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, Dario 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.
Smart monitoring method for photovoltaic systems and failure control based on power smoothing techniques
(2023) Benavides Padilla, Dario Javier ; Arévalo Cordero, Wilian Paul
In recent years, photovoltaic energy production has experienced significant progress, being integrated into the grid through large-scale distributed systems. The intermittent nature of solar irradiance coupled with the presence of photovoltaic failures causes fluctuations that could compromise the quality and stability of electrical grid. This paper presents a novel photovoltaic power smoothing method in a combination with moving averages and ramp rate to reduce fluctuations with hybrid storage systems (supercapacitors/batteries), the main novelty involves optimizing the number of charging/discharging cycles under PV failures. To achieve this goal, a photovoltaic failure detection method is proposed that uses machine learning to process big data by monitoring the behavior of photovoltaic. The experiments have been done under controlled conditions in the microgrid laboratory of the University of Cuenca. The results show the reduction of the supercapacitor operation with respect to other power smoothing methods. Moreover, the monitoring system is capable of detecting a failure in photovoltaic systems with a root mean squared error of 0.66 and the computational effort is reduced using the new smoothing technique. In this sense, the initial execution time is 4 times lower compared to the moving average method.
Intrinsic Characteristics of Forward Simulation Modeling Electric Vehicle for Energy Analysis
(2022) Arévalo Cordero, Wilian Paul
The forward method for modeling electric vehicles is one of the most suitable for estimating energy consumption in different imposed driving cycles. However, a detailed description of the methodology used for the development of electric vehicle models is necessary and is scarce in the current literature. To fill this gap, this study focuses on highlighting the intrinsic characteristics through a theoretical study with a mathematical model, complemented by demonstrative simulations in Matlab/Simulink. The results show that the forward method can be estimated more accurately based on the energy consumption of the electric vehicle. Moreover, this paper aims to be explicitly descriptive for the development of more complex electric vehicle models to incorporate real driving cycles, being able to size the drivetrain of the vehicle itself or develop ecological routes.
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.
A New Adaptive Strategy for Enhancing the Stability of Isolated Grids through the Integration of Renewable Energy and V2G Management
(2024) Villa Ávila, Edisson Andrés; Ochoa Correa, Danny Vinicio; Iñiguez Morán, Vinicio Estuardo; Arévalo Cordero, Wilian Paul
The integration of renewable energy sources into isolated microgrids introduces significant power fluctuations due to their intermittent nature. This study addresses the need for advanced power smoothing methods to enhance the stability of isolated networks. An innovative adaptive strategy is presented, combining photovoltaic solar generation with vehicle-to-grid technology, utilizing an enhanced adaptive moving average filter with fuzzy logic control. The primary objective is to dynamically optimize the time frame of the Li-ion battery energy storage system for immediate power stabilization, leveraging the high energy density and rapid response capabilities inherent in electric vehicle batteries. The methodology encompasses data acquisition from photovoltaic panels, definition of fuzzy logic control rules, and implementation of the proposed method within a computer-controlled system connected to a bidirectional three-phase inverter. Experimental results highlight the proposed method’s superiority over conventional moving averages and ramp-rate filters.
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.
Analysis of different energy storage technologies for microgrids energy management
(EDP Sciences, 2020) Gonzalez Morales, Luis Gerardo; Benavides Padilla, Dario Javier; Aguado Sánchez, José Antonio; Arévalo Cordero, Wilian Paul
The importance of energy storage systems is increasing in microgrids energy management. In this study, an analysis is carried out for different types of energy storage technologies commonly used in the energy storage systems of a microgrid, such as: lead acid batteries, lithium ion batteries, redox vanadium flux batteries and supercapacitors. In this work, it is analyzed the process of charging and discharging (slow and fast) in these systems, the calculation of energy efficiency, performance and energy supplied under different load levels, in its normal operating conditions and installed power capacity is developed. The results allow us to choose the optimal conditions of charge and discharge at different levels of reference power, analyzing the strengths and weaknesses of the characteristics of each storage system within a microgrid.
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.
A novel experimental method of power smoothing using supercapacitors and hydrogen for hybrid system PV/HKT
(2023) Arévalo Cordero, Wilian Paul
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. © 2023 Elsevier Ltd
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.