Facultad de Ingeniería

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A study of software architects’ cognitive approaches: kolb’s learning styles inventory in action
(Springer, 2024-10-19) Hidalgo Barrientos, Mauricio Fernando; Astudillo, Hernán; Castro, Laura M.
The multidisciplinary nature of software architects demands a diverse set of skills, ranging from technical expertise to interpersonal abilities. Within this domain, software architects are responsible for designing systems that adhere to quality standards, meet functional requirements, and align with organizational goals. However, educating or training software architects presents a challenge due to the complexity of their roles and responsibilities. To address this challenge, this paper proposes an approach to understanding the learning styles of software architects using Kolb’s Learning Style Inventory (KLSI). It aims to provide a characterization of their teaching and learning preferences, thus facilitating the design and execution of educational strategies tailored to their specific needs. In conducting our research, we utilized LinkedIn as a platform to distribute the KLSI Test, ultimately gathering a sample comprising 18 Senior and Mid-Senior Software Architects. Through trend analysis of their responses, we consistently observed a discernible pattern. This led us to identify the Deciding Learning Style as the primary approach among the sample of software architects regarding their learning preferences. This finding offers initial insights into the predominant learning style within this profession, providing valuable guidance for educational practitioners and institutions aiming to optimize their training programs for software architects.
A systematic review of recent developments in IoT-based demand side management for PV power generation
(De Gruyter, 2024-06-21) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Demand-side management (DSM) with Internet of Things (IoT) integration has become a vital path for optimizing photovoltaic (PV) power generating systems. This systematic review synthesizes and evaluates the latest advancements in IoT-based DSM strategies applied to PV power generation. The review encompasses a comprehensive analysis of recent literature, focusing on the key elements of IoT implementation, data analytics, communication protocols, and control strategies in relation to solar energy DSM. The combined results show how IoT-driven solutions are changing and how they might improve PV power systems’ sustainability, dependability, and efficiency. The review also identifies gaps in current research and proposes potential avenues for future investigations, thereby contributing to the ongoing discourse on leveraging smart DSM in the solar energy domain using IoT technology.
An IoT-based intelligent smart energy monitoring system for solar PV power generation
(De Gruyter Mouton, 2023-09-15) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
As the world’s attention turns to cleaner, more dependable, and sustainable resources, the renewable energy sector is rising quickly. The decline in world energy use and climate change are the two most significant factors nowadays. PV forecasting was essential to enhancing the efficiency of the real-time control system and preventing any undesirable effects. The smart energy management systems of distributed energy resources, the forecasting model of irradiation received from the sun, and therefore PV energy production might mitigate the impact of uncertainty on PV energy generation, improve system dependability, and increase the incursion level of solar power generation. Smart sensors and Internet of Things technologies are essential for monitoring and controlling applications in a broad range of fields. As a result, solar power generation forecasting was essential for microgrid stability and security, as well as solar photovoltaic integration in a strategic approach. This paper examines how to use IoT, a solar photovoltaic system being monitored, and shows the proposed monitoring system is a potentially viable option for smart remote and in-person monitoring of a solar PV system.
Análisis de canasta de mercado en supermercados mediante mapas auto-organizados
(Universidade Federal do Paraná, 2021-11-08) Cordero, Joaquín; Bolt, Alfredo José; Valle, Mauricio A.
Introducción: Una cadena importante de supermercados de la zona poniente de la capital de Chile, necesita obtener información clave para tomar decisiones. Esta información se encuentra disponible en las bases de datos, pero necesita ser procesada debido a la complejidad y cantidad de información, lo que genera una dificultad a la hora de visualizar. Método: Para este propósito, se ha desarrollado un algoritmo que utiliza redes neuronales artificiales, aplicando el método SOM de Kohonen. Para llevarlo a cabo, se han debido seguir ciertos procedimientos claves, como preparar la información, para luego utilizar solo los datos relevantes a las canastas de compra de la investigación. Luego de efectuado el filtrado, se tiene que preparar el ambiente de programación en Python para adaptarlo a los datos de la muestra, y luego proceder a entrenar el SOM con sus parámetros fijados luego de resultados de pruebas. Resultado: El resultado del SOM obtiene la relación entre los productos que más se compraron, posicionándolos topológicamente cerca, para conformar promociones y bundles, para que el retail mánager tome en consideración. Conclusión: En base a esto, se han hecho recomendaciones sobre canastas de compra frecuentes a la cadena de supermercados que ha proporcionado los datos utilizados en la investigación.
Applying multi-criteria analysis in a port system
(2017) Córdova, Felisa M.; Durán, Claudia
This work presents a study developed in a medium port system composed of 50 public and private actors interacting with their macro-environment, which can generate strategic synergistic relationships. In order to determine these synergistic links between their components, the strategic phrases contained in their missions are analysed and classified according to the multicriteria that are part of the macro-environment of each port actor: political, economic, social, technological, environmental, risk and learning. Likewise, new characterizations and classifications are proposed for groups and sub-groups of port actors. From absolute frequencies, Contingency Tables and the Chi-square test, quantitative results are obtained, which show the potential cases of strategic synergistic relations in the port system and the behavior of each group and sub-group of actors, as well as dependence / independence between every pair of criteria. Finally, it is verified that it is possible to use quantitative methods to analyze the strategic synergistic relationships between the actors of the port system.
Automatons immersed in ocean currents for transformation of biomass into fuel
(Springer, 2021) Cañete, Lucio; Córdova, Felisa; Perez de Arce, Andrés
When observing the Chilean sea from both biotic and mechanical perspective, oceanographers note that the Humboldt Current carries abundant biomass and that the movement of the water itself has the capacity to do work. Taking advantage of these two qualities of the ocean current, this article exposes the sketch of an automated device, the computational simulation when it was conceived and its mathematical model to make efficient the capture of biomass that will be processed, stored and dispatched as biodiesel. Said submerged automaton has a structural configuration that was outlined by cybernetic design resulting in a body that carries out the transformation process by itself, which starts on the side that faces the current with its content of biomass. This raw material is trapped thanks to an intelligent system that informs the reactor about the relative importance of the state variables that its body can control, stimulating those swimming organisms to move in the desired direction. The captured biomass begins its process until it becomes biodiesel by virtue of the mechanical energy provided by the same flow of seawater that affects the reactor. The rear part of the reactor releases both incident water and by-products into the sea without harmful environmental consequences. Some users of this new type of device are armies in time of conflict and merchant marines during algae bloom.
Challenges to applying role playing in software engineering education: a taxonomy derived from a rapid literature review
(Springer, 2024-07) Hidalgo Barrientos, Mauricio Fernando; Astudillo, Hernán; Castro, Laura M.
Role Playing (RP) serves as an instructional approach to enrich the learning experience for students and boost their learning by the effective application of their theoretical knowledge within a practical context. In Software Engineering Education (SEE), the utilization of RP proves beneficial in fostering the development of skills such as teamwork, problem-solving, and critical thinking among students and aids them in comprehending the intricacies and hurdles inherent in software development, instilling the significance of collaborative efforts and effective communication. To use role playing effectively, SEE teachers need to understand the challenges that arise from using it. This paper presents a taxonomy, resulting from the analysis of a rapid review developed to identify these challenges: a thorough review of relevant articles indexed by well-known digital libraries (Web of Science, Scopus, and IEEE Xplore) which, after inclusion/exclusion criteria, yielded a total of 23 papers. This taxonomy provides an organized structure for understanding the challenges in implementing Role Playiing (RP) activities in the context of Software Engineering Education (SEE).
Chapter 12: Demand side energy management algorithms integrated with the IoT framework in the PV smart grid system
(Academic Press, 2024-01-01) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
The smart grid revolution in the electric power sector will play a major role in the future. In the electric power system, the combination of new technology and communication infrastructure makes the grid smarter. To incorporate intelligence into the grid, many technological challenges must be solved, including those posed by energy storage systems, the integration of renewable sources, communication, protection, control, and demand-side management with customer involvement. Considering the rising need for electricity, one of the primary operational difficulties in the power system is balancing power generation to the constantly shifting load. Under the smart grid, utilities have realized that through demand side management and different demand response (DR) efforts, customer participation may be efficiently used for this balancing mechanism. The two-way communication between supply and demand can be successfully implemented with the help of smart grid intervention. Customers participate in DR schemes by actively reducing or shifting loads from peak to nonpeak hours concerning the pricing scheme. Therefore, it is essential to develop new demand response strategies for the smart grid, taking into consideration all features of the utility provider and the customers.
Chapter 14: Roles and Challenges of 6G for the Human–Computer Interface
(Wiley, 2024-12-02) Priyabrata, Dash; Akankshya, Patnaik; Sahoo, Sarat Kumar; Yanine, Fernando
Needless to say, technology flies like time. Within a relatively short span of about 30 years, we have been able to visualize the advent of 2G, 3G, 4G, and, finally today, the whole world is fondly using 5G. It is without a doubt that the 5G network is successfully satisfying customers’ needs, but dealing with the flow of excessive data and massive network densification all around the world has made things difficult for telecommunications and industry digitalization. Thus, the evolution of wireless communication beyond 5G (B5G) or 6G may not only be necessary for future industry growth, encompassing massive digital transformation of several industries, but also certainly plausible. In this context, we will show, for example, that 6G is expected to deal with multi-sensory technologies in order to create new ways for people to interact with each other and with other technologies, in an effort to be updated with the upcoming global sustainability and fairness trends. 6G is believed to be a self-content application of Artificial Intelligence. Thus, as researchers, it is the right time to investigate and discover upcoming 6G technology deliverables, especially for the human–computer interface (HCI). HCI is a multidisciplinary topic of investigation and also a broad term that connects Computer Science, Cognitive Science, and Human Factors Engineering. HCI no longer focuses on the behavior of individual or generic users, but it broadens its base towards organizational and social computing. This chapter aims to clarify the proposed road map that lies ahead, which identifies certain roles of 6G technology for the human–computer interface. It also explores its role in current businesses and organizations. The chapter also unearths key tenets of the literature on the subject as well as helps researchers to discover both identified and unidentified challenges of 6G for HCI. The chapter also intends to identify potential technology challenges that may lie ahead and provide a solution for many upcoming problems and open a new horizon for imminent research on the matter, in order to advance knowledge in these areas. The main purpose of this chapter is to analyze and deliberate the various potentials and flaws of 6G for human–computer interfaces.
Closed-shell d10–d10 mechanochromic [AuPh(CNPh)]n complex: quantum chemistry electronic and optical properties
(Royal Society of Chemistry, 2024-01-31) Mendizabal, Fernando; Cerón, María Luisa; Lara, Dina; Miranda-Rojas, Sebastián
The electronic structure, spectroscopic properties, and solid state chemistry of monomer and dimers of [AuPh(CNPh)] complex were studied at post-Hartree-Fock (MP2, SCS-MP2, and CC2) and density functional theory levels. The absorption spectra of these complexes were calculated using single excitation time-dependent (TD) methods at DFT, CC2, and SCS-CC2 levels. The influences of the bulk are accounted for at the PBE-D3 level, incorporating dispersion effects. The calculated values agree with the experimental range, where absorption and emission energies reproduce experimental trends with large Stokes shifts. The aurophilic interaction is identified as a key factor influencing the spectroscopic and structural properties of these complexes. The intermetallic interactions were found as the main factor responsible for MMCT electronic transitions in the models studied.
Deep learning aplicado para la detección de hemorragias y tumores cerebrales
(Universidade Federal do Paraná, 2021-12-01) Hidalgo Barrientos, Mauricio Fernando; Hayes Ortiz, Bryan Isaac; Delgadillo Vera, Ignacio; Goyo Escalona, Manuel
Introducción: Un problema que afecta a la salud en Chile se refiere a las patologías cerebrales, toma de exámenes y el alto tiempo de espera para la obtención de los resultados (retrasando el diagnóstico y tratamiento). Actualmente, los exámenes se envían al extranjero para ser procesados y el tiempo de espera juega en contra del paciente. Dada esta realidad, nuestro documento propone un modelo de deep learning para la predicción de imágenes cerebrales que permita obtener un diagnóstico previo, pero no definitivo, en virtud de disminuir el tiempo del proceso y, de ser necesario, priorizar a los pacientes cuya vida estaría potencialmente en riesgo. Métodos: El desarrollo utilizó un enfoque RAD iterativo y las imágenes se recogieron de Kaggle. Adicionalmente, se redimensiona el dataset para normalizar el tamaño y generamos nuevas imágenes utilizando “data augmentation”. Las imágenes fueron procesadas en redes convolucionales, indagando en distintas configuraciones para la red, su optimizador y la función de activación, hasta llegar a un modelo que consideramos razonable. Resultados: Con el modelo definitivo, los resultados superan el 80% de precisión en las predicciones y descubrimos que separar patologías (hemorragias y tumores) fue crucial para este resultado. Conclusiones: Hemos logrado una herramienta de diagnóstico previo, pero se debe continuar la investigación en virtud de aumentar la precisión. Un próximo paso considera ampliar el dataset con imágenes de otras fuentes y separar el modelo para analizar patologías de forma independiente. Motivamos a seguir investigando ya que este tipo de apoyo puede contribuir a salvar vidas.
Design and deployment of a novel decisive algorithm to enable real-time optimal load scheduling within an intelligent smart energy management system based on IoT
(Elsevier, 2024-12-01) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Consumers routinely use electrical devices, leading to a disparity between consumer demand and the supply side a significant concern for the energy sector. Implementing demand-side energy management can enhance energy efficiency and mitigate substantial supply-side shortages. Current energy management practices focus on reducing power consumption during peak hours, enabling a decrease in overall electricity costs without sacrificing usage. To tackle the mentioned challenges and maintain system equilibrium, it is essential to develop a flexible and portable system. Introducing an intelligent energy management system could pre-empt power outages by implementing controlled partial load shedding based on consumer preferences. During a demand response event, the system adapts by imposing a maximum demand limit, considering various scenarios and adjusting appliance priorities. Experimental work, incorporating user comfort levels, sensor data, and usage times, is conducted using Smart Energy Management Systems (SEMS) integrated with cost-optimization algorithms.
Design and development of grid connected home automation system for prosumers
(Institute of Electrical and Electronics Engineers (IEEE), 2023-03-23) Patro, Swati P.; Sahoo, Sarat Kumar; Thanarak, Paprita; Yanine, Fernando
Home automation system extend convenience and sophisticated to operate electronic product with in the household. Home automation can be a solution to automating functions like monitoring, controlling etcetera in the home. This paper describes the drives, restraints, opportunities and challenges pertaining to the home automation system market. The grid solar power system is connected to the utility grid where solar power is generated. The power generated if in excess send back to utility grid. Hence the consumer gets compensation as the extra power sent back to the utility grid. Whenever the sunlight, electricity is generated by solar cell, the inverter connected to grid convert the dc power into ac power. The electricity produced is routed to the grid from, where it used to run the various appliances such as air conditioners, washing machine, refrigerator, geyser, induction cooker, pump etc. The excess power at every instant is transmitted to the ground without grid connection and if solar power system is synchronized with the utility grid then return to the grid. It will also determine the feasibility of reducing green gas emission by the use of grid tied PV power system.
Design of an EEG analytical methodology for the analysis and interpretation of cerebral connectivity signals
(Elsevier, 2022-02-20) Córdova, Felisa M.; Cifuentes, Hugo F.; Díaz, Hernán A.; Yanine, Fernando; Pereira, Robertino
The objective of this study is to design an Electroencephalographic (EEG) analytic methodology that allows to develop a variety of analysis and interpretations of brain signals. The initial phase considers the acquisition and filtering of EEG signals, the division into bands in data ranges, and the storage of EEG signals in a cloud data base. Then, an analytical phase considering descriptive, predictive and prescriptive analysis is accomplished. A sequence of analytic intermediate processing steps is done in order to render a graphic visualization of significant correlations between pairs of EEG channels. Pearson correlation is utilized to detect synchronic connectivity through the brain areas. Time series in nearly instantaneous time lapses are treated by using Hilbert Huang Transform. An experimental design by submitting a set of students to an abbreviated version Raven visual test is made providing results in correlation maps of cerebral connectivity
Design of smart socket for monitoring of IoT-based intelligent smart energy management system
(Springer, 2021) Rao, Challa Krishna; Kumar Sahoo, Sarat; Balamurugan, M.; Yanine, Fernando
Smart socket is designed for collecting and sending the data from the various nodes in one field to other fields. Smart socket consists of the Arduino_Uno, XBee, sensors, gateway, computer, USB, and IDE. This works emphasis on design and development of smart socket with wireless capability, this can be used to collect the data from each electrical device by using sensors. An XBee transmitter and receiver node are used for data communication in wireless networks. Real-time data gathered at the central node can be used to prioritize and schedule the appliances. Then, the system analyzes the data to generate control commands to turn the devices attached to the smart socket on or off. This paper presents the operation and functions of smart socket in different sensor network topologies. The results show that the proposed smart socket can correctly read the data from the various nodes and also send it to different nodes of different parameters.
Designing an intelligent smart energy monitoring system for optimizing the utilization of PV energy
(Springer Nature, 2024-11-01) Rao, Challa Krishna; Sahoo, Sarat Kumar; Yanine, Fernando
Consumers in both residential and commercial settings are increasingly interested in reducing their energy consumption, influenced by feed-in tariffs for renewable resources and the recent surge in electricity rates. This study introduces a central control system and a smart power plug utilizing the XBee communication protocol to effectively manage energy usage. Smart energy management systems are employed to measure and optimize power consumption at the consumer premises level. The primary objective of this paper is the design and development of wireless smart plugs capable of assessing various power characteristics and collecting real-time data on individual consumer appliances' power usage. The SEMS setup establishes a Consumer Area Network through an XBee transmitter and receiver node, enabling real-time data collection at the central node for scheduling and prioritizing appliances. Utilizing the SEMS setup, consumer appliance datasets are generated and additional datasets aid in load disaggregation. The system configuration enables wireless data transfer from smart outlets to a central controller. Control instructions derived from data analysis are then used by the system to turn connected devices to the smart plug on or off. Test results indicate that the proposed smart plug accurately assesses power consumption up to eighteen meters away without compromising data integrity. The central controller, guided by a planned user program code, effectively manages multiple plugs based on the test findings. The Smart Energy Management algorithm suggests that employing smart plugs as load controllers results in a significant decrease in energy consumption (0.811 kW min or 0.0134 kWh) when accompanied by the appropriate scheduling algorithm. This technology holds potential in a comprehensive smart energy management system. The data's insights highlight the superiority of the proposed approach compared to current standard practices.
Diagnosing grid's service quality issues: the virtual microgrid and the digitization and innovations in the power distribution grid
(Elsevier, 2024-08-23) Cordova, Felisa; Yanine, Fernando; Carrasco, Sergio; Rother, Hans; Duarte, Vanessa
Power quality issues from small and medium-sized grid-integrated solar photovoltaic systems (PMGD), whose plant capacity is less than or equal to 9 MW, challenge electric power distribution companies like ENEL Distribución S.A. in Chile. To tackle these challenges, leveraging rapidly configurable, scalable, and easily deployable distributed energy resources (DER) through a virtual power plant (VPP) is crucial. This paper introduces a "virtual microgrid," a specific VPP design and functional specification that swiftly diagnoses and resolves power quality issues. Virtual power plants use renewable energy sources, energy storage, and smart energy management to provide ancillary services to the grid. With the expanding PMGD market in Chile and globally, there is an urgent need for deployable DER to energize feeder sub-sections during reliability events and create grid pathways between DER and loads. Thus, DER are essential for enhancing grid flexibility, reliability, and maintaining the stability and integrity of electric supply quality standards. VPPs can reduce high costs associated with utility grid power capacity deficiencies in areas with unstable power supply, enhancing resilience and flexibility without compromising system stability. This study presents a novel approach—currently at the conceptual development stage—for diagnosing service quality issues related to grid supply through the rapid deployment of a configurable and fully scalable virtual microgrid. This solution is particularly relevant for zones in Santiago, Chile, where ENEL Chile faces challenges due to the increasing presence of PMGD—independent solar farms injecting all their production into the grid for profit, as permitted by current Chilean electric law (N°88/2019 of Ministry of Energy). Such practices negatively impact grid service quality standards, which can be mitigated through the deployment of virtual microgrids. The concept of a "virtual microgrid" emphasizes rapid configurability, scalability, and ease of deployment, tailored to address specific grid quality issues. Unlike previous efforts, this approach focuses on seamlessly integrating DER with existing grid infrastructure to quickly address and mitigate power quality issues caused by PMGD.
Diseño y desarrollo de aplicación móvil para la clasificación de flora nativa chilena utilizando redes neuronales convolucionales
(Universidade Federal do Paraná, 2022-01-22) Muñoz Villalobos, Ignacio Andrés; Bolt, Alfredo José
Introducción: Las aplicaciones móviles, a través de la visión artificial, son capaces de reconocer especies vegetales en tiempo real. Sin embargo, las actuales aplicaciones de reconocimiento de especies no consideran la gran variedad de especies endémicas y nativas de Chile, tendiendo a predecir erróneamente. Esta investigación presenta la construcción de un dataset de especies chilenas y el desarrollo de un modelo de clasificación optimizado e implementado en una aplicación móvil. Método: La construcción del dataset se realizó a través de la captura de fotografías de especies en terreno y selección de imágenes de datasets en línea. Se utilizaron redes neuronales convolucionales para desarrollar los modelos de predicción de imágenes. Se realizó un análisis de sensibilidad al entrenar las redes, validando con k-fold cross validation y efectuando pruebas con distintos hiperparámetros, optimizadores, capas convolucionales y tasas de aprendizaje, para seleccionar los mejores modelos y luego ensamblarlos en un solo modelo de clasificación. Resultados: El dataset construido se conformó por 46 especies, incluyendo especies nativas, endémicas y exóticas de Chile, con 6120 imágenes de entrenamiento y 655 de prueba. Los mejores modelos se implementaron en una aplicación móvil, donde se obtuvo un porcentaje de acierto de aproximadamente 95% con respecto al conjunto de pruebas. Conclusiones: La aplicación desarrollada es capaz de clasificar especies correctamente con una probabilidad de acierto acorde con el estado del arte de la visión artificial y de mostrar información de la especie clasificada.
Editorial: Recent advances in renewable energy automation and energy forecasting
(Frontiers Media S.A., 2023-05-10) Sahoo, Sarat Kumar; Yanine, Fernando; Kulkarni, Vikram; Kalam, Akhtar
Renewable energy sources like solar, wind, and hydroelectric power are gaining popularity as we work towards a more sustainable future. However, their intermittent and often unpredictable nature, creates challenges for the energy industry in terms of being able to ensure continuous electric power generation over regular periods of time. Thus, accurate forecasting of renewable energy output is crucial for their reliable integration into the power grid. In this regard, automation and machine learning have made significant improvements in energy forecasting by enabling more precise predictions of energy output. Advanced algorithms and high-performance computing systems allow for better grid management and increased power generation systems’ efficiency. Automation is also being used for the operation and maintenance of renewable energy systems. Real-time monitoring and control systems enable a rapid response to changes in weather conditions, optimizing energy production. This editorial summarizes recent advancements in renewable energy automation and energy forecasting, which are critical areas for achieving a sustainable energy future. The Research Topic covers areas like machine learning-based energy forecasting, control and optimization of renewable energy systems, and the integration of renewable energy into microgrids as shown in Figure 1. Continued research and development in renewable energy automation and energy forecasting are essential for the transition towards a sustainable energy future.
Energy homeostasis management strategy for building rooftop nanogrids, considering the thermal model and a HVAC unit installed
(Elsevier, 2022-02-04) Yanine, Fernando; Sanchez-Squella, Antonio; Barrueto, Aldo; Sahoo, Sarat Kumar; Parejo, Antonio; Cordova, Felisa M.
This paper presents a case study on power control and energy management for a 60 apartments’ residential building with solar generation and energy storage tied to the grid in Santiago, Chile. A new energy management algorithm based on energy homeostasis is designed for a small electro thermal generation system (nanogrid), with smart metering. The test bed employs supervisory control with energy management that regulates the temperature inside a large room by the action of an HVAC (Heating/Ventilating/Air Conditioning) unit. The main objective of supervisory control is to allow temperature comfort for residents while evaluating the decrease in energy cost. The study considers a room with rooftop grid-tie nanogrid with a photovoltaic and wind turbine generation plant, working in parallel. It also has an external weather station that allows predictive analysis and control of the temperature inside the abode. The electrical system can be disconnected from the local network, working independently (islanding) and with voltage regulation executed by the photovoltaic generation system. Additionally, the system has a battery bank that allows the energy management by means of the supervisory control system. Under this scenario, a set of coordination and supervisory control strategies, adapted for the needs defined in the energy management program and considering the infrastructure conditions of the network and the abode, are applied with the aim of efficiently managing the supply and consumption of energy, considering Electricity Distribution Net Billing Laws 20.571 and 21.118 in Chile (https://www.bcn.cl/historiadelaley/historia-de-la-ley/vista-expandida/7596/), the electricity tariffs established by the distribution company and the option of incorporating an energy storage system and temperature control inside the room. The results show the advantage of the proposed tariffs and the overall energy homeostasis management strategy for the integration of distributed power generation and distribution within the smart grid transformation agenda in Chile. Este artículo presenta un caso de estudio sobre control de potencia y gestión de energía para un edificio residencial de 60 departamentos con generación solar y almacenamiento de energía conectado a la red en Santiago, Chile. Se diseña un nuevo algoritmo de gestión energética basado en la homeostasis energética para un pequeño sistema de generación electrotérmica (nanogrid), con medición inteligente. El banco de pruebas emplea un control de supervisión con administración de energía que regula la temperatura dentro de una habitación grande mediante la acción de una unidad HVAC (Calefacción/Ventilación/Aire acondicionado). El objetivo principal del control de supervisión es permitir el confort de la temperatura para los residentes mientras se evalúa la disminución del costo de la energía. El estudio considera una habitación con nanorredes conectadas a la red en la azotea con una planta de generación fotovoltaica y eólica, trabajando en paralelo. También cuenta con una estación meteorológica externa que permite el análisis predictivo y control de la temperatura al interior de la morada. El sistema eléctrico se puede desconectar de la red local, trabajando de forma independiente (islanding) y con regulación de tensión ejecutada por el sistema de generación fotovoltaica. Adicionalmente, el sistema cuenta con un banco de baterías que permite la gestión de la energía a través del sistema de control de supervisión. Bajo este escenario, se aplican un conjunto de estrategias de coordinación y control supervisor, adaptadas a las necesidades definidas en el programa de gestión energética y considerando las condiciones de infraestructura de la red y del domicilio, con el objetivo de gestionar eficientemente el suministro y consumo de energía, considerando las Leyes de Facturación Neta de Distribución Eléctrica 20.571 y 21.118 de Chile (https://www.bcn.cl/historiadelaley/historia-de-la-ley/vista-expandida/7596/), las tarifas eléctricas establecidas por la empresa distribuidora y la posibilidad de incorporar un sistema de almacenamiento de energía y control de temperatura en el interior de la estancia. Los resultados muestran la ventaja de las tarifas propuestas y la estrategia general de gestión de la homeostasis energética para la integración de la generación y distribución de energía distribuida dentro de la agenda de transformación de redes inteligentes en Chile.

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