IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i14p4162-d591771.html
   My bibliography  Save this article

Modeling of Small Productive Processes for the Operation of a Microgrid

Author

Listed:
  • Danny Espín-Sarzosa

    (Department of Electrical Engineering, Faculty of Physical and Mathematical Sciences, University of Chile, Tupper Av. 2007, Santiago 8370451, Chile
    Energy Center, Faculty of Physical and Mathematical Sciences, University of Chile, Ercilla 847, Santiago 8370450, Chile)

  • Rodrigo Palma-Behnke

    (Department of Electrical Engineering, Faculty of Physical and Mathematical Sciences, University of Chile, Tupper Av. 2007, Santiago 8370451, Chile
    Energy Center, Faculty of Physical and Mathematical Sciences, University of Chile, Ercilla 847, Santiago 8370450, Chile)

  • Felipe Valencia

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

Abstract

Small productive processes (SPPs) are promising drivers that promote the economic use of energy in microgrids (MGs). Both the complex nature of the SPPs and voltage variations make the operation of MGs challenging, since the quality of an energy management system’s (EMS) decisions depend on its characterization. The aim of this work is to propose a methodology for SPPs modeling, and to consider the influence of voltage on load consumption, which has general validity, and can be efficiently integrated into different MG EMS approaches. For this purpose, a novel extended multi-zone ZIP approach for the characterization of SPP loads and sensitivity to voltage changes is proposed. The associated framework herein presented was assessed using actual data collected from SPPs installed near the city of Arica, in northern Chile. The results showed that the proposed methodology was capable of representing the complex load behavior of the SPPs, properly considering the voltage influence. These results were compared with those obtained through common approaches found in the literature. The effectiveness of the proposed approach in representing SPP loads and their sensitivity to voltage changes was verified. The proposed scheme can be efficiently integrated into a wide range of EMS for MGs that include SPPs.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4162-:d:591771
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/14/4162/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/14/4162/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    2. Bustos, Cristian & Watts, David, 2017. "Novel methodology for microgrids in isolated communities: Electricity cost-coverage trade-off with 3-stage technology mix, dispatch & configuration optimizations," Applied Energy, Elsevier, vol. 195(C), pages 204-221.
    3. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, 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. Zia, Muhammad Fahad & Elbouchikhi, Elhoussin & Benbouzid, Mohamed, 2018. "Microgrids energy management systems: A critical review on methods, solutions, and prospects," Applied Energy, Elsevier, vol. 222(C), pages 1033-1055.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Min Yi & Wei Xie & Li Mo, 2021. "Short-Term Electricity Price Forecasting Based on BP Neural Network Optimized by SAPSO," Energies, MDPI, vol. 14(20), pages 1-17, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leonori, Stefano & Martino, Alessio & Frattale Mascioli, Fabio Massimo & Rizzi, Antonello, 2020. "Microgrid Energy Management Systems Design by Computational Intelligence Techniques," Applied Energy, Elsevier, vol. 277(C).
    2. Raya-Armenta, Jose Maurilio & Bazmohammadi, Najmeh & Avina-Cervantes, Juan Gabriel & Sáez, Doris & Vasquez, Juan C. & Guerrero, Josep M., 2021. "Energy management system optimization in islanded microgrids: An overview and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    3. Lilia Tightiz & Joon Yoo, 2022. "A Review on a Data-Driven Microgrid Management System Integrating an Active Distribution Network: Challenges, Issues, and New Trends," Energies, MDPI, vol. 15(22), pages 1-24, November.
    4. 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.
    5. Sewdien, V.N. & Preece, R. & Torres, J.L. Rueda & Rakhshani, E. & van der Meijden, M., 2020. "Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting," Renewable Energy, Elsevier, vol. 161(C), pages 878-892.
    6. Md Shafiullah & Akib Mostabe Refat & Md Ershadul Haque & Dewan Mabrur Hasan Chowdhury & Md Sanower Hossain & Abdullah G. Alharbi & Md Shafiul Alam & Amjad Ali & Shorab Hossain, 2022. "Review of Recent Developments in Microgrid Energy Management Strategies," Sustainability, MDPI, vol. 14(22), pages 1-30, November.
    7. Àlex Alonso-Travesset & Helena Martín & Sergio Coronas & Jordi de la Hoz, 2022. "Optimization Models under Uncertainty in Distributed Generation Systems: A Review," Energies, MDPI, vol. 15(5), pages 1-40, March.
    8. Sharma, Pavitra & Dutt Mathur, Hitesh & Mishra, Puneet & Bansal, Ramesh C., 2022. "A critical and comparative review of energy management strategies for microgrids," Applied Energy, Elsevier, vol. 327(C).
    9. Villanueva-Rosario, Junior Alexis & Santos-García, Félix & Aybar-Mejía, Miguel Euclides & Mendoza-Araya, Patricio & Molina-García, Angel, 2022. "Coordinated ancillary services, market participation and communication of multi-microgrids: A review," Applied Energy, Elsevier, vol. 308(C).
    10. Polimeni, Simone & Moretti, Luca & Martelli, Emanuele & Leva, Sonia & Manzolini, Giampaolo, 2023. "A novel stochastic model for flexible unit commitment of off-grid microgrids," Applied Energy, Elsevier, vol. 331(C).
    11. Soheil Mohseni & Alan C. Brent, 2022. "A Metaheuristic-Based Micro-Grid Sizing Model with Integrated Arbitrage-Aware Multi-Day Battery Dispatching," Sustainability, MDPI, vol. 14(19), pages 1-24, October.
    12. Gui, Yonghao & Wei, Baoze & Li, Mingshen & Guerrero, Josep M. & Vasquez, Juan C., 2018. "Passivity-based coordinated control for islanded AC microgrid," Applied Energy, Elsevier, vol. 229(C), pages 551-561.
    13. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    14. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
    15. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    16. Thomas Schmitt & Tobias Rodemann & Jürgen Adamy, 2021. "The Cost of Photovoltaic Forecasting Errors in Microgrid Control with Peak Pricing," Energies, MDPI, vol. 14(9), pages 1-13, April.
    17. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    18. Yıldıran, Uğur & Kayahan, İsmail, 2018. "Risk-averse stochastic model predictive control-based real-time operation method for a wind energy generation system supported by a pumped hydro storage unit," Applied Energy, Elsevier, vol. 226(C), pages 631-643.
    19. Muhammad Umair Safder & Mohammad J. Sanjari & Ameer Hamza & Rasoul Garmabdari & Md. Alamgir Hossain & Junwei Lu, 2023. "Enhancing Microgrid Stability and Energy Management: Techniques, Challenges, and Future Directions," Energies, MDPI, vol. 16(18), pages 1-28, September.
    20. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4162-:d:591771. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.