IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v199y2022icp866-880.html
   My bibliography  Save this article

Uncertainty modeling methods for risk-averse planning and operation of stand-alone renewable energy-based microgrids

Author

Listed:
  • Bakhtiari, Hamed
  • Zhong, Jin
  • Alvarez, Manuel

Abstract

The accuracy of models to capture the uncertainty of renewables significantly affects the planning and operation of renewable energy-based stand-alone (REB-SA) microgrids. This paper aims to first study different stochastic and deterministic models for renewables, then evaluate the performance of an REB-SA microgrid planning problem and provide qualitative and quantitative comparisons. A modified Metropolis-coupled Markov chain Monte Carlo simulation is considered for the first time in the planning of an REB-SA microgrid to predict the behavior of renewables with minimum iterations. The modified model is benchmarked against two prevalent models including the retrospective model with worst-case scenarios and the Monte Carlo simulation. The operations of three designed microgrids (by these three methods) are evaluated using the last three-year historical data of a city in northern Sweden including solar radiation, wind speed, the water flow of a river, and load consumption. The impacts of the considered methods on using PV panels and hydrogen systems are investigated. The results verify that the modified model decreases the risk of planning and operation of an REB-SA microgrid from the energy and power shortage viewpoints. Moreover, the designed microgrid with the modified model can cope with all possible scenarios from economic, technical, and environmental viewpoints.

Suggested Citation

  • Bakhtiari, Hamed & Zhong, Jin & Alvarez, Manuel, 2022. "Uncertainty modeling methods for risk-averse planning and operation of stand-alone renewable energy-based microgrids," Renewable Energy, Elsevier, vol. 199(C), pages 866-880.
    • Handle: RePEc:eee:renene:v:199:y:2022:i:c:p:866-880
    DOI: 10.1016/j.renene.2022.09.040
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122013908
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.09.040?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Adefarati, T. & Bansal, R.C., 2017. "Reliability assessment of distribution system with the integration of renewable distributed generation," Applied Energy, Elsevier, vol. 185(P1), pages 158-171.
    2. Bérubé, Jean-François & Gendreau, Michel & Potvin, Jean-Yves, 2009. "An exact [epsilon]-constraint method for bi-objective combinatorial optimization problems: Application to the Traveling Salesman Problem with Profits," European Journal of Operational Research, Elsevier, vol. 194(1), pages 39-50, April.
    3. Hakimi, Seyed Mehdi & Hasankhani, Arezoo & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Stochastic planning of a multi-microgrid considering integration of renewable energy resources and real-time electricity market," Applied Energy, Elsevier, vol. 298(C).
    4. Nikmehr, Nima & Najafi-Ravadanegh, Sajad & Khodaei, Amin, 2017. "Probabilistic optimal scheduling of networked microgrids considering time-based demand response programs under uncertainty," Applied Energy, Elsevier, vol. 198(C), pages 267-279.
    5. Collins, Seán & Deane, John Paul & Poncelet, Kris & Panos, Evangelos & Pietzcker, Robert C. & Delarue, Erik & Ó Gallachóir, Brian Pádraig, 2017. "Integrating short term variations of the power system into integrated energy system models: A methodological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 839-856.
    6. Fonseca, Juan D. & Commenge, Jean-Marc & Camargo, Mauricio & Falk, Laurent & Gil, Iván D., 2021. "Multi-criteria optimization for the design and operation of distributed energy systems considering sustainability dimensions," Energy, Elsevier, vol. 214(C).
    7. Hemmati, Reza & Saboori, Hedayat & Siano, Pierluigi, 2017. "Coordinated short-term scheduling and long-term expansion planning in microgrids incorporating renewable energy resources and energy storage systems," Energy, Elsevier, vol. 134(C), pages 699-708.
    8. Pereira, Sérgio & Ferreira, Paula & Vaz, A.I.F., 2017. "Generation expansion planning with high share of renewables of variable output," Applied Energy, Elsevier, vol. 190(C), pages 1275-1288.
    9. Iris, Çağatay & Lam, Jasmine Siu Lee, 2021. "Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty," Omega, Elsevier, vol. 103(C).
    10. Mah, Angel Xin Yee & Ho, Wai Shin & Hassim, Mimi H. & Hashim, Haslenda & Ling, Gabriel Hoh Teck & Ho, Chin Siong & Muis, Zarina Ab, 2021. "Optimization of a standalone photovoltaic-based microgrid with electrical and hydrogen loads," Energy, Elsevier, vol. 235(C).
    11. 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.
    12. Poncelet, Kris & Delarue, Erik & Six, Daan & Duerinck, Jan & D’haeseleer, William, 2016. "Impact of the level of temporal and operational detail in energy-system planning models," Applied Energy, Elsevier, vol. 162(C), pages 631-643.
    13. Bakhtiari, Hamed & Zhong, Jin & Alvarez, Manuel, 2021. "Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation," Applied Energy, Elsevier, vol. 290(C).
    14. Zakaria, A. & Ismail, Firas B. & Lipu, M.S. Hossain & Hannan, M.A., 2020. "Uncertainty models for stochastic optimization in renewable energy applications," Renewable Energy, Elsevier, vol. 145(C), pages 1543-1571.
    15. Matti Koivisto & Kaushik Das & Feng Guo & Poul Sørensen & Edgar Nuño & Nicolaos Cutululis & Petr Maule, 2019. "Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(3), May.
    16. Soroudi, Alireza & Amraee, Turaj, 2013. "Decision making under uncertainty in energy systems: State of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 376-384.
    17. Chen, Xianqing & Dong, Wei & Yang, Qiang, 2022. "Robust optimal capacity planning of grid-connected microgrid considering energy management under multi-dimensional uncertainties," Applied Energy, Elsevier, vol. 323(C).
    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. Vladislav Volnyi & Pavel Ilyushin & Konstantin Suslov & Sergey Filippov, 2023. "Approaches to Building AC and AC–DC Microgrids on Top of Existing Passive Distribution Networks," Energies, MDPI, vol. 16(15), 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. Bakhtiari, Hamed & Zhong, Jin & Alvarez, Manuel, 2021. "Predicting the stochastic behavior of uncertainty sources in planning a stand-alone renewable energy-based microgrid using Metropolis–coupled Markov chain Monte Carlo simulation," Applied Energy, Elsevier, vol. 290(C).
    2. Vrionis, Constantinos & Tsalavoutis, Vasilios & Tolis, Athanasios, 2020. "A Generation Expansion Planning model for integrating high shares of renewable energy: A Meta-Model Assisted Evolutionary Algorithm approach," Applied Energy, Elsevier, vol. 259(C).
    3. Niina Helistö & Juha Kiviluoma & Hannele Holttinen & Jose Daniel Lara & Bri‐Mathias Hodge, 2019. "Including operational aspects in the planning of power systems with large amounts of variable generation: A review of modeling approaches," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 8(5), September.
    4. Poncelet, Kris & Delarue, Erik & D’haeseleer, William, 2020. "Unit commitment constraints in long-term planning models: Relevance, pitfalls and the role of assumptions on flexibility," Applied Energy, Elsevier, vol. 258(C).
    5. Sakki, G.K. & Tsoukalas, I. & Kossieris, P. & Makropoulos, C. & Efstratiadis, A., 2022. "Stochastic simulation-optimization framework for the design and assessment of renewable energy systems under uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    6. 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.
    7. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    8. Collins, Seán & Deane, J.P. & Ó Gallachóir, Brian, 2017. "Adding value to EU energy policy analysis using a multi-model approach with an EU-28 electricity dispatch model," Energy, Elsevier, vol. 130(C), pages 433-447.
    9. Chang, Miguel & Lund, Henrik & Thellufsen, Jakob Zinck & Østergaard, Poul Alberg, 2023. "Perspectives on purpose-driven coupling of energy system models," Energy, Elsevier, vol. 265(C).
    10. Elkholy, M.H. & Metwally, Hamid & Farahat, M.A. & Senjyu, Tomonobu & Elsayed Lotfy, Mohammed, 2022. "Smart centralized energy management system for autonomous microgrid using FPGA," Applied Energy, Elsevier, vol. 317(C).
    11. Dagoumas, Athanasios S. & Koltsaklis, Nikolaos E., 2019. "Review of models for integrating renewable energy in the generation expansion planning," Applied Energy, Elsevier, vol. 242(C), pages 1573-1587.
    12. Yazdanie, M. & Orehounig, K., 2021. "Advancing urban energy system planning and modeling approaches: Gaps and solutions in perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    13. Sun, Xu & Liu, Yanli & Deng, Liangchen, 2020. "Reliability assessment of cyber-physical distribution network based on the fault tree," Renewable Energy, Elsevier, vol. 155(C), pages 1411-1424.
    14. Oree, Vishwamitra & Sayed Hassen, Sayed Z. & Fleming, Peter J., 2019. "A multi-objective framework for long-term generation expansion planning with variable renewables," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    15. Deveci, Kaan & Güler, Önder, 2020. "A CMOPSO based multi-objective optimization of renewable energy planning: Case of Turkey," Renewable Energy, Elsevier, vol. 155(C), pages 578-590.
    16. 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.
    17. Seljom, Pernille & Rosenberg, Eva & Haaskjold, Kristina, 2024. "The effect and value of end-use flexibility in the low-carbon transition of the energy system," Energy, Elsevier, vol. 292(C).
    18. Ringkjøb, Hans-Kristian & Haugan, Peter M. & Seljom, Pernille & Lind, Arne & Wagner, Fabian & Mesfun, Sennai, 2020. "Short-term solar and wind variability in long-term energy system models - A European case study," Energy, Elsevier, vol. 209(C).
    19. Seljom, Pernille & Kvalbein, Lisa & Hellemo, Lars & Kaut, Michal & Ortiz, Miguel Muñoz, 2021. "Stochastic modelling of variable renewables in long-term energy models: Dataset, scenario generation & quality of results," Energy, Elsevier, vol. 236(C).
    20. Thomas T. D. Tran & Amanda D. Smith, 2019. "Stochastic Optimization for Integration of Renewable Energy Technologies in District Energy Systems for Cost-Effective Use," Energies, MDPI, vol. 12(3), pages 1-26, February.

    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:eee:renene:v:199:y:2022:i:c:p:866-880. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.