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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Kelly Joel Gurubel-Tun; José A. Domínguez-Navarro; Marco Pérez-Cisneros; Virgilio Zúñiga-Grajeda; +2 Authors

    Abstract Photovoltaic systems, wind turbines and battery storage systems are all commonly present in energy self-sufficiency and independent producers schemes, since they support distributed generation designs. For both cases, single phase inverters provide power conditioning in order to inject micro-sources power to the grid, demanding multiple tasks such as maximum power tracking, DC to AC voltage conversion, electrical signal filtering and network synchronization, among others. The massive integration of such devices demands an assessment on their impact over the low voltage distribution network to which they are connected to. Hence, an accurate and efficient mathematical modeling is required, for both steady and transient state, in order to provide a robust dynamical simulation to support the designing of coordinated protection schemes and operational control algorithms. The analysis also contributes to ensure network stability within the required quality issues. Therefore, this paper extends the dynamic phasors technique that is a widely employed method for modeling oscillatory systems, in order to develop, simulate and analyze the mathematical model of single phase full bridge inverter. A comparison between different types of numeric methods is made, to find the better option depending of the finality of the study. The technique focuses on three frequencies of interest: the network rate, the boost and the inverter stage frequencies. Based on dynamic phasors information, two PI controls are designed to control the DC an AC voltages, and new formulas for calculation of DC and AC powers are presented. Simulation results in Matlab and Simulink demonstrate the effectiveness of the work.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Electric Power Syste...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Electric Power Systems Research
    Article . 2016 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Electric Power Syste...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Electric Power Systems Research
      Article . 2016 . Peer-reviewed
      License: Elsevier TDM
      Data sources: Crossref
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Kelly Joel Gurubel-Tun; José A. Domínguez-Navarro; Marco Pérez-Cisneros; Virgilio Zúñiga-Grajeda; +2 Authors

    Abstract Photovoltaic systems, wind turbines and battery storage systems are all commonly present in energy self-sufficiency and independent producers schemes, since they support distributed generation designs. For both cases, single phase inverters provide power conditioning in order to inject micro-sources power to the grid, demanding multiple tasks such as maximum power tracking, DC to AC voltage conversion, electrical signal filtering and network synchronization, among others. The massive integration of such devices demands an assessment on their impact over the low voltage distribution network to which they are connected to. Hence, an accurate and efficient mathematical modeling is required, for both steady and transient state, in order to provide a robust dynamical simulation to support the designing of coordinated protection schemes and operational control algorithms. The analysis also contributes to ensure network stability within the required quality issues. Therefore, this paper extends the dynamic phasors technique that is a widely employed method for modeling oscillatory systems, in order to develop, simulate and analyze the mathematical model of single phase full bridge inverter. A comparison between different types of numeric methods is made, to find the better option depending of the finality of the study. The technique focuses on three frequencies of interest: the network rate, the boost and the inverter stage frequencies. Based on dynamic phasors information, two PI controls are designed to control the DC an AC voltages, and new formulas for calculation of DC and AC powers are presented. Simulation results in Matlab and Simulink demonstrate the effectiveness of the work.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Electric Power Syste...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Electric Power Systems Research
    Article . 2016 . Peer-reviewed
    License: Elsevier TDM
    Data sources: Crossref
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    12
    citations12
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Electric Power Syste...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Electric Power Systems Research
      Article . 2016 . Peer-reviewed
      License: Elsevier TDM
      Data sources: Crossref
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: L.F. Gonzalez Gabriel; R. Ruiz-Cruz; H.J. Coss y Leon Monterde; V. Zúñiga-Grajeda; +2 Authors

    In this work, we demonstrate the importance of implementing techniques that allow us to include demand side management as a tool for the planning of energy systems, in this particular case, standalone power systems. Penetration indexes are also proposed and calculated to establish the minimum requirements for the energy supply of a predominantly residential system powered by renewable resources. The indexes were optimized using meta-heuristic optimization techniques based on a genetic algorithm and particle swarm optimization. Periods of one, five and 10 years were analyzed in order to understand the importance of the penetration of these technologies. Through the use of numerical tools, the relationship between generation and demand is optimized for different cases, with the aim of reducing the energy that is not supplied to the system at minimum cost. This document analyzes a series of penetration indexes that were obtained by optimizing an energy system. The mentioned indexes allow to visualize the behavior of the technologies susceptible of being implemented in the western region of Mexico. The purpose of the work is focused on understanding the potential of demand management as a structural element and foundation of energy networks that use renewable energy.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energy Reportsarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energy Reports
    Article . 2022 . Peer-reviewed
    License: CC BY NC ND
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energy Reports
    Article
    License: CC BY NC ND
    Data sources: UnpayWall
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energy Reports
    Article . 2022
    Data sources: DOAJ
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    11
    citations11
    popularityTop 10%
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energy Reportsarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Energy Reports
      Article . 2022 . Peer-reviewed
      License: CC BY NC ND
      Data sources: Crossref
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Energy Reports
      Article
      License: CC BY NC ND
      Data sources: UnpayWall
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Energy Reports
      Article . 2022
      Data sources: DOAJ
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: L.F. Gonzalez Gabriel; R. Ruiz-Cruz; H.J. Coss y Leon Monterde; V. Zúñiga-Grajeda; +2 Authors

    In this work, we demonstrate the importance of implementing techniques that allow us to include demand side management as a tool for the planning of energy systems, in this particular case, standalone power systems. Penetration indexes are also proposed and calculated to establish the minimum requirements for the energy supply of a predominantly residential system powered by renewable resources. The indexes were optimized using meta-heuristic optimization techniques based on a genetic algorithm and particle swarm optimization. Periods of one, five and 10 years were analyzed in order to understand the importance of the penetration of these technologies. Through the use of numerical tools, the relationship between generation and demand is optimized for different cases, with the aim of reducing the energy that is not supplied to the system at minimum cost. This document analyzes a series of penetration indexes that were obtained by optimizing an energy system. The mentioned indexes allow to visualize the behavior of the technologies susceptible of being implemented in the western region of Mexico. The purpose of the work is focused on understanding the potential of demand management as a structural element and foundation of energy networks that use renewable energy.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energy Reportsarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energy Reports
    Article . 2022 . Peer-reviewed
    License: CC BY NC ND
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energy Reports
    Article
    License: CC BY NC ND
    Data sources: UnpayWall
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energy Reports
    Article . 2022
    Data sources: DOAJ
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energy Reportsarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Energy Reports
      Article . 2022 . Peer-reviewed
      License: CC BY NC ND
      Data sources: Crossref
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Energy Reports
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Energy Reports
      Article . 2022
      Data sources: DOAJ
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mario Villegas-Ruvalcaba; Kelly Gurubel-Tun; Alberto Coronado-Mendoza;

    The variability of renewable energies and their integration into the grid via power electronics demands the design of robust control algorithms. This work incorporates two techniques to ensure the stability of a boost converter through its state equations, implementing the inverse optimal control and the gain-scheduling technique for robust control settings. In such a way that, under a single adjustment, it is capable of damping different changes such as changes in the parameters, changes in the load, the input voltage, and the reference voltage. On the other hand, inverse optimal control is based on a discrete-time control Lyapunov function (CLF), and CLF candidate depends on fixed parameters that are selected to obtain the solution for inverse optimal control. Once these parameters have been found through heuristic or artificial intelligence methods, the new proposed methodology is capable of obtaining a robust optimal control scheme, without having to search for new parameters through other methods, since these are sometimes sensitive changes and many times the process of a new search is delayed. The results of the approach are simulated using Matlab, obtaining good performance of the proposed control under different operation conditions. Such simulations yielded errors of less than 1% based on the voltage reference, given the disturbances caused by changes in the input variables, system parameters, and changes in the reference. Thus, applying the new methodology, the stability of our system was preserved in all cases.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energies
    Article . 2021 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energies
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    Energies
    Article . 2021
    Data sources: DOAJ
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
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      Energies
      Article . 2021 . Peer-reviewed
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      Energies
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      Energies
      Article . 2021
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mario Villegas-Ruvalcaba; Kelly Gurubel-Tun; Alberto Coronado-Mendoza;

    The variability of renewable energies and their integration into the grid via power electronics demands the design of robust control algorithms. This work incorporates two techniques to ensure the stability of a boost converter through its state equations, implementing the inverse optimal control and the gain-scheduling technique for robust control settings. In such a way that, under a single adjustment, it is capable of damping different changes such as changes in the parameters, changes in the load, the input voltage, and the reference voltage. On the other hand, inverse optimal control is based on a discrete-time control Lyapunov function (CLF), and CLF candidate depends on fixed parameters that are selected to obtain the solution for inverse optimal control. Once these parameters have been found through heuristic or artificial intelligence methods, the new proposed methodology is capable of obtaining a robust optimal control scheme, without having to search for new parameters through other methods, since these are sometimes sensitive changes and many times the process of a new search is delayed. The results of the approach are simulated using Matlab, obtaining good performance of the proposed control under different operation conditions. Such simulations yielded errors of less than 1% based on the voltage reference, given the disturbances caused by changes in the input variables, system parameters, and changes in the reference. Thus, applying the new methodology, the stability of our system was preserved in all cases.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
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    Energies
    Article . 2021 . Peer-reviewed
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      Energies
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      Energies
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    Authors: Iván Carrillo-Gómez; Alberto Coronado-Mendoza; Luis Fernando González-Gabriel;

    This paper shows the importance acquired by specialized software for the design of energy systems that operate under a scheme outside the electricity grid and incorporate Renewable Energy. The relevance of this work is based on the understanding that one of the objectives of current governments is to improve the quality of life of people and their marginality index, through access to electricity service. A methodology is proposed for the sizing of an isolated hybrid system using HOMER Pro, a software of optimization of power systems that facilitates the technical and economic evaluation of the system. It started with the elaboration of a demand profile for a rural community located in the municipality of Mezquitic in the North of the state of Jalisco based on an international review, which allows to improve its Human Development Index; and the selection of the different energy generation and storage components that make up the system. The result of the simulations allows us to build a table of results, which facilitates the selection of equipment because it allows to observe the different combinations of technical, economic and meteorological variables and economically viable.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Revista de Ingenierí...arrow_drop_down
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    Revista de Ingeniería Eléctrica
    Article . 2019 . Peer-reviewed
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      Revista de Ingeniería Eléctrica
      Article . 2019 . Peer-reviewed
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    Authors: Iván Carrillo-Gómez; Alberto Coronado-Mendoza; Luis Fernando González-Gabriel;

    This paper shows the importance acquired by specialized software for the design of energy systems that operate under a scheme outside the electricity grid and incorporate Renewable Energy. The relevance of this work is based on the understanding that one of the objectives of current governments is to improve the quality of life of people and their marginality index, through access to electricity service. A methodology is proposed for the sizing of an isolated hybrid system using HOMER Pro, a software of optimization of power systems that facilitates the technical and economic evaluation of the system. It started with the elaboration of a demand profile for a rural community located in the municipality of Mezquitic in the North of the state of Jalisco based on an international review, which allows to improve its Human Development Index; and the selection of the different energy generation and storage components that make up the system. The result of the simulations allows us to build a table of results, which facilitates the selection of equipment because it allows to observe the different combinations of technical, economic and meteorological variables and economically viable.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Revista de Ingenierí...arrow_drop_down
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    Revista de Ingeniería Eléctrica
    Article . 2019 . Peer-reviewed
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      Revista de Ingeniería Eléctrica
      Article . 2019 . Peer-reviewed
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    Authors: Mónica Camas-Náfate; Alberto Coronado-Mendoza; Carlos Vargas-Salgado; Jesús Águila-León; +1 Authors

    In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 °C; 3.6 C, 455 s) and 0.1705 (25 °C; 3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(nk), while GWO implied a potential polynomial complexity within the range of O(nk) or O(2n) based on execution times from populations of 10 to 1000.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
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    Energies
    Article . 2024 . Peer-reviewed
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    Energies
    Article . 2024
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    RiuNet
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      Article . 2024 . Peer-reviewed
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      Article . 2024
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    Authors: Mónica Camas-Náfate; Alberto Coronado-Mendoza; Carlos Vargas-Salgado; Jesús Águila-León; +1 Authors

    In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 °C; 3.6 C, 455 s) and 0.1705 (25 °C; 3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(nk), while GWO implied a potential polynomial complexity within the range of O(nk) or O(2n) based on execution times from populations of 10 to 1000.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
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    Energies
    Article . 2024 . Peer-reviewed
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    Article . 2024
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    RiuNet
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      Energies
      Article . 2024 . Peer-reviewed
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      Energies
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    Authors: Alberto Coronado-Mendoza; Mónica Camas-Náfate; Jesús Sergio Artal-Sevil; José Antonio Domínguez-Navarro;

    The deployment of photovoltaic single-phase inverters has been rapidly increasing worldwide. However, the performance of these systems is highly influenced by atmospheric conditions and load variations, necessitating the development of performance indices to enhance their efficiency and energy quality. In this study, four performance indices are proposed to evaluate the efficiency and energy quality of photovoltaic systems quantitatively. The entire process is analyzed, encompassing solar energy capture, DC-DC and DC-AC conversion, and filtering, to deliver maximum energy and quality to the load. Furthermore, eight system parameters are optimized using advanced techniques such as genetic algorithms, particle swarm optimization, and gray wolf optimization. These optimizations enhance the global performance of two critical stages: (1) the maximum power point tracking algorithm based on sliding mode control, which minimizes switching losses in the boost stage, and (2) the effective transfer of captured solar power to the load by optimizing the gains of a PI controller. The PI controller computes the switching triggers for the inverter stage, significantly improving the total harmonic distortion of voltage and current waveforms. Simulation results validate the proposed approach, demonstrating a marked improvement in overall system efficiency (95.8%) when compared to the incremental conductance method (−11.8%) and a baseline sliding mode control configuration (−1.14%).

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    Authors: Alberto Coronado-Mendoza; Mónica Camas-Náfate; Jesús Sergio Artal-Sevil; José Antonio Domínguez-Navarro;

    The deployment of photovoltaic single-phase inverters has been rapidly increasing worldwide. However, the performance of these systems is highly influenced by atmospheric conditions and load variations, necessitating the development of performance indices to enhance their efficiency and energy quality. In this study, four performance indices are proposed to evaluate the efficiency and energy quality of photovoltaic systems quantitatively. The entire process is analyzed, encompassing solar energy capture, DC-DC and DC-AC conversion, and filtering, to deliver maximum energy and quality to the load. Furthermore, eight system parameters are optimized using advanced techniques such as genetic algorithms, particle swarm optimization, and gray wolf optimization. These optimizations enhance the global performance of two critical stages: (1) the maximum power point tracking algorithm based on sliding mode control, which minimizes switching losses in the boost stage, and (2) the effective transfer of captured solar power to the load by optimizing the gains of a PI controller. The PI controller computes the switching triggers for the inverter stage, significantly improving the total harmonic distortion of voltage and current waveforms. Simulation results validate the proposed approach, demonstrating a marked improvement in overall system efficiency (95.8%) when compared to the incremental conductance method (−11.8%) and a baseline sliding mode control configuration (−1.14%).

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  • Authors: K. J. Gurubel; V. Osuna-Enciso; J. J. Cardenas; A. Coronado-Mendoza; +2 Authors

    Energy systems with renewable sources are used around the world in order to satisfy both off-grid and on-grid load demands, and are commonly coupled to conventional sources. A good behavior of this kind of systems depends on the renewable sources availability that includes the solar irradiance and the wind speed, as well as the profile variations over the energy demand. Their main objective is to satisfy the load demand while minimizing the use of conventional sources, reducing pollutant emissions and storing the energy excess for deficit conditions. This paper presents modeling, neural forecasting and optimal sizing for hybrid energy systems, which are proposed to minimize both the overall annual cost and the use of conventional sources, which in turn represents reduction of pollutant emissions. In this paper, the use of renewable sources along with load demand variations are predicted by a High Order Neural Network trained with an Extended Kalman Filter, whereas the optimal sizing is calculated by using both a Clonal Selection Algorithm and a Genetic Algorithm. The efficiency of using neural forecasting data is illustrated through a simulation with the results showing the effectiveness of both optimization algorithms for calculating an optimal sizing of the hybrid system, which ultimately represents an optimal cost-effective system.

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  • Authors: K. J. Gurubel; V. Osuna-Enciso; J. J. Cardenas; A. Coronado-Mendoza; +2 Authors

    Energy systems with renewable sources are used around the world in order to satisfy both off-grid and on-grid load demands, and are commonly coupled to conventional sources. A good behavior of this kind of systems depends on the renewable sources availability that includes the solar irradiance and the wind speed, as well as the profile variations over the energy demand. Their main objective is to satisfy the load demand while minimizing the use of conventional sources, reducing pollutant emissions and storing the energy excess for deficit conditions. This paper presents modeling, neural forecasting and optimal sizing for hybrid energy systems, which are proposed to minimize both the overall annual cost and the use of conventional sources, which in turn represents reduction of pollutant emissions. In this paper, the use of renewable sources along with load demand variations are predicted by a High Order Neural Network trained with an Extended Kalman Filter, whereas the optimal sizing is calculated by using both a Clonal Selection Algorithm and a Genetic Algorithm. The efficiency of using neural forecasting data is illustrated through a simulation with the results showing the effectiveness of both optimization algorithms for calculating an optimal sizing of the hybrid system, which ultimately represents an optimal cost-effective system.

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  • Authors: Alberto Coronado-Mendoza; Angel A. Bayod-Rújula; Jose M. Yusta; José A. Domínguez-Navarro; +3 Authors

    An energy management system to minimize the operation cost of a microgrid is presented in this work. The microgrid is composed of the technologies more used as renewable energies and storage systems, as well as active consumers which demand depends on price. The proposed model is based on linear programming with the operating cost as the objective function and the operating limits as constraints. The model has two phases. The first phase does the optimal dispatch of generating units in function of the forecasting demand and the offers made by generators. The second phase minimizes the differences between the planning values obtained in the first phase and the real data, because there are differences as consequence of the uncertainties of renewable energies and the consumers’ behaviour.

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  • Authors: Alberto Coronado-Mendoza; Angel A. Bayod-Rújula; Jose M. Yusta; José A. Domínguez-Navarro; +3 Authors

    An energy management system to minimize the operation cost of a microgrid is presented in this work. The microgrid is composed of the technologies more used as renewable energies and storage systems, as well as active consumers which demand depends on price. The proposed model is based on linear programming with the operating cost as the objective function and the operating limits as constraints. The model has two phases. The first phase does the optimal dispatch of generating units in function of the forecasting demand and the offers made by generators. The second phase minimizes the differences between the planning values obtained in the first phase and the real data, because there are differences as consequence of the uncertainties of renewable energies and the consumers’ behaviour.

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  • Authors: K. J. Gurubel; V. Osuna-Enciso; A. Coronado-Mendoza; E. Cuevas;

    In the renewable energy generation, several processes require the integration of a set of advanced techniques in order to find optimal solutions. Dynamic estimation, stabilizing control for disturbance rejection, optimization for control effort, and parameter tuning are techniques used to address the whole process requirements and obtain optimal results. In this paper, an optimal control strategy for a maximum biofuel production in the presence of disturbances is proposed. First, an integrated optimal control strategy to maximize biofuel production in the presence of disturbances is proposed. Second, due to its high nonlinearity, complex nature, and multiplicity of equilibrium points, a biological process for biofuel generation is described in order to demonstrate the efficiency of the optimal control strategy. A nonlinear discrete-time neural observer for unknown nonlinear systems in the presence of external disturbances and parameter uncertainties is used to estimate unmeasurable variables. An inverse optimal control law for trajectory tracking based on the neural observer is designed such that asymptotic convergence reference trajectory is guaranteed. Differential Evolution and Clonal Selection Algorithms are used to calculate the optimal parameters for neural network training, neural network gains, and feedback control gains. Additionally, a supervisory fuzzy control is proposed in order to select the adequate control action between the closed loop and the open loop and to determine optimal reference trajectories. Simulation results comparison and statistical validation are presented, where it is demonstrated that the optimal control strategy integrated with the Differential Evolution algorithm gives better results to maximize the biofuel production in the presence of disturbances.

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  • Authors: K. J. Gurubel; V. Osuna-Enciso; A. Coronado-Mendoza; E. Cuevas;

    In the renewable energy generation, several processes require the integration of a set of advanced techniques in order to find optimal solutions. Dynamic estimation, stabilizing control for disturbance rejection, optimization for control effort, and parameter tuning are techniques used to address the whole process requirements and obtain optimal results. In this paper, an optimal control strategy for a maximum biofuel production in the presence of disturbances is proposed. First, an integrated optimal control strategy to maximize biofuel production in the presence of disturbances is proposed. Second, due to its high nonlinearity, complex nature, and multiplicity of equilibrium points, a biological process for biofuel generation is described in order to demonstrate the efficiency of the optimal control strategy. A nonlinear discrete-time neural observer for unknown nonlinear systems in the presence of external disturbances and parameter uncertainties is used to estimate unmeasurable variables. An inverse optimal control law for trajectory tracking based on the neural observer is designed such that asymptotic convergence reference trajectory is guaranteed. Differential Evolution and Clonal Selection Algorithms are used to calculate the optimal parameters for neural network training, neural network gains, and feedback control gains. Additionally, a supervisory fuzzy control is proposed in order to select the adequate control action between the closed loop and the open loop and to determine optimal reference trajectories. Simulation results comparison and statistical validation are presented, where it is demonstrated that the optimal control strategy integrated with the Differential Evolution algorithm gives better results to maximize the biofuel production in the presence of disturbances.

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    Authors: Mónica Camas-Náfate; Alberto Coronado-Mendoza; Carlos Jesahel Vega-Gómez; Francisco Espinosa-Moreno;

    The analysis of the behavior of lithium-ion batteries has gained considerable interest in recent years. There are different alternatives for the analysis of this behavior; however, depending on the type of modeling, there are application and optimization restrictions. In this work, a hybrid model has been made that is capable of predicting the characteristics of a lithium-ion battery. As a novelty, the simplification, at the same time, facilitates the sampling of parameters for their prompt selection for optimization. A new model open to the user is proposed, which has proven to be efficient in simulation time. For example, one hour simulates it in 5 min, providing information detailing how these parameters, State of Health (SOH), Open Circuit Voltage (VOC), State of charge (SOC), and Number of charge and discharge cycles, in the face of temperature variations and charge and discharge cycles. Opening the possibility of optimizing the parameters with different techniques to estimate the performance and dynamics in the face of temperature change and charge and discharge cycles. A model based on linear regressions, manufacturer characteristics, and integrating equations in the electrical model of electrochemical phenomena is proposed.

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    Authors: Mónica Camas-Náfate; Alberto Coronado-Mendoza; Carlos Jesahel Vega-Gómez; Francisco Espinosa-Moreno;

    The analysis of the behavior of lithium-ion batteries has gained considerable interest in recent years. There are different alternatives for the analysis of this behavior; however, depending on the type of modeling, there are application and optimization restrictions. In this work, a hybrid model has been made that is capable of predicting the characteristics of a lithium-ion battery. As a novelty, the simplification, at the same time, facilitates the sampling of parameters for their prompt selection for optimization. A new model open to the user is proposed, which has proven to be efficient in simulation time. For example, one hour simulates it in 5 min, providing information detailing how these parameters, State of Health (SOH), Open Circuit Voltage (VOC), State of charge (SOC), and Number of charge and discharge cycles, in the face of temperature variations and charge and discharge cycles. Opening the possibility of optimizing the parameters with different techniques to estimate the performance and dynamics in the face of temperature change and charge and discharge cycles. A model based on linear regressions, manufacturer characteristics, and integrating equations in the electrical model of electrochemical phenomena is proposed.

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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Kelly Joel Gurubel-Tun; José A. Domínguez-Navarro; Marco Pérez-Cisneros; Virgilio Zúñiga-Grajeda; +2 Authors

    Abstract Photovoltaic systems, wind turbines and battery storage systems are all commonly present in energy self-sufficiency and independent producers schemes, since they support distributed generation designs. For both cases, single phase inverters provide power conditioning in order to inject micro-sources power to the grid, demanding multiple tasks such as maximum power tracking, DC to AC voltage conversion, electrical signal filtering and network synchronization, among others. The massive integration of such devices demands an assessment on their impact over the low voltage distribution network to which they are connected to. Hence, an accurate and efficient mathematical modeling is required, for both steady and transient state, in order to provide a robust dynamical simulation to support the designing of coordinated protection schemes and operational control algorithms. The analysis also contributes to ensure network stability within the required quality issues. Therefore, this paper extends the dynamic phasors technique that is a widely employed method for modeling oscillatory systems, in order to develop, simulate and analyze the mathematical model of single phase full bridge inverter. A comparison between different types of numeric methods is made, to find the better option depending of the finality of the study. The technique focuses on three frequencies of interest: the network rate, the boost and the inverter stage frequencies. Based on dynamic phasors information, two PI controls are designed to control the DC an AC voltages, and new formulas for calculation of DC and AC powers are presented. Simulation results in Matlab and Simulink demonstrate the effectiveness of the work.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Electric Power Syste...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Electric Power Systems Research
    Article . 2016 . Peer-reviewed
    License: Elsevier TDM
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Electric Power Syste...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Electric Power Systems Research
      Article . 2016 . Peer-reviewed
      License: Elsevier TDM
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  • image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Authors: Kelly Joel Gurubel-Tun; José A. Domínguez-Navarro; Marco Pérez-Cisneros; Virgilio Zúñiga-Grajeda; +2 Authors

    Abstract Photovoltaic systems, wind turbines and battery storage systems are all commonly present in energy self-sufficiency and independent producers schemes, since they support distributed generation designs. For both cases, single phase inverters provide power conditioning in order to inject micro-sources power to the grid, demanding multiple tasks such as maximum power tracking, DC to AC voltage conversion, electrical signal filtering and network synchronization, among others. The massive integration of such devices demands an assessment on their impact over the low voltage distribution network to which they are connected to. Hence, an accurate and efficient mathematical modeling is required, for both steady and transient state, in order to provide a robust dynamical simulation to support the designing of coordinated protection schemes and operational control algorithms. The analysis also contributes to ensure network stability within the required quality issues. Therefore, this paper extends the dynamic phasors technique that is a widely employed method for modeling oscillatory systems, in order to develop, simulate and analyze the mathematical model of single phase full bridge inverter. A comparison between different types of numeric methods is made, to find the better option depending of the finality of the study. The technique focuses on three frequencies of interest: the network rate, the boost and the inverter stage frequencies. Based on dynamic phasors information, two PI controls are designed to control the DC an AC voltages, and new formulas for calculation of DC and AC powers are presented. Simulation results in Matlab and Simulink demonstrate the effectiveness of the work.

    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Electric Power Syste...arrow_drop_down
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    Electric Power Systems Research
    Article . 2016 . Peer-reviewed
    License: Elsevier TDM
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Electric Power Syste...arrow_drop_down
      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      Electric Power Systems Research
      Article . 2016 . Peer-reviewed
      License: Elsevier TDM
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: L.F. Gonzalez Gabriel; R. Ruiz-Cruz; H.J. Coss y Leon Monterde; V. Zúñiga-Grajeda; +2 Authors

    In this work, we demonstrate the importance of implementing techniques that allow us to include demand side management as a tool for the planning of energy systems, in this particular case, standalone power systems. Penetration indexes are also proposed and calculated to establish the minimum requirements for the energy supply of a predominantly residential system powered by renewable resources. The indexes were optimized using meta-heuristic optimization techniques based on a genetic algorithm and particle swarm optimization. Periods of one, five and 10 years were analyzed in order to understand the importance of the penetration of these technologies. Through the use of numerical tools, the relationship between generation and demand is optimized for different cases, with the aim of reducing the energy that is not supplied to the system at minimum cost. This document analyzes a series of penetration indexes that were obtained by optimizing an energy system. The mentioned indexes allow to visualize the behavior of the technologies susceptible of being implemented in the western region of Mexico. The purpose of the work is focused on understanding the potential of demand management as a structural element and foundation of energy networks that use renewable energy.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energy Reportsarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energy Reports
    Article . 2022 . Peer-reviewed
    License: CC BY NC ND
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energy Reports
    Article
    License: CC BY NC ND
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    Energy Reports
    Article . 2022
    Data sources: DOAJ
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energy Reportsarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Energy Reports
      Article . 2022 . Peer-reviewed
      License: CC BY NC ND
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Energy Reports
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      Energy Reports
      Article . 2022
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: L.F. Gonzalez Gabriel; R. Ruiz-Cruz; H.J. Coss y Leon Monterde; V. Zúñiga-Grajeda; +2 Authors

    In this work, we demonstrate the importance of implementing techniques that allow us to include demand side management as a tool for the planning of energy systems, in this particular case, standalone power systems. Penetration indexes are also proposed and calculated to establish the minimum requirements for the energy supply of a predominantly residential system powered by renewable resources. The indexes were optimized using meta-heuristic optimization techniques based on a genetic algorithm and particle swarm optimization. Periods of one, five and 10 years were analyzed in order to understand the importance of the penetration of these technologies. Through the use of numerical tools, the relationship between generation and demand is optimized for different cases, with the aim of reducing the energy that is not supplied to the system at minimum cost. This document analyzes a series of penetration indexes that were obtained by optimizing an energy system. The mentioned indexes allow to visualize the behavior of the technologies susceptible of being implemented in the western region of Mexico. The purpose of the work is focused on understanding the potential of demand management as a structural element and foundation of energy networks that use renewable energy.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energy Reportsarrow_drop_down
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energy Reports
    Article . 2022 . Peer-reviewed
    License: CC BY NC ND
    Data sources: Crossref
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Energy Reports
    Article . 2022
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energy Reportsarrow_drop_down
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      Energy Reports
      Article . 2022 . Peer-reviewed
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      Energy Reports
      Article . 2022
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mario Villegas-Ruvalcaba; Kelly Gurubel-Tun; Alberto Coronado-Mendoza;

    The variability of renewable energies and their integration into the grid via power electronics demands the design of robust control algorithms. This work incorporates two techniques to ensure the stability of a boost converter through its state equations, implementing the inverse optimal control and the gain-scheduling technique for robust control settings. In such a way that, under a single adjustment, it is capable of damping different changes such as changes in the parameters, changes in the load, the input voltage, and the reference voltage. On the other hand, inverse optimal control is based on a discrete-time control Lyapunov function (CLF), and CLF candidate depends on fixed parameters that are selected to obtain the solution for inverse optimal control. Once these parameters have been found through heuristic or artificial intelligence methods, the new proposed methodology is capable of obtaining a robust optimal control scheme, without having to search for new parameters through other methods, since these are sometimes sensitive changes and many times the process of a new search is delayed. The results of the approach are simulated using Matlab, obtaining good performance of the proposed control under different operation conditions. Such simulations yielded errors of less than 1% based on the voltage reference, given the disturbances caused by changes in the input variables, system parameters, and changes in the reference. Thus, applying the new methodology, the stability of our system was preserved in all cases.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Energies
    Article . 2021 . Peer-reviewed
    License: CC BY
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    Energies
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    Energies
    Article . 2021
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
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      Energies
      Article . 2021 . Peer-reviewed
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mario Villegas-Ruvalcaba; Kelly Gurubel-Tun; Alberto Coronado-Mendoza;

    The variability of renewable energies and their integration into the grid via power electronics demands the design of robust control algorithms. This work incorporates two techniques to ensure the stability of a boost converter through its state equations, implementing the inverse optimal control and the gain-scheduling technique for robust control settings. In such a way that, under a single adjustment, it is capable of damping different changes such as changes in the parameters, changes in the load, the input voltage, and the reference voltage. On the other hand, inverse optimal control is based on a discrete-time control Lyapunov function (CLF), and CLF candidate depends on fixed parameters that are selected to obtain the solution for inverse optimal control. Once these parameters have been found through heuristic or artificial intelligence methods, the new proposed methodology is capable of obtaining a robust optimal control scheme, without having to search for new parameters through other methods, since these are sometimes sensitive changes and many times the process of a new search is delayed. The results of the approach are simulated using Matlab, obtaining good performance of the proposed control under different operation conditions. Such simulations yielded errors of less than 1% based on the voltage reference, given the disturbances caused by changes in the input variables, system parameters, and changes in the reference. Thus, applying the new methodology, the stability of our system was preserved in all cases.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Energiesarrow_drop_down
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    Energies
    Article . 2021 . Peer-reviewed
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    Authors: Iván Carrillo-Gómez; Alberto Coronado-Mendoza; Luis Fernando González-Gabriel;

    This paper shows the importance acquired by specialized software for the design of energy systems that operate under a scheme outside the electricity grid and incorporate Renewable Energy. The relevance of this work is based on the understanding that one of the objectives of current governments is to improve the quality of life of people and their marginality index, through access to electricity service. A methodology is proposed for the sizing of an isolated hybrid system using HOMER Pro, a software of optimization of power systems that facilitates the technical and economic evaluation of the system. It started with the elaboration of a demand profile for a rural community located in the municipality of Mezquitic in the North of the state of Jalisco based on an international review, which allows to improve its Human Development Index; and the selection of the different energy generation and storage components that make up the system. The result of the simulations allows us to build a table of results, which facilitates the selection of equipment because it allows to observe the different combinations of technical, economic and meteorological variables and economically viable.

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    Revista de Ingeniería Eléctrica
    Article . 2019 . Peer-reviewed
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      Revista de Ingeniería Eléctrica
      Article . 2019 . Peer-reviewed
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    Authors: Iván Carrillo-Gómez; Alberto Coronado-Mendoza; Luis Fernando González-Gabriel;

    This paper shows the importance acquired by specialized software for the design of energy systems that operate under a scheme outside the electricity grid and incorporate Renewable Energy. The relevance of this work is based on the understanding that one of the objectives of current governments is to improve the quality of life of people and their marginality index, through access to electricity service. A methodology is proposed for the sizing of an isolated hybrid system using HOMER Pro, a software of optimization of power systems that facilitates the technical and economic evaluation of the system. It started with the elaboration of a demand profile for a rural community located in the municipality of Mezquitic in the North of the state of Jalisco based on an international review, which allows to improve its Human Development Index; and the selection of the different energy generation and storage components that make up the system. The result of the simulations allows us to build a table of results, which facilitates the selection of equipment because it allows to observe the different combinations of technical, economic and meteorological variables and economically viable.

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    Revista de Ingeniería Eléctrica
    Article . 2019 . Peer-reviewed
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      Revista de Ingeniería Eléctrica
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    Authors: Mónica Camas-Náfate; Alberto Coronado-Mendoza; Carlos Vargas-Salgado; Jesús Águila-León; +1 Authors

    In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 °C; 3.6 C, 455 s) and 0.1705 (25 °C; 3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(nk), while GWO implied a potential polynomial complexity within the range of O(nk) or O(2n) based on execution times from populations of 10 to 1000.

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    Authors: Mónica Camas-Náfate; Alberto Coronado-Mendoza; Carlos Vargas-Salgado; Jesús Águila-León; +1 Authors

    In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 °C; 3.6 C, 455 s) and 0.1705 (25 °C; 3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(nk), while GWO implied a potential polynomial complexity within the range of O(nk) or O(2n) based on execution times from populations of 10 to 1000.

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    Authors: Alberto Coronado-Mendoza; Mónica Camas-Náfate; Jesús Sergio Artal-Sevil; José Antonio Domínguez-Navarro;

    The deployment of photovoltaic single-phase inverters has been rapidly increasing worldwide. However, the performance of these systems is highly influenced by atmospheric conditions and load variations, necessitating the development of performance indices to enhance their efficiency and energy quality. In this study, four performance indices are proposed to evaluate the efficiency and energy quality of photovoltaic systems quantitatively. The entire process is analyzed, encompassing solar energy capture, DC-DC and DC-AC conversion, and filtering, to deliver maximum energy and quality to the load. Furthermore, eight system parameters are optimized using advanced techniques such as genetic algorithms, particle swarm optimization, and gray wolf optimization. These optimizations enhance the global performance of two critical stages: (1) the maximum power point tracking algorithm based on sliding mode control, which minimizes switching losses in the boost stage, and (2) the effective transfer of captured solar power to the load by optimizing the gains of a PI controller. The PI controller computes the switching triggers for the inverter stage, significantly improving the total harmonic distortion of voltage and current waveforms. Simulation results validate the proposed approach, demonstrating a marked improvement in overall system efficiency (95.8%) when compared to the incremental conductance method (−11.8%) and a baseline sliding mode control configuration (−1.14%).

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    Authors: Alberto Coronado-Mendoza; Mónica Camas-Náfate; Jesús Sergio Artal-Sevil; José Antonio Domínguez-Navarro;

    The deployment of photovoltaic single-phase inverters has been rapidly increasing worldwide. However, the performance of these systems is highly influenced by atmospheric conditions and load variations, necessitating the development of performance indices to enhance their efficiency and energy quality. In this study, four performance indices are proposed to evaluate the efficiency and energy quality of photovoltaic systems quantitatively. The entire process is analyzed, encompassing solar energy capture, DC-DC and DC-AC conversion, and filtering, to deliver maximum energy and quality to the load. Furthermore, eight system parameters are optimized using advanced techniques such as genetic algorithms, particle swarm optimization, and gray wolf optimization. These optimizations enhance the global performance of two critical stages: (1) the maximum power point tracking algorithm based on sliding mode control, which minimizes switching losses in the boost stage, and (2) the effective transfer of captured solar power to the load by optimizing the gains of a PI controller. The PI controller computes the switching triggers for the inverter stage, significantly improving the total harmonic distortion of voltage and current waveforms. Simulation results validate the proposed approach, demonstrating a marked improvement in overall system efficiency (95.8%) when compared to the incremental conductance method (−11.8%) and a baseline sliding mode control configuration (−1.14%).

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  • Authors: K. J. Gurubel; V. Osuna-Enciso; J. J. Cardenas; A. Coronado-Mendoza; +2 Authors

    Energy systems with renewable sources are used around the world in order to satisfy both off-grid and on-grid load demands, and are commonly coupled to conventional sources. A good behavior of this kind of systems depends on the renewable sources availability that includes the solar irradiance and the wind speed, as well as the profile variations over the energy demand. Their main objective is to satisfy the load demand while minimizing the use of conventional sources, reducing pollutant emissions and storing the energy excess for deficit conditions. This paper presents modeling, neural forecasting and optimal sizing for hybrid energy systems, which are proposed to minimize both the overall annual cost and the use of conventional sources, which in turn represents reduction of pollutant emissions. In this paper, the use of renewable sources along with load demand variations are predicted by a High Order Neural Network trained with an Extended Kalman Filter, whereas the optimal sizing is calculated by using both a Clonal Selection Algorithm and a Genetic Algorithm. The efficiency of using neural forecasting data is illustrated through a simulation with the results showing the effectiveness of both optimization algorithms for calculating an optimal sizing of the hybrid system, which ultimately represents an optimal cost-effective system.

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  • Authors: K. J. Gurubel; V. Osuna-Enciso; J. J. Cardenas; A. Coronado-Mendoza; +2 Authors

    Energy systems with renewable sources are used around the world in order to satisfy both off-grid and on-grid load demands, and are commonly coupled to conventional sources. A good behavior of this kind of systems depends on the renewable sources availability that includes the solar irradiance and the wind speed, as well as the profile variations over the energy demand. Their main objective is to satisfy the load demand while minimizing the use of conventional sources, reducing pollutant emissions and storing the energy excess for deficit conditions. This paper presents modeling, neural forecasting and optimal sizing for hybrid energy systems, which are proposed to minimize both the overall annual cost and the use of conventional sources, which in turn represents reduction of pollutant emissions. In this paper, the use of renewable sources along with load demand variations are predicted by a High Order Neural Network trained with an Extended Kalman Filter, whereas the optimal sizing is calculated by using both a Clonal Selection Algorithm and a Genetic Algorithm. The efficiency of using neural forecasting data is illustrated through a simulation with the results showing the effectiveness of both optimization algorithms for calculating an optimal sizing of the hybrid system, which ultimately represents an optimal cost-effective system.

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  • Authors: Alberto Coronado-Mendoza; Angel A. Bayod-Rújula; Jose M. Yusta; José A. Domínguez-Navarro; +3 Authors

    An energy management system to minimize the operation cost of a microgrid is presented in this work. The microgrid is composed of the technologies more used as renewable energies and storage systems, as well as active consumers which demand depends on price. The proposed model is based on linear programming with the operating cost as the objective function and the operating limits as constraints. The model has two phases. The first phase does the optimal dispatch of generating units in function of the forecasting demand and the offers made by generators. The second phase minimizes the differences between the planning values obtained in the first phase and the real data, because there are differences as consequence of the uncertainties of renewable energies and the consumers’ behaviour.

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  • Authors: Alberto Coronado-Mendoza; Angel A. Bayod-Rújula; Jose M. Yusta; José A. Domínguez-Navarro; +3 Authors

    An energy management system to minimize the operation cost of a microgrid is presented in this work. The microgrid is composed of the technologies more used as renewable energies and storage systems, as well as active consumers which demand depends on price. The proposed model is based on linear programming with the operating cost as the objective function and the operating limits as constraints. The model has two phases. The first phase does the optimal dispatch of generating units in function of the forecasting demand and the offers made by generators. The second phase minimizes the differences between the planning values obtained in the first phase and the real data, because there are differences as consequence of the uncertainties of renewable energies and the consumers’ behaviour.

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  • Authors: K. J. Gurubel; V. Osuna-Enciso; A. Coronado-Mendoza; E. Cuevas;

    In the renewable energy generation, several processes require the integration of a set of advanced techniques in order to find optimal solutions. Dynamic estimation, stabilizing control for disturbance rejection, optimization for control effort, and parameter tuning are techniques used to address the whole process requirements and obtain optimal results. In this paper, an optimal control strategy for a maximum biofuel production in the presence of disturbances is proposed. First, an integrated optimal control strategy to maximize biofuel production in the presence of disturbances is proposed. Second, due to its high nonlinearity, complex nature, and multiplicity of equilibrium points, a biological process for biofuel generation is described in order to demonstrate the efficiency of the optimal control strategy. A nonlinear discrete-time neural observer for unknown nonlinear systems in the presence of external disturbances and parameter uncertainties is used to estimate unmeasurable variables. An inverse optimal control law for trajectory tracking based on the neural observer is designed such that asymptotic convergence reference trajectory is guaranteed. Differential Evolution and Clonal Selection Algorithms are used to calculate the optimal parameters for neural network training, neural network gains, and feedback control gains. Additionally, a supervisory fuzzy control is proposed in order to select the adequate control action between the closed loop and the open loop and to determine optimal reference trajectories. Simulation results comparison and statistical validation are presented, where it is demonstrated that the optimal control strategy integrated with the Differential Evolution algorithm gives better results to maximize the biofuel production in the presence of disturbances.

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  • Authors: K. J. Gurubel; V. Osuna-Enciso; A. Coronado-Mendoza; E. Cuevas;

    In the renewable energy generation, several processes require the integration of a set of advanced techniques in order to find optimal solutions. Dynamic estimation, stabilizing control for disturbance rejection, optimization for control effort, and parameter tuning are techniques used to address the whole process requirements and obtain optimal results. In this paper, an optimal control strategy for a maximum biofuel production in the presence of disturbances is proposed. First, an integrated optimal control strategy to maximize biofuel production in the presence of disturbances is proposed. Second, due to its high nonlinearity, complex nature, and multiplicity of equilibrium points, a biological process for biofuel generation is described in order to demonstrate the efficiency of the optimal control strategy. A nonlinear discrete-time neural observer for unknown nonlinear systems in the presence of external disturbances and parameter uncertainties is used to estimate unmeasurable variables. An inverse optimal control law for trajectory tracking based on the neural observer is designed such that asymptotic convergence reference trajectory is guaranteed. Differential Evolution and Clonal Selection Algorithms are used to calculate the optimal parameters for neural network training, neural network gains, and feedback control gains. Additionally, a supervisory fuzzy control is proposed in order to select the adequate control action between the closed loop and the open loop and to determine optimal reference trajectories. Simulation results comparison and statistical validation are presented, where it is demonstrated that the optimal control strategy integrated with the Differential Evolution algorithm gives better results to maximize the biofuel production in the presence of disturbances.

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    Authors: Mónica Camas-Náfate; Alberto Coronado-Mendoza; Carlos Jesahel Vega-Gómez; Francisco Espinosa-Moreno;

    The analysis of the behavior of lithium-ion batteries has gained considerable interest in recent years. There are different alternatives for the analysis of this behavior; however, depending on the type of modeling, there are application and optimization restrictions. In this work, a hybrid model has been made that is capable of predicting the characteristics of a lithium-ion battery. As a novelty, the simplification, at the same time, facilitates the sampling of parameters for their prompt selection for optimization. A new model open to the user is proposed, which has proven to be efficient in simulation time. For example, one hour simulates it in 5 min, providing information detailing how these parameters, State of Health (SOH), Open Circuit Voltage (VOC), State of charge (SOC), and Number of charge and discharge cycles, in the face of temperature variations and charge and discharge cycles. Opening the possibility of optimizing the parameters with different techniques to estimate the performance and dynamics in the face of temperature change and charge and discharge cycles. A model based on linear regressions, manufacturer characteristics, and integrating equations in the electrical model of electrochemical phenomena is proposed.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Mónica Camas-Náfate; Alberto Coronado-Mendoza; Carlos Jesahel Vega-Gómez; Francisco Espinosa-Moreno;

    The analysis of the behavior of lithium-ion batteries has gained considerable interest in recent years. There are different alternatives for the analysis of this behavior; however, depending on the type of modeling, there are application and optimization restrictions. In this work, a hybrid model has been made that is capable of predicting the characteristics of a lithium-ion battery. As a novelty, the simplification, at the same time, facilitates the sampling of parameters for their prompt selection for optimization. A new model open to the user is proposed, which has proven to be efficient in simulation time. For example, one hour simulates it in 5 min, providing information detailing how these parameters, State of Health (SOH), Open Circuit Voltage (VOC), State of charge (SOC), and Number of charge and discharge cycles, in the face of temperature variations and charge and discharge cycles. Opening the possibility of optimizing the parameters with different techniques to estimate the performance and dynamics in the face of temperature change and charge and discharge cycles. A model based on linear regressions, manufacturer characteristics, and integrating equations in the electrical model of electrochemical phenomena is proposed.

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      License: CC BY
      Data sources: Crossref
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      Sustainability
      Article . 2022
      Data sources: DOAJ
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