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Approach to an Emulation Model to Evaluate the Behavior and Impact of Microgrids in Isolated Communities

Author

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  • Carlos M. Paredes

    (Departamento de Automática y Electrónica, Universidad Autónoma de Occidente, Cali 760030, Colombia)

  • Andrés F. Bayona

    (Departamento de Automática y Electrónica, Universidad Autónoma de Occidente, Cali 760030, Colombia)

  • Diego Martínez

    (Departamento de Automática y Electrónica, Universidad Autónoma de Occidente, Cali 760030, Colombia)

  • Alfons Crespo

    (Departamento de Informática de Sistemas y Computadores, Universidad Politécnica de Valencia, 46022 Valencia, Spain)

  • Apolinar González

    (Facultad de Ingeniería Mecánica y Eléctrica, Universidad de Colima, Colima 28400, Mexico)

  • José Simo

    (Departamento de Informática de Sistemas y Computadores, Universidad Politécnica de Valencia, 46022 Valencia, Spain)

Abstract

In microgrid projects, social ownership involves aspects beyond their operation that may compromise the sustainability of the system. For this reason, the development of analysis methods to assess the feasibility and impact during the design stages of these solutions is of growing interest. Recent studies have proposed methods that allow an individual analysis of technological components and social behaviors. However, a complete evaluation of the performance and the impact of these projects should allow the simultaneous evaluation of the behavior of these subsystems, allowing the analysis of their interactions and effects in a dynamic way. Accordingly, this paper presents simulation and emulation models to evaluate the impact of a microgrid in isolated communities. These models contemplate sublevels that consider the energetic, automation and computational aspects in the microgrids and a multi-agent system (MAS) that is used to study the environmental and economic impact of the microgrid through the evolution of certain indicators. The socio-technological interdependence in the operation of the isolated microgrid is analyzed through the integration of the microgrid emulation platform with the MAS. Our approach includes a comprehensive study of the performance of these projects in specific communities, in order to contribute to the design and implementation, considering the technological, economic, environmental, and social impacts.

Suggested Citation

  • Carlos M. Paredes & Andrés F. Bayona & Diego Martínez & Alfons Crespo & Apolinar González & José Simo, 2021. "Approach to an Emulation Model to Evaluate the Behavior and Impact of Microgrids in Isolated Communities," Energies, MDPI, vol. 14(17), pages 1-34, August.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5316-:d:623080
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    References listed on IDEAS

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    1. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    2. Groppi, D. & Astiaso Garcia, D. & Lo Basso, G. & De Santoli, L., 2019. "Synergy between smart energy systems simulation tools for greening small Mediterranean islands," Renewable Energy, Elsevier, vol. 135(C), pages 515-524.
    3. Claudia Rahmann & Oscar Núñez & Felipe Valencia & Susana Arrechea & Jalel Sager & Daniel Kammen, 2016. "Methodology for Monitoring Sustainable Development of Isolated Microgrids in Rural Communities," Sustainability, MDPI, vol. 8(11), pages 1-24, November.
    4. Peter Roopnarine, 2013. "Ecology and the Tragedy of the Commons," Sustainability, MDPI, vol. 5(2), pages 1-25, February.
    5. Adefarati, T. & Bansal, R.C., 2019. "Reliability, economic and environmental analysis of a microgrid system in the presence of renewable energy resources," Applied Energy, Elsevier, vol. 236(C), pages 1089-1114.
    6. Gamarra, Carlos & Guerrero, Josep M., 2015. "Computational optimization techniques applied to microgrids planning: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 413-424.
    7. Ortega-Arriaga, P. & Babacan, O. & Nelson, J. & Gambhir, A., 2021. "Grid versus off-grid electricity access options: A review on the economic and environmental impacts," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    8. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
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