IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i4p616-d206192.html
   My bibliography  Save this article

Mitigating Energy System Vulnerability by Implementing a Microgrid with a Distributed Management Algorithm

Author

Listed:
  • Juan M. Lujano-Rojas

    (Department of Electrical Engineering, University of Zaragoza, Calle María de Luna 3, 50018 Zaragoza, Spain)

  • José M. Yusta

    (Department of Electrical Engineering, University of Zaragoza, Calle María de Luna 3, 50018 Zaragoza, Spain)

  • José Antonio Domínguez-Navarro

    (Department of Electrical Engineering, University of Zaragoza, Calle María de Luna 3, 50018 Zaragoza, Spain)

Abstract

This work presents a management strategy for microgrid (MG) operation. Photovoltaic (PV) and wind generators, as well as storage systems and conventional units, are distributed over a wide geographical area, forming a distributed energy system, which is coordinated to face any contingency of the utility company by means of its isolated operation. The management strategy divides the system into three main layers: renewable generation, storage devices, and conventional units. Interactions between devices of the same layer are determined by solving an economic dispatch problem (EDP) in a distributed manner using a consensus algorithm (CA), and interactions between layers are determined by means of a load following strategy. In this way, the complex behaviour of PV and wind generation, the battery storage system, and conventional units has been effectively combined with CA to solve EDP in a distributed manner. MG performance and its vulnerability are deeply analysed by means of an illustrative case study. From the observed results, vulnerability under extreme conditions could be reduced up to approximately 30% by coupling distributed renewable generation and storage capacity with an energy system based on conventional generation.

Suggested Citation

  • Juan M. Lujano-Rojas & José M. Yusta & José Antonio Domínguez-Navarro, 2019. "Mitigating Energy System Vulnerability by Implementing a Microgrid with a Distributed Management Algorithm," Energies, MDPI, vol. 12(4), pages 1-30, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:616-:d:206192
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/4/616/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/4/616/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zio, Enrico & Aven, Terje, 2011. "Uncertainties in smart grids behavior and modeling: What are the risks and vulnerabilities? How to analyze them?," Energy Policy, Elsevier, vol. 39(10), pages 6308-6320, October.
    2. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
    3. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    4. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    5. Rampinelli, G.A. & Krenzinger, A. & Chenlo Romero, F., 2014. "Mathematical models for efficiency of inverters used in grid connected photovoltaic systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 578-587.
    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. F. Ignacio Aguilar & Robert B. Gibson, 2023. "Advancing a New Generation of Sustainability-Based Assessments for Electrical Energy Systems: Ontario as an Illustrative Application—A Review," Energies, MDPI, vol. 16(17), pages 1-26, August.

    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. Lujano-Rojas, Juan M. & Dufo-López, Rodolfo & Artal-Sevil, Jesús Sergio & García-Paricio, Eduardo, 2024. "Design of small-scale hybrid energy systems taking into account generation and demand uncertainties," Renewable Energy, Elsevier, vol. 227(C).
    2. de Guibert, Paul & Shirizadeh, Behrang & Quirion, Philippe, 2020. "Variable time-step: A method for improving computational tractability for energy system models with long-term storage," Energy, Elsevier, vol. 213(C).
    3. Marko Hočevar & Lovrenc Novak & Primož Drešar & Gašper Rak, 2022. "The Status Quo and Future of Hydropower in Slovenia," Energies, MDPI, vol. 15(19), pages 1-13, September.
    4. Lukas Kriechbaum & Philipp Gradl & Romeo Reichenhauser & Thomas Kienberger, 2020. "Modelling Grid Constraints in a Multi-Energy Municipal Energy System Using Cumulative Exergy Consumption Minimisation," Energies, MDPI, vol. 13(15), pages 1-23, July.
    5. Behrang Shirizadeh, Quentin Perrier, and Philippe Quirion, 2022. "How Sensitive are Optimal Fully Renewable Power Systems to Technology Cost Uncertainty?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    6. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    7. Liu, Hailiang & Andresen, Gorm Bruun & Greiner, Martin, 2018. "Cost-optimal design of a simplified highly renewable Chinese electricity network," Energy, Elsevier, vol. 147(C), pages 534-546.
    8. Géremi Gilson Dranka & Paula Ferreira, 2020. "Electric Vehicles and Biofuels Synergies in the Brazilian Energy System," Energies, MDPI, vol. 13(17), pages 1-22, August.
    9. Shirizadeh, Behrang & Quirion, Philippe, 2022. "The importance of renewable gas in achieving carbon-neutrality: Insights from an energy system optimization model," Energy, Elsevier, vol. 255(C).
    10. Liu, Hailiang & Brown, Tom & Andresen, Gorm Bruun & Schlachtberger, David P. & Greiner, Martin, 2019. "The role of hydro power, storage and transmission in the decarbonization of the Chinese power system," Applied Energy, Elsevier, vol. 239(C), pages 1308-1321.
    11. Andrés Henao-Muñoz & Andrés Saavedra-Montes & Carlos Ramos-Paja, 2018. "Optimal Power Dispatch of Small-Scale Standalone Microgrid Located in Colombian Territory," Energies, MDPI, vol. 11(7), pages 1-20, July.
    12. Laura Canale & Anna Rita Di Fazio & Mario Russo & Andrea Frattolillo & Marco Dell’Isola, 2021. "An Overview on Functional Integration of Hybrid Renewable Energy Systems in Multi-Energy Buildings," Energies, MDPI, vol. 14(4), pages 1-33, February.
    13. Gorre, Jachin & Ortloff, Felix & van Leeuwen, Charlotte, 2019. "Production costs for synthetic methane in 2030 and 2050 of an optimized Power-to-Gas plant with intermediate hydrogen storage," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    14. Shirizadeh, Behrang & Quirion, Philippe, 2021. "Low-carbon options for the French power sector: What role for renewables, nuclear energy and carbon capture and storage?," Energy Economics, Elsevier, vol. 95(C).
    15. Topi Rasku & Juha Kiviluoma, 2018. "A Comparison of Widespread Flexible Residential Electric Heating and Energy Efficiency in a Future Nordic Power System," Energies, MDPI, vol. 12(1), pages 1-27, December.
    16. Dranka, Géremi Gilson & Ferreira, Paula, 2018. "Planning for a renewable future in the Brazilian power system," Energy, Elsevier, vol. 164(C), pages 496-511.
    17. Li, Xudong & Yang, Weijia & Liao, Yiwen & Zhang, Shushu & Zheng, Yang & Zhao, Zhigao & Tang, Maojia & Cheng, Yongguang & Liu, Pan, 2024. "Short-term risk-management for hydro-wind-solar hybrid energy system considering hydropower part-load operating characteristics," Applied Energy, Elsevier, vol. 360(C).
    18. Héctor Marañón-Ledesma & Asgeir Tomasgard, 2019. "Analyzing Demand Response in a Dynamic Capacity Expansion Model for the European Power Market," Energies, MDPI, vol. 12(15), pages 1-24, August.
    19. Bosch, Jonathan & Staffell, Iain & Hawkes, Adam D., 2017. "Temporally-explicit and spatially-resolved global onshore wind energy potentials," Energy, Elsevier, vol. 131(C), pages 207-217.
    20. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Nybø, Astrid, 2020. "Transitioning remote Arctic settlements to renewable energy systems – A modelling study of Longyearbyen, Svalbard," Applied Energy, Elsevier, vol. 258(C).

    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:12:y:2019:i:4:p:616-:d:206192. 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.