Examinando por Autor "Cano-Ortega, Antonio"

Mostrando 1 - 20 de 30

A comparison of sizing methods for a long-term renewable hybrid system. Case study: Galapagos Islands 2031
(The Royal Society of Chemistry, 2021-02-04) Cano-Ortega, Antonio; Arévalo, Paul; Jurado-Melguizo, Francisco
This research compares different sizing methods to improve the current autonomous hybrid system in the Galapagos Islands in the year 2031, analyzing the loss of power supply probability (LPSP). In the first place, the energy consumed in the islands for the year 2031 has been obtained, using ANN artificial neural networks with Matlab, from fundamental parameters in the design of a multilayer perceptron neural network model. Second, the methods used for sizing the system are HOMER Pro and Simulink Design Optimization (SDO). The dynamic models of the different components of the hybrid system have been made in MATLAB/Simulink. The proposed hybrid system is composed of PV photovoltaic and WT wind, and lead-acid batteries, hydraulic pumping, and diesel generator as storage and support systems. Then, in order to design a sustainable system, a hybrid system has been dimensioned with renewable energy sources of an appropriate size. The LPSP values obtained are below 0.09% and 0.22%, which shows that the system has been optimally dimensioned. In addition, a cost analysis has been carried out, the values obtained from NPC and COE according to HOMER Pro are $ 183,810,067 and 0.26 $/kWh, and $ 233,385,656 and 0.25 $/kWh and using SDO are $ 148,523,110 and 0.25 $/kWh, $ 189,576,556 and 0.24 $/kWh for strategies I and II respectively of the proposed hybrid system. The data obtained shows that SDO's Latin Hypercube algorithm achieves a better optimization compared to HOMER Pro.Abstract text goes here. The abstract should be a single paragraph that summarises the content of the article.
A novel experimental method of power smoothing using supercapacitors and hydrogen for hybrid system PV/HKT
(Elsevier, 2023-09-01) Arévalo, Paul; Cano-Ortega, Antonio; Jurado-Melguizo, Francisco
Nowadays, the intermittent nature of renewable energy systems represents one of the most significant challenges in isolated systems, where power fluctuations can cause instability and compromise energy quality. Although hydrogen systems and supercapacitors have been widely studied in the literature, they have been less investigated as participating agents, and further research is needed in this area. This paper presents a novel power smoothing method for an off-grid system that consist of photovoltaic panels, hydrokinetic turbines, fuel cells and a hybrid storage system (hydrogen and supercapacitors). Two well-known power smoothing methods were used to generate the power signals for the new method. The main novelty is based on controlling the state of charge of the supercapacitor using the fuel cell, for the reduction of power fluctuations and efficiently hydrogen produce. First, the capacity of the renewable system is optimized using the HOMER Pro software. Then, the optimized system was used to simulate the new method proposed in Matlab-Simulink. Finally, to validate the results obtained, extensive experiments were conducted in a laboratory test bench. The results showed that the power fluctuations index was reduced by up to 50 % in the electrolyzer and 20 % in the fuel cell, with a levelized cost of electricity of 0.19 USD/kWh. Therefore, the application of the new proposed energy smoothing method significantly improves hydrogen production.
A Platform for Analysing Huge Amounts of Data from Households, Photovoltaics, and Electrical Vehicles: From Data to Information
(MDPI, 2022-12-01) Cano-Ortega, Antonio; García-Cumbreras, Miguel Ángel; Sánchez-Sutil, Francisco; de-la-Casa-Hernández, Jesús
Analytics is an essential procedure to acquire knowledge and support applications for determining electricity consumption in smart homes. Electricity variables measured by the smart meter (SM) produce a significant amount of data on consumers, making the data sets very sizable and the analytics complex. Data mining and emerging cloud computing technologies make collecting, processing, and analysing the so-called big data possible. The monitoring and visualization of information aid in personalizing applications that benefit both homeowners and researchers in analysing consumer profiles. This paper presents a smart meter for household (SMH) to obtain load profiles and a new platform that allows the innovative analysis of captured Internet of Things data from smart homes, photovoltaics, and electrical vehicles. We propose the use of cloud systems to enable data-based services and address the challenges of complexities and resource demands for online and offline data processing, storage, and classification analysis. The requirements and system design components are discussed.
Calibration of a Class A Power Quality Analyser Connected to the Cloud in Real Time
(MDPI, 2024-08-13) Cano-Ortega, Antonio; Sánchez-Sutil, Francisco; Casa-Hernández, Jesús; Baier, Carlos; Gilabert-Torres, Carlos
Power quality measurements are essential to monitor, analyse and control the operation of smart grids within power systems. This work aims to develop and calibrate a PQ network analyser. As the penetration of non-linear loads connected to power systems is increasing every day, it is essential to measure power quality. In this sense, a power quality (PQ) analyser is based on the high-speed sampling of electrical signals in single-phase and three-phase electrical installations, which are available in real time for analysis using wirelessWi-Fi (Wireless-Fidelity) networks. The PQAE (Power Quality Analyser Embedded) power quality analyser has met the calibration standards for Class A devices from IEC 61000-4-30, IEC 61000-4-7 and IEC 62586-2. In this paper, a complete guide to the tests included in this standard has been provided. The Fast Fourier Transform (FFT) obtains the harmonic components from the measured signals and the window functions used reduce spectral leakage. The window size depends on the fundamental frequency of, intensity of and changes in the signal. Harmonic measurements from the 2nd to 50th harmonics for each phase of the voltage and each phase and neutral of the current have been performed, using the Fast Fourier transform algorithm with various window functions and their comparisons. PQAE is developed on an open-source platform that allows you to adapt its programming to the measurement needs of the users.
Comparative analysis of HESS (battery/supercapacitor) for power smoothing of PV/HKT, simulation and experimental analysis
(Elsevier, 2022-09-17) Cano-Ortega, Antonio; Arévalo, Paul; Benavides, Darío; Jurado-Melguizo, Francisco
Photovoltaic and hydrokinetic systems are increasing their penetration in electrical distribution systems. This leads to problems of power fluctuations due to the intermittence of renewable sources that could compromise the stability and quality of the power grid. To address this issue, this paper presents a feasibility study of three power smoothing methods for a photovoltaic-hydrokinetic system using laboratory equipment to optimally replicate the real behavior of this type of hybrid system. The proposed algorithms are based on a hybrid storage system with supercapacitors and lithium-ion batteries, several analyzes are presented based on technical and economic parameters. The results demonstrate the feasibility of power smoothing methods for real systems, the comparison between the algorithms highlights the characteristics of the Enhanced Linear Exponential Smoothing Method, reducing the energy cost and regulating the point of common coupling voltage. Moreover, the sensitivity studies show that the energy exchange with the utility grid is affected according to the variations in the capacity of the batteries and the response to power smoothing can decrease or improve depending on the size of the supercapacitors.
Design and Implementation of a Smart Energy Meter Using a LoRa Network in Real Time
(MDPI, 2021-12-17) Sánchez-Sutil, Francisco; Cano-Ortega, Antonio; de-la-Casa-Hernández, Jesús
Nowadays, the development, implementation and deployment of smart meters (SMs) is increasing in importance, and its expansion is exponential. The use of SMs in electrical engineering covers a multitude of applications ranging from real-time monitoring to the study of load profiles in homes. The use of wireless technologies has helped this development. Various problems arise in the implementation of SMs, such as coverage, locations without Internet access, etc. LoRa (long range) technology has great coverage and equipment with low power consumption that allows the installation of SMs in all types of locations, including those without Internet access. The objective of this research is to create an SM network under the LoRa specification that solves the problems presented by other wireless networks. For this purpose, a gateway for residential electricity metering networks using LoRa (GREMNL) and an electrical variable measuring device for households using LoRa (EVMDHL) have been created, which allow the development of SM networks with large coverage and low consumption.
Design and Testing of a Power Analyzer Monitor and Programming Device in Industries with a LoRA LPWAN Network
(MDPI, 2021-02-11) Sánchez-Sutil, Francisco; Cano-Ortega, Antonio
Electrical installations represent an important part of the industry. In this sense, knowing the state of the electrical installation in real time through the readings of the installed power analyzers is of vital importance. For this purpose, the RS485 bus can be used, which most electrical installations already have. An alternative to the bus wiring and its distance limitation is the use of low-power wide area networks (LPWAN). The long range (LoRa) protocol is ideal for industries due to its low-power consumption and coverage of up to 10 km. In this research, a device is developed to control all the reading and programming functions of a power analyzer and to integrate the device into the LoRa LPWAN network. The power analyzer monitor and programming device (PAMPD) is inexpensive and small enough to be installed in electrical panels, together with the power analyzer, without additional wiring. The information collected is available in the cloud in real time, allowing a multitude of analysis be run and optimization in real time. The results support high efficiency in information transmission with average information loss rate of 3% and a low average transmission time of 30 ms.
Determination of the power smoothing effect in a photovoltaic-hydrokinetic system by experimental analysis and pattern search
(Elsevier, 2023-03-26) Arévalo , Paul; Cano-Ortega, Antonio; Jurado-Melguizo, Francisco
Sizing optimization and power smoothing methodology in highly fluctuating grid-connected renewable systems represent important challenges nowadays, the total cost and energy quality depend on the effective balance between source and load with certain restrictions to increase reliability and enhance the use of renewable sources at all scales. This article presents a novel power smoothing effect and sizing optimization methodology for a grid-connected photovoltaic-hydrokinetic system by pattern search method comparatively with three optimization algorithms encompassing a multi-objective optimization. In this context, the method considers the reduction of power fluctuations using supercapacitors, where the objective function considers technical and economic indexes. The main contribution of the research is that the objective function decreases the fluctuation suppression ratio to sizing optimization of renewable system through three pattern search size optimization algorithms. Experiments have shown that pattern search method can find the global optimum by reducing the computational effort; the Latin hypercube algorithm reduces the photovoltaic capacity by approximately 5% and 2.3% with respect to the Nelder Mead and genetic algorithm respectively. The influence of the supercapacitor successfully reduces power fluctuations and causes an increase in self-consumption of 32.32%, this result represents an annual cost savings of 42% on energy purchased from the grid. Furthermore, this paper goes further by calculating the optimal location of a hydrokinetic turbine in a river using the HEC-RAS software to determine with high precision the HKT power fluctuations as input signal to novel proposed method.
Development and Calibration of an Open Source, Low-Cost Power Smart Meter Prototype for PV Household-Prosumers
(MDPI, 2019-08-07) Sánchez-Sutil, Francisco; Cano-Ortega, Antonio; de-la-Casa-Hernández, Jesús; Rus-Casas, Catalina
Smart meter roll-out in photovoltaic (PV) household-prosumers provides easy access to granular meter measurements, which enables advanced energy services. The design of these services is based on the training and validation of models. However, this requires temporal high-resolution data for generation/load profiles collected in real-world household facilities. For this purpose, this research developed and successfully calibrated a new prototype for an accurate low-cost On-time Single-Phase Power Smart Meter (OSPPSM), which corresponded to these profiles. This OSPPSM is based on the Arduino open-source electronic platform. Not only can it locally store information, but can also wirelessly send these data to cloud storage in real-time. This paper describes the hardware and software design and its implementation. The experimental results are presented and discussed. The OSPPSM demonstrated that it was capable of in situ real-time processing. Moreover, the OSPPSM was able to meet all of the calibration standard tests in terms of accuracy class 1 (measurement error 1%) included in the International Electrotechnical Commission (IEC) standards for smart meters. In addition, the evaluation of the uncertainty of electrical variables is provided within the context of the law of propagation of uncertainty. The approximate cost of the prototype was 60 € from eBay stores.
Development and implementation of a PQ analyser to monitoring public lighting installations with a LoRa wireless system
(Elsevier, 2023-01-30) Sánchez-Sutil, Francisco; Cano-Ortega, Antonio
In smart cities, public lighting installations have a significant consumption of electrical energy. In order to measure this consumption, it is necessary to install sensors capable of obtaining electrical parameters in real time. In this way, the installations are monitored and energy saving and efficiency measures can be implemented. In this sense, this research has developed a PQ analyser meter with Long Range wireless communications technology (LoRa). This technology allows coverage of up to 5 km, which makes it ideal for public lighting applications. PQ Analyser with LoRa (PQAL) has been developed to achieve a fully functional prototype. PQAL comes in two versions for single-phase and three-phase street lighting lines, so that it adapts to any type of installation. PQAL uses the open-source Arduino platform which allows it to adapt to the monitoring needs of every installation. PQAL can monitor installations up to 100 A and 23 kW per phase. The use of non-invasive current sensors allows this device to be installed without modifying the installation to be monitored. Due to the long range of the LoRa network, it can be installed in locations far from the gateway without the need for additional cabling or Internet connection.
Energy Management Model for a Remote Microgrid Based on Demand-Side Energy Control
(MDPI, 2023-12-28) Benavides, Darío; Arévalo, Paul; Cano-Ortega, Antonio; Sánchez-Sutil, Francisco; Villa-Ávila, Edisson
The internet of things is undergoing rapid expansion, transforming diverse industries by facilitating device connectivity and supporting advanced applications. In the domain of energy production, internet of things holds substantial promise for streamlining processes and enhancing efficiency. This research introduces a comprehensive monitoring and energy management model tailored for the University of Cuenca’s microgrid system, employing internet of things and ThingSpeak as pivotal technologies. The proposed approach capitalizes on intelligent environments and employs ThingSpeak as a robust platform for presenting and analyzing data. Through the integration of internet of things devices and sensors, the photovoltaic system’s parameters, including solar radiation and temperature, are monitored in real time. The collected data undergo analysis using sophisticated models and are presented visually through ThingSpeak, facilitating effective energy management and decision making. The developed monitoring system underwent rigorous testing in a laboratory microgrid setup, where the photovoltaic system is interconnected with other generation and storage systems, as well as the electrical grid. This seamless integration enhances visibility and control over the microgrid’s energy production. The results attest to the successful implementation of the monitoring system, highlighting its efficacy in improving the supervision, automation, and analysis of daily energy production. By leveraging internet of things technologies and ThingSpeak, stakeholders gain access to real-time data, enabling them to analyze performance trends and optimize energy resources. This research underscores the practical application of internet of things in enhancing the monitoring and management of energy systems with tangible benefits for stakeholders involved.
Evaluation of temporal resolution impact on power fluctuations and self-consumption for a hydrokinetic on grid system using supercapacitors
(Elsevier, 2022-05-18) Cano-Ortega, Antonio; Arévalo, Paul; Jurado-Melguizo, Francisco
The power output obtained from the hydrokinetic renewable system fluctuates with changes in weather conditions, which could cause adverse effects on the voltage, frequency and stability of the electrical grid. In this article, an evaluation of power smoothing is performed for a renewable hydrokinetic on grid system using a supercapacitor. The power fluctuations are mitigated and smoothed by the proposed energy control and supplied to the utility grid and household load. Moreover, this paper studies the impact of the temporal resolution of data sampling with respect to the self-consumption of prosumers under technical, economic and environmental parameters using Matlab/Simulink software. The simulation results show that the fluctuations of the hydrokinetic turbine output power can be significantly mitigated, using the supercapacitor based storage system for different temporal resolutions reducing up to 90% of power peaks and power fluctuations produced by the hydrokinetic turbine and the load. Besides, the use of supercapacitors allows increasing the self-consumption of prosumers up to 17.27% for time resolutions of 1 min. Finally, the proposed control reduces up to 0.54 kgCO2/day at cost of energy 0.15 USD/kWh.
Evolución de los sistemas energéticos en Iberoamérica
(Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo -CYTED-, 2025-06-05) de-la-Casa-Hernández, Jesús; Enríquez-Zuñiga, Andrea; Cano-Ortega, Antonio; Martínez-García, Antonio; Taveras-Cruz, Armando; Dias, Bruno; Borba, Bruno; Boj, Carlos Alfredo; Romero-Aquino, Carlos; Vásquez-Stanescu, Carmen; Vega-Penagos, César; Medina-Morel, Christian; Fleck, Conrado; Rivera-López, Dennis; Calderón-Alfonso, Doris; Andrade-Rengifo, Fabio; Martín-Serra, Federico; Henríquez, Félix; Rustan Roca Subirana, Félix; Santos-García, Félix; Weschenfelder, Franciele; Sánchez-Sutil, Francisco; Pico-Mera, Gabriel; Magallanes-Galla, Gualberto; Magaldi, Guillermo; Tzoc, Héctor; Herrera-Moya, Idalberto; López-Díaz, Iosvani; Sánchez-Figueroa, Ismael; Mírez-Tarrillo, Jorge Luis; Rodas, Jorge; Fariña, Juan; Fuentes-Gallo, Juan Carlos; Moreno-Castro, Juan; Andela, Julián; Delorme, Larizza; Guerra-Hernández, Lázaro; Cruz-Sánchez, Lisner; Valarezo, Lucio; Martínez-Figueroa, Luis Aarón; Mogollón, Luis; Rodolfo-Montes, Luis; Ayala-Silva, Magno; González-Valdez, Manuel; Flores, Marco; Intriago-Cedeño, María Gabriela; Rodríguez-Gámez, María; Escalante, Mauricio; Bueno-López, Maximiliano; Aybar-Mejía, Miguel; Castro-Fernández, Miguel; Vilaragut, Miriam; Balderramo-Vélez, Ney Raúl; García, Omar; Medinilla, Oscar; Canto-Franco, Oscaryvan; González-Barrios, Osvaldo; Maidana, Paola; Peters Barbosa, Pedro; Ebert, Priscila; Gregor, Raúl; Orellana, Renán; Pérez-Cedeño, Rhonmer; Quintal-Palomo, Roberto; Acevedo, Rubén; Condeff, Susana; Ocaña-Guevara, Víctor; Vega-Garita, Víctor; Silva, Walquiria; Canedo, Walter
Esta publicación entrega una compilación del estado actual de los sistemas de generación de energía eléctrica en 19 países de Iberoamérica, lo cual le permitirá al lector tener una completa radiografía de la situación actual de este sector para esta región. El trabajo conjunto de los miembros de la Red para la integración a gran escala de energías renovables en los sistemas eléctricos (RIBIERSE-CYTED) ha permitido tener este documento el cual esperamos se convierta en un medio de consulta para los sectores de la academia, industria y gubernamental y a partir de las lecciones aprendidas en los diferentes países de la región se pueda planear el sector energético de manera que el servicio que se entrega a los usuarios finales sea cada vez más confiable y de alta calidad.
Extraction and characterization of Cucumis melon seeds (Muskmelon seed oil) biodiesel and studying its blends impact on performance, combustion, and emission characteristics in an internal combustion engine
(Elsevier BV, 2024-05-28) Al-Bawwat, Ala’a K.; Gomaa, Mohamed R.; Cano-Ortega, Antonio; Jurado-Melguizo, Francisco; Alsbou, Eid Musa
This study examines the performance, combustion, and emissions characteristics of a single-cylinder internal combustion diesel engine when fueled with a blend of diesel and biodiesel derived from muskmelon seeds. The kinematic viscosity of the extracted muskmelon seed oil was 6.1 cSt at 40 ◦C, which is higher than the kinematic viscosity of petroleum diesel of 2.6 cSt. Muskmelon biodiesel was further analyzed using thin-layer chromatography (TLC) and high-voltage separator tests. A comparison of the fuel properties of muskmelon biodiesel with conventional diesel fuel revealed that muskmelon biodiesel could be used alone or in a diesel– biodiesel blend to fuel compression diesel engines. In this study, muskmelon seed biodiesel was blended with diesel fuel at proportions of 10 %, 20 %, and 50 % (BD10, BD20, and BD50, respectively). At a relatively low rotational speed of 1200 rpm, the brake thermal efficiency (BTE) of the engine operated with BD10 and BD20 blends were 36.1 % and 36.0 %, respectively, while the brake-specific fuel consumption (BSFC) of the two blends were 0.260 kg/kWh, and 0.262 kg/kWh, respectively. These values closely resemble those typically observed in diesel fuel engines. Indeed, the average BTE of the BD20 blend was only 3.24 % less than the average BTE of diesel fuel. Diesel fuel generates less NOx and SO2 emissions compared to biodiesel blends: BD100 emitted the most NOx pollution of all fuels tested. In addition, BD10 released significantly more SO2 emissions compared to the other fuels tested. However, the BD20 blend outperformed all other blends in terms of CO, NOx, and SO2 emissions at high engine speeds. The only exception was H2S emissions, which were higher than BD50 and BD100. BD20 also exhibited significantly reduced CO emissions compared to diesel fuel, while BD10 emitted significantly more CO emissions than the other biodiesel blends. Our findings revealed that BD20 exhibited the best engine performance and lower emissions among all fuels tested. In other words, BD20 is the ideal fuel blend for use in diesel engines and does not require any alterations to the engine. Muskmelon waste seeds represent a non-edible waste stream that can be exploited in the production of biodiesel fuel, allowing for the upcycling of a potentially problematic thermochemical conversion feedstock. This potentially valuable use for waste muskmelon seeds in the energy sector could address the wastefulness associated with this particular waste stream.
Fault analysis in clustered microgrids utilizing SVM-CNN and differential protection
(Elsevier, 2024-07-10) Arévalo, Paul; Cano-Ortega, Antonio; Benavides, Darío; Jurado-Melguizo, Francisco
The integration of distributed generation, microgrids, and renewable energy sources has significantly enhanced the resilience of modern electrical grids. However, this transition presents challenges in control, stability, safety, and protection due to low fault currents from renewables. This paper addresses these challenges by proposing novel methodologies to enhance fault detection, classification, and localization in microgrids. The literature review highlights a shift towards intelligent learning methods in microgrid protection systems, improving fault response times and identifying electrical faults, including high impedance faults. Nonetheless, existing methods often neglect high impedance fault detection and the integration of differential protection in clustered microgrids. To fill these gaps, this study presents a methodology combining support vector machines and convolutional neural networks for fault detection in microgrids, integrating differential protection for high impedance fault detection. The paper also proposes approaches to optimize protection in clustered microgrid systems. The effectiveness of the methodology is validated using Opal-RT through comparative analyses of signal decomposition techniques, performance and accuracy of support vector machines and convolutional neural networks, KFold validation, and sensitivity analysis. Results demonstrate robustness and high performance, achieving up to 100 % accuracy in fault detection and classification.
Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain
(MDPI, 2020-10-23) de-la-Casa-Hernández, Jesús; Sánchez-Sutil, Francisco; Cano-Ortega, Antonio; Baier, Carlos
Smart meter (SM) deployment in the residential context provides a vast amount of data of high granularity at the individual household level. In this context, the choice of temporal resolution for describing household load profile features has a crucial impact on the results of any action or assessment. This study presents a methodology that makes two new contributions. Firstly, it proposes periodograms along with autocorrelation and partial autocorrelation analyses and an empirical distribution-based statistical analysis, which are able to describe household consumption profile features with greater accuracy. Secondly, it proposes a framework for data collection in households at a high sampling frequency. This methodology is able to analyze the influence of data granularity on the description of household consumption profile features. Its e ectiveness was confirmed in a case study of four households in Spain. The results indicate that high-resolution data should be used to consider the full range of consumption load fluctuations. Nonetheless, the accuracy of these features was found to largely depend on the load profile analyzed. Indeed, in some households, accurate descriptions were obtained with coarse-grained data. In any case, an intermediate data-resolution of 5 s showed feature characterization closer to those of 0.5 s.
Integrating discrete wavelet transform with neural networks and machine learning for fault detection in microgrids
(Elsevier, 2023-10-23) Cano-Ortega, Antonio; Arévalo, Paul; Benavides, Darío; Jurado-Melguizo, Francisco
Microgrids are essential for integrating renewable energy sources into the power grid. However, fault detection is challenging due to bidirectional energy flow. Traditional relay-based systems struggle in microgrids, primarily because of limited fault currents from grid-connected renewable energy inverters. To address these challenges, this paper proposes a new methodology for fault detection and classification in a renewable microgrid. The main contributions encompass two key aspects. Firstly, it enhances fault detection performance in microgrids characterized by nonlinear relationships, including photovoltaic, hydrokinetic, and variable electric load systems. Secondly, the combination of the discrete wavelet transform with various types of neural networks and supervised learning techniques provides a robust methodology for fault detection and classification. The proposed approach is evaluated using an IEEE-5 feeder test bed representing a realistic ring network configuration. The results show that the radial basis function neural network model exhibited promising outcomes, yielding a low prediction error of 1.31 e-31, highlighting its practical potential for enhancing system reliability and performance. Furthermore, various test cases were conducted by altering the ground resistance to train the neural networks, demonstrating the effectiveness of this neural network in accurately identifying fault conditions. Additionally, this research achieved promising outcomes with other models, including support vector machine and nonlinear autoregressive with external input, emphasizing the adaptability of these models in fault detection.
Large-scale integration of renewable energies by 2050 through demand prediction with ANFIS, Ecuador case study
(Elsevier, 2023-10-21) Arévalo, Paul; Cano-Ortega, Antonio; Jurado-Melguizo, Francisco
The growing reliance on hydroelectric power and the risk of future droughts pose significant challenges for power systems, especially in developing countries. To address these challenges, comprehensive long-term energy planning is essential. This paper proposes an optimized electrical system for 2050, using Ecuador as a case study. For forecasting electricity demand, a Neuro-Fuzzy Adaptive Inference System is employed, utilizing real historical data. Subsequently, the EnergyPlan software constructs a long-term energy consumption model, exploring three scenarios based on Ecuador's energy potential. The first scenario represents a 'business as usual' approach, mirroring the current trend in the Ecuadorian electricity system. In contrast to the second scenario, it encompasses a broader range of renewable sources, including offshore wind, pumped storage, biomass, and geothermal energy. The third scenario extends the second one by incorporating demand response systems, such as vehicle-to-grid and hydrogen-to-grid technologies. In terms of novelty, this study highlights the innovative use of the Neuro-Fuzzy Adaptive Inference System for demand forecasting, along with a comprehensive exploration of multiple scenarios to optimize the electrical system. Research findings indicate that the integration of these new renewable energy sources not only reduces electricity import costs but also ensures surplus electricity production. Consequently, it is anticipated that the 2050 electricity system will reduce its dependence on hydroelectric energy while adopting photovoltaic and wind energy with penetration rates of 65%, 11.2%, and 9%, respectively. This transition will be facilitated by a pumped storage system with a 28% penetration rate and enhanced connectivity with neighboring countries, enabling the seamless integration of electric and hydrogen vehicles
Mitigation of carbon footprint with 100% renewable energy system by 2050: The case of Galapagos islands
(Elsevier, 2022-01-17) Arévalo, Paul; Cano-Ortega, Antonio; Jurado-Melguizo, Francisco
In this paper, a technical-economic study of the 100% renewable energy sources in the Galapagos islands is done. Historical consumption data for 2011-2020 have been considered to forecast the load curve. To achieve this goal, the load is forecasting by Nonlinear autoregressive exogenous neuronal network model for 2030 and 2050. The study focuses on supplying the energy demand of the islands with renewable sources, analyzing possible scenarios based on the current electricity system. The methodology studies the capacity of renewable sources to balance supply and demand through dispatch simulations using the EnergyPLAN software. The results show energy flows, costs and long-term energy balances (2050), with 100% renewable energy from several wind and photovoltaic combinations. Moreover, The precision of the demand forecast was 98.12% with a mean square error of 0.013%. The total annual cost decreases while the capacities of the renewable sources increase to a certain point of equilibrium. As salient features of the developed approach, various sensitivity analyzes are presented that allow understanding the uncertainties, scope and limitations of the proposed models.
Monitoring of the E ciency and Conditions of Induction Motor Operations by Smart Meter Prototype Based on a LoRa Wireless Network
(MDPI, 2019-09-16) Cano-Ortega, Antonio; Sánchez-Sutil, Francisco
The installation of smart meters in the industry to monitor induction motors (IMs) provides easy access to the measurements of the electrical and mechanical variables, which improves the installation process. Using smart meters in industry requires temporary high-resolution data to improve the energy e ciency (EE) and power factor (PF) of IMs. For these purposes, Long Range (LoRa) is an ideal wireless protocol for the usage in industries due to its low energy consumption. In addition, it provides secure communications and long range indoors and outdoors. LoRa avoids the need to install antennas or routers to extend coverage, as each gateway can service 300 LoRa devices with distances of up to 10 km. For this purpose, this research successfully developed a new prototype for a low-cost IM E ciency Monitoring System using LoRa (IMEMSL). IMEMSL is based on the Arduino open-source electronic platform. It sends the acquired data through the LoRa low-power wide-area-network (LPWAN) and cloud access gateway. This document describes the hardware and software design and implementation. The experimental results are presented and discussed. Received Signal Strength Indicator (RSSI) and time on air measured endorse the use of LoRa for this type of meters. The approximate cost of the prototype was ¿72 and ¿93 for the versions without and with Global Positioning System (GPS), respectively.

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Examinando por Autor "Cano-Ortega, Antonio"