromecánica de UTN FR Reconquista). Además, la investigación realizada será parte del proyecto de tesis de doctorado del Ing. Martin Alarcón, becario de doctorado UTN.
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    LSTM recurrent neural network for energy demand forecasting
    (2023-05-16) Alarcón, Rodrigo Germán; Alarcón, Martín Alejandro; González, Alejandro H.; Ferramosca, Antonio
    Abstract—Recurrent Neural Networks (RNN) of the Long Short Term Memory (LSTM) type provide high accuracy in predicting sequential models in various application domains. As in most process control problems, their dynamics include non manipulated variables that need to be predicted. This paper proposes using an LSTM neural network for energy demand forecasting, which applies to an Economic Model Predictive Control (EMPC) as a forecasting tool. For the training, data are taken from a three-phase intelligent power quality analyser located at the National Technological University, Reconquista Regional Faculty (Santa Fe, Argentina). A recursive strategy is used to update the state of the neural network and forecast over different prediction horizons. The accuracy achieved in training the neural network is measured using the root mean square error (RMSE) metric. Experimental results show that the proposed LSTM neural network has excellent generalisation capability.
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    Cooperative n-personal games in the coalitional economic control of a microgrid community
    (2023-05-16) Alarcón, Martín Alejandro; Alarcón, Rodrigo Germán; González, Alejandro H.; Ferramosca, Antonio
    Abstract—The deployment of microgrids connected to an electricity grid is increasing every day. These energy districts with their control system are the intelligent nodes of future electricity grids; therefore, strategies for managing these new systems must be developed and proposed. This paper presents a novel coalitional economic model predictive control strategy for managing a microgrid community. Because coalitional control considers the dynamic variation of coalitions of agents, a coop erative n-personal game with economic aspects occurs to decide which coalition to build, where the optimal control strategy to solve for each of these coalitions also takes place. Furthermore, an example proposes an economic criterion for using both the cooperative game and the control strategy for each coalition. Finally, some results on coalition formation are presented for the example mentioned above.