Person:
Arévalo Cordero, Wilian Paul

ORCID

0000-0002-6721-1326

Scopus Author ID

57211026456

Afiliación

Universidad de Cuenca, Cuenca, Ecuador
Universidad de Cuenca, Departamento de Ingeniería Eléctrica, Electrónica y Telecomunicaciones(DEET), Cuenca, Ecuador
Universidad de Jaén

País

Ecuador

Organizational Units

Organizational Unit

Facultad de Ingeniería

La Facultad de Ingeniería, a inicios de los años 60, mediante resolución del Honorable Consejo Universitario, se formalizó la Facultad de Ingeniería de la Universidad de Cuenca, conformada por las escuelas de Ingeniería Civil y Topografía. Esta nueva estructura permitió una mayor especialización y fortalecimiento en áreas clave para el desarrollo regional. Cuenta con programas académicos reconocidos internacionalmente, que promueven y lideran actividades de investigación. Aplica un modelo educativo centrado en el estudiante y con procesos de mejora continua. Establece como prioridad una educación integra, la formación humanística es parte del programa de estudios que complementa a la sólida preparación científico-técnica. Las actividades culturales pertenecen a un programa permanente y activo al interior de nuestras dependencias, a la par de proyectos que desde el alumnado y bajo la supervisión de docentes cumplen con servicios de apoyo a nivel local y regional; promoviendo así una vinculación estrecha con la comunidad.

Job Title

Profesor (C)

Last Name

Arévalo Cordero

First Name

Wilian Paul

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Now showing 1 - 10 of 18

Experimental validation of a novel power smoothing method for on-grid photovoltaic systems using supercapacitors
(2023) Benavides Padilla, Darío Javier; Arévalo Cordero, Wilian Paul
Renewable energy sources have been widely developed in grid-connected systems. However, a challenge to overcome is the random characteristic of renewable resources such as solar irradiance, photovoltaic power fluctuations caused by cloud movement could cause instability of the utility grid. To solve this drawback, several authors have proposed various power smoothing methods for photovoltaic systems using supercapacitors. Nevertheless, sizing optimization and operability of the supercapacitor has not been properly studied. Forecasting power fluctuations is an important strategy to avoid the unnecessary operation of the supercapacitor in certain cases. In this paper, a novel power smoothing method (predictor – corrector) using supercapacitors for a grid-connected photovoltaic system is proposed, the method consists of two stages, prediction and correction. The main novelty of the new method is the use a simple k-means algorithm application model in the cycle estimation stage for supercapacitors, with the aim of selecting representative data of power fluctuations and supercapacitor charge/discharge cycles. Then, for the correction stage, the novel proposed method uses ramp rate algorithms to generate the reference signal to control the state of charge of the supercapacitor. The validation of the new proposed method has been done through exhaustive laboratory experiments under different cloudiness events. The results show that the energy losses when applying the new method are lower with respect to the moving average and ramp rate methods. Furthermore, the number of technical violations is reduced, demonstrating the feasibility of the proposed method to ensure successful mitigation of PV power fluctuations.
A Novel Fuzzy-Logic-Based Control Strategy for Power Smoothing in High-Wind Penetrated Power Systems and Its Validation in a Microgrid Lab
(2023) Ochoa Correa, Danny Vinicio; Arévalo Cordero, Wilian Paul
Wind power generation has undergone significant development in recent decades due to its environmental advantages and its economic competitiveness. However, its increasing level of penetration is not exempt from drawbacks, such as those derived from the fluctuating nature of the wind. To reduce its negative incidence on grid power quality and stability, different techniques have been developed, such as those based on power smoothing. In these techniques, there is a research gap on the adjustment of the time constant that adapts to the needs of the smoothing, avoiding uncertain results, computational efforts and delays in the response of the control. This paper addresses the problem, proposing a novel method for power smoothing in a wind turbine by using a fuzzy-logic-based supercapacitor storage system and time-constant fitting, with a first-order adaptive transfer function. The method considers as input variables the active power generated by the wind turbine and the state of charge of the supercapacitor, both sampled simultaneously. After a computation process, the proposal generates active power set-point values that the supercapacitor must produce to compensate for the intermittency of the wind, seen from the point of connection to the grid. The results were validated experimentally with comprehensive laboratory tests.
Optimal energy management strategies to reduce diesel consumption for a hybrid off-grid system
(2020) Benavides Padilla, Darío Javier; Espinoza Abad, Juan Leonardo; Jurado Melguizo, Francisco; Arévalo Cordero, Wilian Paul
Although climate change is a reality, many off-grid communities continue to use diesel generators for electricity supply. This document presents a strategy to reduce diesel consumption in an out-of-grid system formed by renewable sources (PV-HKT-WT-DG). Three energy dispatch strategies have been proposed to verify the impact on diesel consumption and generator operating hours. In addition, different energy storage technologies (acid lead, lithium-ion, vanadium redox flow, pump storage and supercapacitor) have been considered. The HOMER software has been used to calculate the optimal size of the systems through technical-economic indicators. The results show that it is possible to reduce diesel consumption progressively; however, the cost of energy increases. On the other hand, when using lithium-ion batteries under charge cycle control, the penetration of the diesel generator has been greatly reduced without affecting the cost of the system. Finally, sensitivity analyzes have shown that when demand increases, diesel consumption does not increase significantly by using redox vanadium flow batteries, whereas the diesel generator operating hours decrease significantly in all systems.
Optimal design and energy management for a grid connected renewable hybrid system (PV-HKT-GRID)
(EDP Sciences, 2020) Benavides Padilla, Darío Javier; Espinoza Abad, Juan Leonardo; Jurado, Francisco; Arévalo Cordero, Wilian Paul
The integration of renewable energy is transcendental for sustainable development. This article analyses a hybrid grid-connected system composed of renewable energy technologies (photovoltaichydrokinetic), where several scenarios for energy management are proposed. They include a battery system as energy storage and a system without storage but with resale fee to grid, with the aim of determining the best economic and environmental balance. The results show that, by having a (PV-HKT-GRID) system with energy storage and no resale fee to the grid, the Net Present Cost (NPC) is increased by USD $ 132, 760 and the Cost of Energy (COE) decreases $ 0.013/kWh when compared to the grid. In addition, the same hybrid system without energy storage and no resale fee to grid, presents an energy cost savings of $ 0.043/kWh, and an additional cost of USD $ 43, 630. Finally, if a grid resale rate is included in the renewable hybrid system, then the difference is noticeable, the savings in the Cost of Energy is $ 0.073/kWh and presents a saving in the NPC of USD $ 39, 930. In all cases, CO2 emissions have been avoided.
Energy analysis and techno-economic assessment of a hybrid PV/HKT/BAT system using biomass gasifier: Cuenca-Ecuador case study
(2020) Cano Ortega, Antonio; Jurado Melguizo, Francisco; Arévalo Cordero, Wilian Paul
This paper analyzes the impact on an off-grid renewable hybrid system composed of photovoltaic energy, hydrokinetic turbines, batteries and biomass gasifiers, using various types of biomass in order to determine the optimal configuration of the system located in southern Ecuador. Three types of energy dispatch, charge cycle, load following and combined cycle have been proposed with the objective of determining new patterns on the behavior of sources with respect to electric demand. The biomass used as an energy resource produces electricity through a biomass gasifier that feeds a microturbine. Considering the types of biomass consumed by the gasifier, the items such as net present cost and cost of energy have been analyzed for the different types of control. Sensitivity studies indicate the increase in the cost of the system by increasing the minimum state of charge in the batteries. However, this increase reduces biomass consumption and CO2 emissions. Finally, the variation of the cost in the components influences the total cost of the system, being the fuel and the photovoltaic system the systems that have the highest sensitivity, the results have shown that the renewable system is able to supply the demand without violating any norm.
Comparative study of two new energy control systems based on PEMFC for a hybrid tramway in Ecuador
(2020) Cano Ortega, Antonio; Jurado, Francisco; Arévalo Cordero, Wilian Paul
This article presents a comparison of two alternative systems to supply the traction power of a tramway in Cuenca–Ecuador. Each system studies the effective combination of supercapacitors, lithium ion batteries and proton exchange membrane fuel cells (SC/LIB/PEMFC) on board. The first system uses renewable sources (PV/HKT/GB/Grid) supplying the on-board systems through the existing grid and hydrogen charging stations. While the second system uses only grid power from a single point of charge, leaving the tramway without any external connection point throughout their journey. The energy and economic analyses are based on the capacity of each system to supply the load and the resources used. The results show that the new proposed control systems, by means of the analysed configurations made up of different control states, are always capable of perfectly supplying the power required by the tramway throughout their journey. However, when using energy from renewable sources, hydrogen consumption decreases by 4.27% with respect to the grid on each round trip, with a lower net present cost. Furthermore, in the first proposed system, the depth of discharge in SC and LIB is greater.
Comparative analysis of HESS (battery/supercapacitor) for power smoothing of PV/HKT, simulation and experimental analysis
(2022) Cano Ortega, Antonio ; Jurado Melguizo, Francisco; Benavides Padilla, Darío Javier; Arévalo Cordero, Wilian Paul
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.
Large-scale integration of renewable energies by 2050 through demand prediction with ANFIS, Ecuador case study
(2024) Arévalo Cordero, Wilian Paul
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.
Innovative Power Smoothing Technique for Enhancing Renewable Integration in Insular Power Systems Using Electric Vehicle Charging Stations
(2024) Villa Ávila, Edisson Andrés; Ochoa Correa, Danny Vinicio; Arévalo Cordero, Wilian Paul
The reliance on imported fuels for electricity generation and internal transportation in insular electrical systems has historically posed a significant challenge due to their geographic isolation. The vulnerability of insular ecosystems to pollution has driven the need to transition toward renewable energy sources. Despite their inherent variability, wind and solar energy have gained acceptance. Integrating these renewable technologies into insular grids presents technical challenges that impact the quality of the power supply, particularly with the increasing presence of electric vehicles. Nevertheless, the batteries of these vehicles provide an opportunity to enhance network performance. This article introduces an innovative power smoothing technique that utilizes electric vehicle batteries to optimize self-consumption and reduce power fluctuations. The proposed method is an enhanced version of the ramp-rate energy smoothing method, incorporating adaptability through real-time control of the ramp-rate using fuzzy logic. It employs an aggregated model of lithium-ion batteries with a bidirectional power electronic converter. Experimental validation is carried out in the Micro-Grid Laboratory of the University of Cuenca, Ecuador. Experimental results demonstrate a significant 14% reduction in energy generation variability, resulting in a more stable electrical supply profile. Additionally, there is a marginal improvement in energy delivery, with an additional injection of 0.23 kWh compared to scenarios without the participation of electric vehicle batteries in power smoothing tasks. These findings support the effectiveness of the proposed approach in optimizing the integration of intermittent renewable generators and electric vehicle charging in insular energy systems.
A Systematic Review on the Integration of Artificial Intelligence into Energy Management Systems for Electric Vehicles: Recent Advances and Future Perspectives
(2024) Villa Ávila, Edisson Andrés; Ochoa Correa, Danny Vinicio; Arévalo Cordero, Wilian Paul
This systematic review paper examines the current integration of artificial intelligence into energy management systems for electric vehicles. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, 46 highly relevant articles were systematically identified from extensive literature research. Recent advancements in artificial intelligence, including machine learning, deep learning, and genetic algorithms, have been analyzed for their impact on improving electric vehicle performance, energy efficiency, and range. This study highlights significant advancements in energy management optimization, route planning, energy demand forecasting, and real-time adaptation to driving conditions through advanced control algorithms. Additionally, this paper explores artificial intelligence’s role in diagnosing faults, predictive maintenance of electric propulsion systems and batteries, and personalized driving experiences based on driver preferences and environmental factors. Furthermore, the integration of artificial intelligence into addressing security and cybersecurity threats in electric vehicles’ energy management systems is discussed. The findings underscore artificial intelligence’s potential to foster innovation and efficiency in sustainable mobility, emphasizing the need for further research to overcome current challenges and optimize practical applications.