IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i3p696-d727775.html
   My bibliography  Save this article

Reliability Optimization of Multi-Energy System Considering Energy Storage Devices Effects under Weather Uncertainties

Author

Listed:
  • Ziyan Liao

    (Electric and Electronic Engineering Department, University of Manchester, Manchester M13 9PL, UK)

  • Alessandra Parisio

    (Electric and Electronic Engineering Department, University of Manchester, Manchester M13 9PL, UK)

Abstract

Multi-energy systems (MES) allow various energy forms, such as electricity, gas, and heat, to interact and achieve energy transfer and mutually benefit, reducing the probability of load cutting in the event of a failure, increasing the energy utilization efficiency, and improving the reliability and robustness of the overall energy supply system. Since energy storage systems can help to restore power in the case of failure and store the surplus energy to enhance the flexibility of MES, this work provides a methodology for reliability optimization, considering different energy storage configuration schemes under weather uncertainties. First of all, a reliability evaluation model of a multi-energy system under weather uncertainties based on a sequential Monte Carlo simulation is established. Then, the reliability optimization problem is formulated as a multi-objective optimization problem to minimize the reliability index, SAIDI (system average interruption duration index), and the reliability cost. Finally, a case study implemented on a typical MES layout is used to demonstrate the proposed methodology. A comparative analysis of three widely adopted multi-objective metaheuristic algorithms, including NSGA-II (non-dominated sorting genetic algorithm II), MOPSO (multiple objective particle swarm optimization), and SPEA2 (strength Pareto evolution algorithm 2), is performed to validate the effectiveness of the proposed method. The simulation results show that the NSGA-II algorithm leads to better optimal values and converges the fastest compared to the other two methods.

Suggested Citation

  • Ziyan Liao & Alessandra Parisio, 2022. "Reliability Optimization of Multi-Energy System Considering Energy Storage Devices Effects under Weather Uncertainties," Energies, MDPI, vol. 15(3), pages 1-24, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:696-:d:727775
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/3/696/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/3/696/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chaudry, Modassar & Wu, Jianzhong & Jenkins, Nick, 2013. "A sequential Monte Carlo model of the combined GB gas and electricity network," Energy Policy, Elsevier, vol. 62(C), pages 473-483.
    2. Zamani Gargari, Milad & Ghaffarpour, Reza, 2020. "Reliability evaluation of multi-carrier energy system with different level of demands under various weather situation," Energy, Elsevier, vol. 196(C).
    Full references (including those not matched with items on IDEAS)

    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. Curtis, John & Lynch, Muireann Á. & Zubiate, Laura, 2016. "The impact of the North Atlantic Oscillation on electricity markets: A case study on Ireland," Energy Economics, Elsevier, vol. 58(C), pages 186-198.
    2. Guoqiang Sun & Wenxue Wang & Yi Wu & Wei Hu & Zijun Yang & Zhinong Wei & Haixiang Zang & Sheng Chen, 2019. "A Nonlinear Analytical Algorithm for Predicting the Probabilistic Mass Flow of a Radial District Heating Network," Energies, MDPI, vol. 12(7), pages 1-20, March.
    3. Zheng, Yingying & Jenkins, Bryan M. & Kornbluth, Kurt & Træholt, Chresten, 2018. "Optimization under uncertainty of a biomass-integrated renewable energy microgrid with energy storage," Renewable Energy, Elsevier, vol. 123(C), pages 204-217.
    4. Yu, Weichao & Song, Shangfei & Li, Yichen & Min, Yuan & Huang, Weihe & Wen, Kai & Gong, Jing, 2018. "Gas supply reliability assessment of natural gas transmission pipeline systems," Energy, Elsevier, vol. 162(C), pages 853-870.
    5. Jiang, Tao & Zhang, Rufeng & Li, Xue & Chen, Houhe & Li, Guoqing, 2021. "Integrated energy system security region: Concepts, methods, and implementations," Applied Energy, Elsevier, vol. 283(C).
    6. Lynch & John Curtis, 2016. "The effects of wind generation capacity on electricity prices and generation costs: a Monte Carlo analysis," Applied Economics, Taylor & Francis Journals, vol. 48(2), pages 133-151, January.
    7. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "State-of-the-art generation expansion planning: A review," Applied Energy, Elsevier, vol. 230(C), pages 563-589.
    8. Andrea Antenucci & Giovanni Sansavini, 2018. "Adequacy and security analysis of interdependent electric and gas networks," Journal of Risk and Reliability, , vol. 232(2), pages 121-139, April.
    9. Antenucci, Andrea & Sansavini, Giovanni, 2019. "Extensive CO2 recycling in power systems via Power-to-Gas and network storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 33-43.
    10. Cabrales, Sergio & Valencia, Carlos & Ramírez, Carlos & Ramírez, Andrés & Herrera, Juan & Cadena, Angela, 2022. "Stochastic cost-benefit analysis to assess new infrastructure to improve the reliability of the natural gas supply," Energy, Elsevier, vol. 246(C).
    11. Wei, Zhinong & Chen, Sheng & Sun, Guoqiang & Wang, Dan & Sun, Yonghui & Zang, Haixiang, 2016. "Probabilistic available transfer capability calculation considering static security constraints and uncertainties of electricity–gas integrated energy systems," Applied Energy, Elsevier, vol. 167(C), pages 305-316.
    12. Lasemi, Mohammad Ali & Arabkoohsar, Ahmad & Hajizadeh, Amin & Mohammadi-ivatloo, Behnam, 2022. "A comprehensive review on optimization challenges of smart energy hubs under uncertainty factors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    13. Dodds, Paul E., 2014. "Integrating housing stock and energy system models as a strategy to improve heat decarbonisation assessments," Applied Energy, Elsevier, vol. 132(C), pages 358-369.
    14. Yu, Weichao & Huang, Weihe & Wen, Yunhao & Li, Yichen & Liu, Hongfei & Wen, Kai & Gong, Jing & Lu, Yanan, 2021. "An integrated gas supply reliability evaluation method of the large-scale and complex natural gas pipeline network based on demand-side analysis," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    15. Cabral, Charlette & Andiappan, Viknesh & Aviso, Kathleen & Tan, Raymond, 2021. "Equipment size selection for optimizing polygeneration systems with reliability aspects," Energy, Elsevier, vol. 234(C).
    16. Devlin, Joseph & Li, Kang & Higgins, Paraic & Foley, Aoife, 2016. "The importance of gas infrastructure in power systems with high wind power penetrations," Applied Energy, Elsevier, vol. 167(C), pages 294-304.
    17. Xu, Qingqing & Wu, Yuhang & Zheng, Wenpei & Gong, Yunhua & Dubljevic, Stevan, 2023. "Modeling and dynamic safety control of compressed air energy storage system," Renewable Energy, Elsevier, vol. 208(C), pages 203-213.
    18. Javad Estakhr & Mohsen Simab & Taher Niknam, 2021. "Security Analysis of Hybrid Multi-Carrier Energy Systems," Sustainability, MDPI, vol. 13(6), pages 1-21, March.
    19. Kosai, Shoki & Cravioto, Jordi, 2020. "Resilience of standalone hybrid renewable energy systems: The role of storage capacity," Energy, Elsevier, vol. 196(C).
    20. Daniel Scamman & Baltazar Solano-Rodríguez & Steve Pye & Lai Fong Chiu & Andrew Z. P. Smith & Tiziano Gallo Cassarino & Mark Barrett & Robert Lowe, 2020. "Heat Decarbonisation Modelling Approaches in the UK: An Energy System Architecture Perspective," Energies, MDPI, vol. 13(8), pages 1-28, April.

    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:15:y:2022:i:3:p:696-:d:727775. 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.