Departamento de Ingeniería Eléctrica

URI permanente para esta comunidadhttps://hdl.handle.net/10953/37

En esta Comunidad se recogen los documentos generados por el Departamento de Ingeniería Eléctrica y que cumplen los requisitos de Copyright para su difusión en acceso abierto.

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An Experimental Study of Power Smoothing Methods to Reduce Renewable Sources Fluctuations Using Supercapacitors and Lithium-Ion Batteries
(MDPI, 2022-11-09) Benavides, Darío; Arévalo, Paul; Tostado-Véliz, Marcos; Vera, David; Escámez, Antonio; Aguado-Sánchez, José Antonio; Jurado-Melguizo, Francisco
The random nature of renewable sources causes power fluctuations affecting the stability in the utility grid. This problem has motivated the development of new power smoothing techniques using supercapacitors and batteries. However, experimental studies based on multiple renewable sources (photovoltaic, wind, hydrokinetic) that demonstrate the validity of power smoothing techniques under real conditions still require further study. For this reason, this article presents a feasibility study of a renewable grid-connected system, addressing various aspects based on power quality and energy management. The first of them is related to the fluctuations produced by the stochastic characteristics of renewable sources and demand. Two power smoothing algorithms are presented (ramp rate and moving average) combining photovoltaic, wind, and hydrokinetic sources with a hybrid storage system composed of supercapacitors and lithium-ion batteries. Then, the self-consumption for an industrial load is analyzed by studying the energy flows between the hybrid renewable energy sources and the grid. The main novelty of this paper is the operability of the supercapacitor. The experimental results show that when applying the power smoothing ramp rate method, the supercapacitor operates fewer cycles with respect to the moving average method. This result is maintained by varying the capacity of the renewable sources. Moreover, by increasing the capacity of photovoltaic and wind renewable sources, the hybrid storage system requires a greater capacity only of supercapacitors, while by increasing the capacity of hydrokinetic turbines, the battery requirement increases considerably. Finally, the cost of energy and self-consumption reach maximum values by increasing the capacity of the hydrokinetic turbines and batteries.
An IoT-enabled hierarchical decentralized framework for multi-energy microgrids market management in the presence of smart prosumers using a deep learning-based forecaster
(Elsevier, 2023-03-01) Mansouri, Seyed Amir; Jordehi, Ahmad Rezaee; Marzband, Mousa; Tostado-Véliz, Marcos; Jurado-Melguizo, Francisco; Aguado-Sánchez, José Antonio
The integrated exploitation of different energy infrastructures in the form of multi-energy systems (MESs) and the transformation of traditional prosumers into smart prosumers are two effective pathways to achieve net-zero emission energy systems in the near future. Managing different energy markets is one of the biggest challenges for the operators of MESs, since different carriers are traded in them simultaneously. Hence, this paper presents a hierarchical decentralized framework for the simultaneous management of electricity, heat and hydrogen markets among multi-energy microgrids (MEMGs) integrated with smart prosumers. The market strategy of MEMGs is deployed using a hierarchical framework and considering the programs requested by smart prosumers. A deep learning-based forecaster is utilized to predict uncertain parameters while a risk-averse information gap decision theory (IGDT)-based strategy controls the scheduling risk. A new prediction-based mechanism for designing dynamic demand response (DR) schemes compatible with smart prosumers’ behavior is introduced, and the results illustrate that this mechanism reduces the electricity and heat clearing prices in peak hours by 17.5% and 8.78%, respectively. Moreover, the results reveal that the introduced structure for hydrogen exchange through the transportation system has the ability to be implemented in competitive markets. Overall, the simulation results confirm that the proposed hierarchical model is able to optimally manage the competitive markets of electricity, heat and hydrogen by taking advantage of the potential of smart prosumers.
Smart monitoring method for photovoltaic systems and failure control based on power smoothing techniques
(Elsevier, 2023-03) Arévalo, Paul; Benavides, Darío; Tostado-Véliz, Marcos; Aguado-Sánchez, José Antonio; Jurado-Melguizo, Francisco
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.

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Examinando Departamento de Ingeniería Eléctrica por Autor "Aguado-Sánchez, José Antonio"