IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i5p2892-d762271.html
   My bibliography  Save this article

Optimal Planning of Remote Microgrids with Multi-Size Split-Diesel Generators

Author

Listed:
  • Gabriel Andres Rojas Cardenas

    (STEM, University of South Australia, Adelaide, SA 5095, Australia)

  • Rahmat Khezri

    (College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia)

  • Amin Mahmoudi

    (College of Science and Engineering, Flinders University, Adelaide, SA 5042, Australia)

  • Solmaz Kahourzadeh

    (STEM, University of South Australia, Adelaide, SA 5095, Australia)

Abstract

This paper proposes a multi-size Split-diesel generator (Split-DG) model with three different sizes of DGs and more switching configurations compared to the existing split-DG models. The proposed multi-size Split-DG system is examined for optimal sizing of remote microgrids with and without renewable-battery system. As a novel concept, multi-size Split-DG is used to reduce contamination, cost, and dumped power by using multiple small DGs to replace the single-size large DG. As another contribution of this study, a practical model is developed by considering the capacity degradation of components, spinning reserve, as well as DG’s and fuel tank’s constraints. The optimization problem is solved using a variable weighting particle swarm optimization (VW-PSO) algorithm. The effectiveness of the proposed Split-DG systems, optimized by the developed VW-PSO, is verified by comparing the results with conventional single-size DG system and the system optimized by conventional PSO. While the formulated optimization problem is general and can be used for any remote microgrids, an aboriginal community in South Australia is examined in this study. For this purpose, realistic data of load and weather, as well as technical and economic data of components, are used. It is found that the Split-DG-PV-WT-BES system has the lowest electricity cost compared to the systems without BES, or without PV and WT.

Suggested Citation

  • Gabriel Andres Rojas Cardenas & Rahmat Khezri & Amin Mahmoudi & Solmaz Kahourzadeh, 2022. "Optimal Planning of Remote Microgrids with Multi-Size Split-Diesel Generators," Sustainability, MDPI, vol. 14(5), pages 1-25, March.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:5:p:2892-:d:762271
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/5/2892/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/5/2892/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zahedi, A., 2011. "Maximizing solar PV energy penetration using energy storage technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(1), pages 866-870, January.
    2. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2016. "Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building," Applied Energy, Elsevier, vol. 171(C), pages 153-171.
    3. Zhang, Ge & Shi, Yong & Maleki, Akbar & A. Rosen, Marc, 2020. "Optimal location and size of a grid-independent solar/hydrogen system for rural areas using an efficient heuristic approach," Renewable Energy, Elsevier, vol. 156(C), pages 1203-1214.
    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. Koholé, Yemeli Wenceslas & Wankouo Ngouleu, Clint Ameri & Fohagui, Fodoup Cyrille Vincelas & Tchuen, Ghislain, 2024. "Optimization of an off-grid hybrid photovoltaic/wind/diesel/fuel cell system for residential applications power generation employing evolutionary algorithms," Renewable Energy, Elsevier, vol. 224(C).
    2. Li, Rong & Guo, Su & Yang, Yong & Liu, Deyou, 2020. "Optimal sizing of wind/ concentrated solar plant/ electric heater hybrid renewable energy system based on two-stage stochastic programming," Energy, Elsevier, vol. 209(C).
    3. Michael J. Fell & Alexandra Schneiders & David Shipworth, 2019. "Consumer Demand for Blockchain-Enabled Peer-to-Peer Electricity Trading in the United Kingdom: An Online Survey Experiment," Energies, MDPI, vol. 12(20), pages 1-25, October.
    4. Pregelj, Boštjan & Micor, Michał & Dolanc, Gregor & Petrovčič, Janko & Jovan, Vladimir, 2016. "Impact of fuel cell and battery size to overall system performance – A diesel fuel-cell APU case study," Applied Energy, Elsevier, vol. 182(C), pages 365-375.
    5. Nian Liu & Cheng Wang & Minyang Cheng & Jie Wang, 2016. "A Privacy-Preserving Distributed Optimal Scheduling for Interconnected Microgrids," Energies, MDPI, vol. 9(12), pages 1-18, December.
    6. Razavi, Seyed-Ehsan & Rahimi, Ehsan & Javadi, Mohammad Sadegh & Nezhad, Ali Esmaeel & Lotfi, Mohamed & Shafie-khah, Miadreza & Catalão, João P.S., 2019. "Impact of distributed generation on protection and voltage regulation of distribution systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 157-167.
    7. Ahmad Alzahrani & Senthil Kumar Ramu & Gunapriya Devarajan & Indragandhi Vairavasundaram & Subramaniyaswamy Vairavasundaram, 2022. "A Review on Hydrogen-Based Hybrid Microgrid System: Topologies for Hydrogen Energy Storage, Integration, and Energy Management with Solar and Wind Energy," Energies, MDPI, vol. 15(21), pages 1-32, October.
    8. 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.
    9. Nguyen, Hai Tra & Safder, Usman & Nhu Nguyen, X.Q. & Yoo, ChangKyoo, 2020. "Multi-objective decision-making and optimal sizing of a hybrid renewable energy system to meet the dynamic energy demands of a wastewater treatment plant," Energy, Elsevier, vol. 191(C).
    10. Mostafa Ahmed & Mohamed Abdelrahem & Ibrahim Harbi & Ralph Kennel, 2020. "An Adaptive Model-Based MPPT Technique with Drift-Avoidance for Grid-Connected PV Systems," Energies, MDPI, vol. 13(24), pages 1-25, December.
    11. Mahmud, Nasif & Zahedi, A., 2016. "Review of control strategies for voltage regulation of the smart distribution network with high penetration of renewable distributed generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 582-595.
    12. Talaat, M. & Farahat, M.A. & Elkholy, M.H., 2019. "Renewable power integration: Experimental and simulation study to investigate the ability of integrating wave, solar and wind energies," Energy, Elsevier, vol. 170(C), pages 668-682.
    13. Wen, Shuli & Lan, Hai & Hong, Ying-Yi & Yu, David C. & Zhang, Lijun & Cheng, Peng, 2016. "Allocation of ESS by interval optimization method considering impact of ship swinging on hybrid PV/diesel ship power system," Applied Energy, Elsevier, vol. 175(C), pages 158-167.
    14. Haesung Jo & Jaemin Park & Insu Kim, 2021. "Environmentally Constrained Optimal Dispatch Method for Combined Cooling, Heating, and Power Systems Using Two-Stage Optimization," Energies, MDPI, vol. 14(14), pages 1-20, July.
    15. Davoudkhani, Iraj Faraji & Dejamkhooy, Abdolmajid & Nowdeh, Saber Arabi, 2023. "A novel cloud-based framework for optimal design of stand-alone hybrid renewable energy system considering uncertainty and battery aging," Applied Energy, Elsevier, vol. 344(C).
    16. Wu, Yubo & Du, Jianqiang & Liu, Guangxin & Ma, Danzhu & Jia, Fengrui & Klemeš, Jiří Jaromír & Wang, Jin, 2022. "A review of self-cleaning technology to reduce dust and ice accumulation in photovoltaic power generation using superhydrophobic coating," Renewable Energy, Elsevier, vol. 185(C), pages 1034-1061.
    17. Li, Wei & Sun, Wen & Li, Guomin & Cui, Pengfei & Wu, Wen & Jin, Baihui, 2017. "Temporal and spatial heterogeneity of carbon intensity in China's construction industry," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 162-173.
    18. 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).
    19. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    20. Chabok, Hossein & Aghaei, Jamshid & Sheikh, Morteza & Roustaei, Mahmoud & Zare, Mohsen & Niknam, Taher & Lehtonen, Matti & Shafi-khah, Miadreza & Catalão, João P.S., 2022. "Transmission-constrained optimal allocation of price-maker wind-storage units in electricity markets," Applied Energy, Elsevier, vol. 310(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:jsusta:v:14:y:2022:i:5:p:2892-:d:762271. 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.