IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v382y2025ics0306261924026503.html
   My bibliography  Save this article

An innovative real-time framework for probabilistic load flow computation in renewable-based microgrids considering correlation: Integrating automatic data clustering with an enhanced arithmetic optimization algorithm

Author

Listed:
  • Javidan, Aghil
  • Lashkar Ara, Afshin
  • Bagheri Tolabi, Hajar

Abstract

This paper suggests a new approach, called automatic data clustering, using an enhanced arithmetic optimization algorithm (ADC-EAOA), for improving the probabilistic load flow (PLF) in microgrids (MGs) integrated with distributed generation resources (DGR). The proposed ADC-EAOA method aims to address real-time solution of the PLF in MGs based on DGR with output large fluctuations, across various states of MG operation. The ADC-EAOA approach achieves more robust clustering compared to traditional data clustering methods by optimizing two objectives: a) maximizing cohesion within clusters, and b) enhancing separation between clusters. The correlation modeling between input random variables (IRVs) comprehensively has been considered. Five strategies have been introduced to improve the arithmetic optimization algorithm (AOA) to stop the algorithm from being extremely greedy and avoid trapping in local optima. The effectiveness of the EAOA is firstly assessed utilizing the test functions CEC2005 and CEC2019 in comparison to well-known optimization algorithms. Subsequently, the EAOA and the suggested ADC-EAOA hybrid approach are applied on 69-node, 33-node, and 123-node MGs to optimize the cost function used in the PLF which is equal to the mismatch among computation power with scheduled power in each node. The outcomes of solving the PLF problem were contrasted with those of the Monte-Carlo Simulation (MCS), the AOA, the PSO, the K-means, and as well as the Fuzzy C-means in terms of precision and run-time. The simulation outcomes demonstrate that the suggested ADC-EAOA approach dramatically increases the processing speed while simultaneously maintaining a high degree of accuracy so that it can provide various advantages and facilities for real-time power system studies.

Suggested Citation

  • Javidan, Aghil & Lashkar Ara, Afshin & Bagheri Tolabi, Hajar, 2025. "An innovative real-time framework for probabilistic load flow computation in renewable-based microgrids considering correlation: Integrating automatic data clustering with an enhanced arithmetic optim," Applied Energy, Elsevier, vol. 382(C).
    • Handle: RePEc:eee:appene:v:382:y:2025:i:c:s0306261924026503
    DOI: 10.1016/j.apenergy.2024.125266
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924026503
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.125266?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. Mohamed, Mohamed A., 2022. "A relaxed consensus plus innovation based effective negotiation approach for energy cooperation between smart grid and microgrid," Energy, Elsevier, vol. 252(C).
    2. Ma, Houzhen & Liu, Chunyang & Zhao, Haoran & Zhang, Hengxu & Wang, Mengxue & Wang, Xiaobing, 2023. "A novel analytical unified energy flow calculation method for integrated energy systems based on holomorphic embedding," Applied Energy, Elsevier, vol. 344(C).
    3. Prusty, B Rajanarayan & Jena, Debashisha, 2017. "A critical review on probabilistic load flow studies in uncertainty constrained power systems with photovoltaic generation and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1286-1302.
    4. Kabir, M.N. & Mishra, Y. & Bansal, R.C., 2016. "Probabilistic load flow for distribution systems with uncertain PV generation," Applied Energy, Elsevier, vol. 163(C), pages 343-351.
    5. Hasanien, Hany M. & Shaheen, Mohamed A.M. & Turky, Rania A. & Qais, Mohammed H. & Alghuwainem, Saad & Kamel, Salah & Tostado-Véliz, Marcos & Jurado, Francisco, 2022. "Precise modeling of PEM fuel cell using a novel Enhanced Transient Search Optimization algorithm," Energy, Elsevier, vol. 247(C).
    6. Aien, Morteza & Rashidinejad, Masoud & Firuz-Abad, Mahmud Fotuhi, 2015. "Probabilistic optimal power flow in correlated hybrid wind-PV power systems: A review and a new approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1437-1446.
    7. Esmaeli, Abdolreza & Abedini, Mohammad & Moradi, Mohammad H., 2016. "A novel power flow analysis in an islanded renewable microgrid," Renewable Energy, Elsevier, vol. 96(PA), pages 914-927.
    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. Samet, Haidar & Khorshidsavar, Morteza, 2018. "Analytic time series load flow," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3886-3899.
    2. Mohamed A. M. Shaheen & Hany M. Hasanien & Said F. Mekhamer & Mohammed H. Qais & Saad Alghuwainem & Zia Ullah & Marcos Tostado-Véliz & Rania A. Turky & Francisco Jurado & Mohamed R. Elkadeem, 2022. "Probabilistic Optimal Power Flow Solution Using a Novel Hybrid Metaheuristic and Machine Learning Algorithm," Mathematics, MDPI, vol. 10(17), pages 1-23, August.
    3. Ziqiang Zhou & Fei Tang & Dichen Liu & Chenxu Wang & Xin Gao, 2020. "Probabilistic Assessment of Distribution Network with High Penetration of Distributed Generators," Sustainability, MDPI, vol. 12(5), pages 1-20, February.
    4. Dichen Liu & Chenxu Wang & Fei Tang & Yixi Zhou, 2020. "Probabilistic Assessment of Hybrid Wind-PV Hosting Capacity in Distribution Systems," Sustainability, MDPI, vol. 12(6), pages 1-19, March.
    5. Xiaoyang Deng & Jinghan He & Pei Zhang, 2017. "A Novel Probabilistic Optimal Power Flow Method to Handle Large Fluctuations of Stochastic Variables," Energies, MDPI, vol. 10(10), pages 1-21, October.
    6. Mohammad Rayati & Pasquale De Falco & Daniela Proto & Mokhtar Bozorg & Mauro Carpita, 2021. "Generation Data of Synthetic High Frequency Solar Irradiance for Data-Driven Decision-Making in Electrical Distribution Grids," Energies, MDPI, vol. 14(16), pages 1-21, August.
    7. Harshavardhan Palahalli & Paolo Maffezzoni & Giambattista Gruosso, 2021. "Gaussian Copula Methodology to Model Photovoltaic Generation Uncertainty Correlation in Power Distribution Networks," Energies, MDPI, vol. 14(9), pages 1-16, April.
    8. Khaled Nusair & Lina Alhmoud, 2020. "Application of Equilibrium Optimizer Algorithm for Optimal Power Flow with High Penetration of Renewable Energy," Energies, MDPI, vol. 13(22), pages 1-35, November.
    9. Ben Christopher, S.J. & Carolin Mabel, M., 2020. "A bio-inspired approach for probabilistic energy management of micro-grid incorporating uncertainty in statistical cost estimation," Energy, Elsevier, vol. 203(C).
    10. Hamed Moazami Goodarzi & Mohammad Hosein Kazemi, 2017. "A Novel Optimal Control Method for Islanded Microgrids Based on Droop Control Using the ICA-GA Algorithm," Energies, MDPI, vol. 10(4), pages 1-17, April.
    11. Mei, Fei & Zhang, Jiatang & Lu, Jixiang & Lu, Jinjun & Jiang, Yuhan & Gu, Jiaqi & Yu, Kun & Gan, Lei, 2021. "Stochastic optimal operation model for a distributed integrated energy system based on multiple-scenario simulations," Energy, Elsevier, vol. 219(C).
    12. González-Ordiano, Jorge Ángel & Mühlpfordt, Tillmann & Braun, Eric & Liu, Jianlei & Çakmak, Hüseyin & Kühnapfel, Uwe & Düpmeier, Clemens & Waczowicz, Simon & Faulwasser, Timm & Mikut, Ralf & Hagenmeye, 2021. "Probabilistic forecasts of the distribution grid state using data-driven forecasts and probabilistic power flow," Applied Energy, Elsevier, vol. 302(C).
    13. Pei, Pucheng & Meng, Yining & Chen, Dongfang & Ren, Peng & Wang, Mingkai & Wang, Xizhong, 2023. "Lifetime prediction method of proton exchange membrane fuel cells based on current degradation law," Energy, Elsevier, vol. 265(C).
    14. Fathy, Ahmed & Rezk, Hegazy & Alharbi, Abdullah G. & Yousri, Dalia, 2023. "Proton exchange membrane fuel cell model parameters identification using Chaotically based-bonobo optimizer," Energy, Elsevier, vol. 268(C).
    15. Ziwei Zhu & Shifan Lu & Sui Peng, 2018. "An Improved Stochastic Response Surface Method Based Probabilistic Load Flow for Studies on Correlated Wind Speeds in the AC/DC Grid," Energies, MDPI, vol. 11(12), pages 1-14, December.
    16. O., Yugeswar Reddy & J., Jithendranath & Chakraborty, Ajoy Kumar & Guerrero, Josep M., 2022. "Stochastic optimal power flow in islanded DC microgrids with correlated load and solar PV uncertainties," Applied Energy, Elsevier, vol. 307(C).
    17. Andrew J. Riad & Hany M. Hasanien & Rania A. Turky & Ahmed H. Yakout, 2023. "Identifying the PEM Fuel Cell Parameters Using Artificial Rabbits Optimization Algorithm," Sustainability, MDPI, vol. 15(5), pages 1-17, March.
    18. Huafang Huang & Sharafat Ali & Yasir Ahmed Solangi, 2023. "Analysis of the Impact of Economic Policy Uncertainty on Environmental Sustainability in Developed and Developing Economies," Sustainability, MDPI, vol. 15(7), pages 1-19, March.
    19. Su, Huai & Chi, Lixun & Zio, Enrico & Li, Zhenlin & Fan, Lin & Yang, Zhe & Liu, Zhe & Zhang, Jinjun, 2021. "An integrated, systematic data-driven supply-demand side management method for smart integrated energy systems," Energy, Elsevier, vol. 235(C).
    20. Kumar, Navin & Sood, Sandeep Kumar & Saini, Munish, 2024. "Internet of Vehicles (IoV) Based Framework for electricity Demand Forecasting in V2G," Energy, Elsevier, vol. 297(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:eee:appene:v:382:y:2025:i:c:s0306261924026503. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.