IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i2p725-d1569695.html

Performance Improvement of a Standalone Hybrid Renewable Energy System Using a Bi-Level Predictive Optimization Technique

Author

Listed:
  • Ayman Al-Quraan

    (Electrical Power Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan)

  • Bashar Al-Mharat

    (Electrical Power Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan)

  • Ahmed Koran

    (Electrical Power Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan)

  • Ashraf Ghassab Radaideh

    (Electrical Power Engineering Department, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan)

Abstract

A standalone hybrid renewable energy system (HRES) that combines different types of renewable energy sources and storages offers a sustainable energy solution by reducing reliance on fossil fuels and minimizing greenhouse gas emissions. In this paper, a standalone hybrid renewable energy system (HRES) involving wind turbines, photovoltaic (PV) modules, diesel generators (DG), and battery banks is proposed. For this purpose, it is necessary to size and run the proposed system for feeding a residential load satisfactorily. For two typical winter and summer weeks, weather historical data, including irradiance, temperature, wind speed, and load profiles, are used as input data. The overall optimization framework is formulated as a bi-level mixed-integer nonlinear programming (BMINLP) problem. The upper-level part represents the sizing sub-problem that is solved based on economic and environmental multi-objectives. The lower-level part represents the energy management strategy (EMS) sub-problem. The EMS task utilizes the model predictive control (MPC) approach to achieve optimal technoeconomic operational performance. By the definition of BMINLP, the EMS sub-problem is defined within the constraints of the sizing sub-problem. The MATLAB R2023a environment is employed to execute and extract the results of the entire problem. The global optimization solver “ga” is utilized to implement the upper sub-problem while the “intlinprg” solver solves the lower sub-problem. The evaluation metrics used in this study are the operating, maintenance, and investment costs, storage unit degradation, and the number of CO 2 emissions.

Suggested Citation

  • Ayman Al-Quraan & Bashar Al-Mharat & Ahmed Koran & Ashraf Ghassab Radaideh, 2025. "Performance Improvement of a Standalone Hybrid Renewable Energy System Using a Bi-Level Predictive Optimization Technique," Sustainability, MDPI, vol. 17(2), pages 1-22, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:2:p:725-:d:1569695
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/2/725/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/2/725/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Walmsley, Timothy Gordon & Philipp, Matthias & Picón-Núñez, Martín & Meschede, Henning & Taylor, Matthew Thomas & Schlosser, Florian & Atkins, Martin John, 2023. "Hybrid renewable energy utility systems for industrial sites: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    2. Liu, Wen Hui & Wan Alwi, Sharifah Rafidah & Hashim, Haslenda & Lim, Jeng Shiun & Mohammad Rozali, Nor Erniza & Ho, Wai Shin, 2016. "Sizing of Hybrid Power System with varying current type using numerical probabilistic approach," Applied Energy, Elsevier, vol. 184(C), pages 1364-1373.
    3. Jiao, P.H. & Chen, J.J. & Peng, K. & Zhao, Y.L. & Xin, K.F., 2020. "Multi-objective mean-semi-entropy model for optimal standalone micro-grid planning with uncertain renewable energy resources," Energy, Elsevier, vol. 191(C).
    4. Iqbal, Rashid & Liu, Yancheng & Zeng, Yuji & Zhang, Qinjin & Zeeshan, Muhammad, 2024. "Comparative study based on techno-economics analysis of different shipboard microgrid systems comprising PV/wind/fuel cell/battery/diesel generator with two battery technologies: A step toward green maritime transportation," Renewable Energy, Elsevier, vol. 221(C).
    5. Madhlopa, Amos & Sparks, Debbie & Keen, Samantha & Moorlach, Mascha & Krog, Pieter & Dlamini, Thuli, 2015. "Optimization of a PV–wind hybrid system under limited water resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 324-331.
    6. Garcia, Raquel S. & Weisser, Daniel, 2006. "A wind–diesel system with hydrogen storage: Joint optimisation of design and dispatch," Renewable Energy, Elsevier, vol. 31(14), pages 2296-2320.
    7. Wu, Chenyu & Gu, Wei & Xu, Yinliang & Jiang, Ping & Lu, Shuai & Zhao, Bo, 2018. "Bi-level optimization model for integrated energy system considering the thermal comfort of heat customers," Applied Energy, Elsevier, vol. 232(C), pages 607-616.
    8. Wang, Delu & Liu, Yifei & Wang, Yadong & Shi, Xunpeng & Song, Xuefeng, 2020. "Allocation of coal de-capacity quota among provinces in China: A bi-level multi-objective combinatorial optimization approach," Energy Economics, Elsevier, vol. 87(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. Hooshmandian, Mehrnaz & Derakhshan, Ghasem & Hakimi, Seyed Mehdi & Rezaee Jordehi, Ahmad, 2025. "A two-stage framework for the joint planning and operation of battery-integrated renewable generation in microgrids coupled with energy hubs and electric vehicle parking lots," Energy, Elsevier, vol. 323(C).
    2. Dejkam, Rahil & Madlener, Reinhard, 2025. "Green mobility infrastructure: A techno-economic analysis of hybrid wind–solar PV charging stations for electric vehicles in Germany," Energy, Elsevier, vol. 334(C).

    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. Al-Quraan, A. & Al-Mhairat, B., 2024. "Economic predictive control-based sizing and energy management for grid-connected hybrid renewable energy systems," Energy, Elsevier, vol. 302(C).
    2. Xu, Jiuping & Shu, Kejing & Wang, Fengjuan & Yang, Guocan, 2024. "Bi-level multi-objective distribution strategy integrating the permit trading scheme towards coal production capacity layout optimization: Case study from China," Resources Policy, Elsevier, vol. 91(C).
    3. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
    4. Kashefi Kaviani, A. & Riahy, G.H. & Kouhsari, SH.M., 2009. "Optimal design of a reliable hydrogen-based stand-alone wind/PV generating system, considering component outages," Renewable Energy, Elsevier, vol. 34(11), pages 2380-2390.
    5. Akhlaque Ahmad Khan & Ahmad Faiz Minai & Rupendra Kumar Pachauri & Hasmat Malik, 2022. "Optimal Sizing, Control, and Management Strategies for Hybrid Renewable Energy Systems: A Comprehensive Review," Energies, MDPI, vol. 15(17), pages 1-29, August.
    6. Sachs, Julia & Sawodny, Oliver, 2016. "Multi-objective three stage design optimization for island microgrids," Applied Energy, Elsevier, vol. 165(C), pages 789-800.
    7. Wang, Yadong & Wang, Delu & Shi, Xunpeng, 2021. "Exploring the dilemma of overcapacity governance in China's coal industry: A tripartite evolutionary game model," Resources Policy, Elsevier, vol. 71(C).
    8. Abdulrahman AlKassem & Azeddine Draou & Abdullah Alamri & Hisham Alharbi, 2022. "Design Analysis of an Optimal Microgrid System for the Integration of Renewable Energy Sources at a University Campus," Sustainability, MDPI, vol. 14(7), pages 1-20, March.
    9. Duic, Neven & Krajacic, Goran & da Graça Carvalho, Maria, 2008. "RenewIslands methodology for sustainable energy and resource planning for islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(4), pages 1032-1062, May.
    10. Wang, Cheng & Liu, Chuang & Lin, Yuzhang & Bi, Tianshu, 2020. "Day-ahead dispatch of integrated electric-heat systems considering weather-parameter-driven residential thermal demands," Energy, Elsevier, vol. 203(C).
    11. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    12. Fadlallah, Sulaiman O. & Benhadji Serradj, Djamal Eddine & Sedzro, Delight M., 2021. "Is this the right time for Sudan to replace diesel-powered generator systems with wind turbines?," Renewable Energy, Elsevier, vol. 180(C), pages 40-54.
    13. Zhao, Chenyu & Wang, Chaoyang & Liu, Ming & Liu, Jiping & Yan, Junjie, 2025. "Cascade co-optimization of the load ramp-down capability considering the transient stored energy of subcritical CFB power units," Energy, Elsevier, vol. 332(C).
    14. Mohammed, Y.S. & Mustafa, M.W. & Bashir, N., 2014. "Hybrid renewable energy systems for off-grid electric power: Review of substantial issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 35(C), pages 527-539.
    15. Chaudhuri, Tanaya & Soh, Yeng Chai & Li, Hua & Xie, Lihua, 2019. "A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings," Applied Energy, Elsevier, vol. 248(C), pages 44-53.
    16. Mateusz Andrychowicz, 2021. "RES and ES Integration in Combination with Distribution Grid Development Using MILP," Energies, MDPI, vol. 14(2), pages 1-19, January.
    17. Yi’an Wang & Zhe Wu & Dong Ni, 2024. "Large-Scale Optimization among Photovoltaic and Concentrated Solar Power Systems: A State-of-the-Art Review and Algorithm Analysis," Energies, MDPI, vol. 17(17), pages 1-38, August.
    18. Shi, Zhengkun & Yang, Yongbiao & Xu, Qingshan & Wu, Chenyu & Hua, Kui, 2023. "A low-carbon economic dispatch for integrated energy systems with CCUS considering multi-time-scale allocation of carbon allowance," Applied Energy, Elsevier, vol. 351(C).
    19. Kalantari, Hosein & Sasmito, Agus P. & Ghoreishi-Madiseh, Seyed Ali, 2021. "An overview of directions for decarbonization of energy systems in cold climate remote mines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    20. Wang, Yadong & Wang, Delu & Shi, Xunpeng, 2022. "Exploring the multidimensional effects of China's coal de-capacity policy: A regression discontinuity design," Resources Policy, Elsevier, vol. 75(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:17:y:2025:i:2:p:725-:d:1569695. 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.