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Towards Digital Twins of Small Productive Processes in Microgrids

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  • Danny Espín-Sarzosa

    (Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Brasil Av. 2147, Valparaíso 2362804, Chile
    Energy Center, Faculty of Physical and Mathematical Sciences, University of Chile, Ercilla 847, Santiago 8370450, Chile)

  • Rodrigo Palma-Behnke

    (Energy Center, Faculty of Physical and Mathematical Sciences, University of Chile, Ercilla 847, Santiago 8370450, Chile)

  • Felipe Valencia-Arroyave

    (Facultad de Ciencias de la Ingeniería, Universidad Austral de Chile, General Lagos St 2086, Valdivia 5111187, Chile)

Abstract

In microgrids (MGs), energy management systems (EMSs) have been using increasingly detailed models of generation units, loads, and networks to make decisions on the power/energy contribution of each available unit to meet the electrical energy demand. This work aims to investigate the use of digital twins (DT) of small productive processes (SPPs) to regulate endogenous process variables to ensure final product quality, while the expected power consumption is estimated and communicated to the EMS so that it can make its decisions on the participation of each power source in meeting the electrical energy demand. The literature review reveals that this is one of the first attempts, in the context of MGs, to generate DT for SPPs that combine not only the electrical energy consumption, but also link it with the energy/mass balances taking place in the SPPs, highlighting the complexity that SPPs have as electrical loads. The results demonstrate that environmental conditions significantly influence the final electrical consumption of the SPPs. Additionally, the MG exhibits better economic performance when the SPP DT supports EMS decision-making, which is of great importance in MGs due to the special conditions they have for electric power generation, being more challenging in isolated MGs.

Suggested Citation

  • Danny Espín-Sarzosa & Rodrigo Palma-Behnke & Felipe Valencia-Arroyave, 2023. "Towards Digital Twins of Small Productive Processes in Microgrids," Energies, MDPI, vol. 16(11), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:11:p:4324-:d:1155633
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    References listed on IDEAS

    as
    1. Min, Qingfei & Lu, Yangguang & Liu, Zhiyong & Su, Chao & Wang, Bo, 2019. "Machine Learning based Digital Twin Framework for Production Optimization in Petrochemical Industry," International Journal of Information Management, Elsevier, vol. 49(C), pages 502-519.
    2. Hyang-A Park & Gilsung Byeon & Wanbin Son & Hyung-Chul Jo & Jongyul Kim & Sungshin Kim, 2020. "Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin," Energies, MDPI, vol. 13(20), pages 1-15, October.
    3. Danny Espín-Sarzosa & Rodrigo Palma-Behnke & Felipe Valencia, 2021. "Modeling of Small Productive Processes for the Operation of a Microgrid," Energies, MDPI, vol. 14(14), pages 1-19, July.
    4. Danny Espín-Sarzosa & Rodrigo Palma-Behnke & Oscar Núñez-Mata, 2020. "Energy Management Systems for Microgrids: Main Existing Trends in Centralized Control Architectures," Energies, MDPI, vol. 13(3), pages 1-32, January.
    5. Guodong Liu & Thomas B. Ollis & Bailu Xiao & Xiaohu Zhang & Kevin Tomsovic, 2017. "Community Microgrid Scheduling Considering Network Operational Constraints and Building Thermal Dynamics," Energies, MDPI, vol. 10(10), pages 1-19, October.
    6. Terrapon-Pfaff, Julia & Gröne, Marie-Christine & Dienst, Carmen & Ortiz, Willington, 2018. "Productive use of energy – Pathway to development? Reviewing the outcomes and impacts of small-scale energy projects in the global south," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 198-209.
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