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Multiobjective Optimization of a Hybrid PV/Wind/Battery/Diesel Generator System Integrated in Microgrid: A Case Study in Djelfa, Algeria

Author

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  • Zakaria Belboul

    (Laboratory of Applied Automation and Industrial Diagnostics (LAADI), Faculty of Science and Technology, Ziane Achour University, Djelfa 17000, Algeria)

  • Belgacem Toual

    (Laboratory of Applied Automation and Industrial Diagnostics (LAADI), Faculty of Science and Technology, Ziane Achour University, Djelfa 17000, Algeria)

  • Abdellah Kouzou

    (Laboratory of Applied Automation and Industrial Diagnostics (LAADI), Faculty of Science and Technology, Ziane Achour University, Djelfa 17000, Algeria
    Electrical and Electronics Engineering Department, Nisantasi University, Istanbul 34398, Turkey
    Institute for High-Power Converter Systems (HLU), Technical University of Munich (TUM), 80333 Munich, Germany)

  • Lakhdar Mokrani

    (LACoSERE Laboratory, Department of Electrical Engineering, Faculty of Technology, University Amar Telidji of Laghouat, Laghouat 03000, Algeria)

  • Abderrahman Bensalem

    (Laboratory of Applied Automation and Industrial Diagnostics (LAADI), Faculty of Science and Technology, Ziane Achour University, Djelfa 17000, Algeria)

  • Ralph Kennel

    (Institute for High-Power Converter Systems (HLU), Technical University of Munich (TUM), 80333 Munich, Germany)

  • Mohamed Abdelrahem

    (Institute for High-Power Converter Systems (HLU), Technical University of Munich (TUM), 80333 Munich, Germany
    Department of Electrical Engineering, Assiut University, Assiut 71516, Egypt)

Abstract

Hybrid Renewable Energy Sources (HRES) integrated into a microgrid (MG) are a cost-effective and convenient solution to supply energy to off-grid and rural areas in developing countries. This research paper focuses on the optimization of an HRES connected to a stand-alone microgrid system consisting of photovoltaics (PV), wind turbines (WT), batteries (BT), diesel generators (DG), and inverters to meet the energy demand of fifteen residential housing units in the city of Djelfa, Algeria. In this context, the multiobjective salp swarm algorithm (MOSSA), which is among the latest nature-inspired metaheuristic algorithms recently introduced for hybrid microgrid system (HMS) optimization, has been proposed in this paper for solving the optimization of an isolated HRES. The proposed multiobjective optimization problem takes into account the cost of energy (COE) and loss of power supply probability (LPSP) as objective functions. The proposed approach is applied to determine three design variables, which are the nominal power of photovoltaic, the number of wind turbines, and the number of battery autonomy days considering higher reliability and minimum COE. In order to perform the optimum size of HMG, MOSSA is combined with a rule-based energy management strategy (EMS). The role of EMS is the coordination of the energy flow between different system components. The effectiveness of using MOSSA in addressing the optimization issue is investigated by comparing its performance with that of the multiobjective dragonfly algorithm (MODA), multiobjective grasshopper optimization algorithm (MOGOA), and multiobjective ant lion optimizer (MOALO). The MATLAB environment is used to simulate HMS. Simulation results confirm that MOSSA achieves the optimum system size as it contributed 0.255 USD/kW h of COE and LPSP of 27.079% compared to MODA, MOGOA, and MOALO. In addition, the optimization results obtained using the proposed method provided a set of design solutions for the HMS, which will help designers select the optimal solution for the HMS.

Suggested Citation

  • Zakaria Belboul & Belgacem Toual & Abdellah Kouzou & Lakhdar Mokrani & Abderrahman Bensalem & Ralph Kennel & Mohamed Abdelrahem, 2022. "Multiobjective Optimization of a Hybrid PV/Wind/Battery/Diesel Generator System Integrated in Microgrid: A Case Study in Djelfa, Algeria," Energies, MDPI, vol. 15(10), pages 1-30, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:10:p:3579-:d:815059
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    References listed on IDEAS

    as
    1. Borhanazad, Hanieh & Mekhilef, Saad & Gounder Ganapathy, Velappa & Modiri-Delshad, Mostafa & Mirtaheri, Ali, 2014. "Optimization of micro-grid system using MOPSO," Renewable Energy, Elsevier, vol. 71(C), pages 295-306.
    2. Lujano-Rojas, Juan M. & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2013. "Probabilistic modelling and analysis of stand-alone hybrid power systems," Energy, Elsevier, vol. 63(C), pages 19-27.
    3. Fauzan Hanif Jufri & Dwi Riana Aryani & Iwa Garniwa & Budi Sudiarto, 2021. "Optimal Battery Energy Storage Dispatch Strategy for Small-Scale Isolated Hybrid Renewable Energy System with Different Load Profile Patterns," Energies, MDPI, vol. 14(11), pages 1-19, May.
    4. Farrugia, R.N., 2003. "The wind shear exponent in a Mediterranean island climate," Renewable Energy, Elsevier, vol. 28(4), pages 647-653.
    5. Sanajaoba, Sarangthem & Fernandez, Eugene, 2016. "Maiden application of Cuckoo Search algorithm for optimal sizing of a remote hybrid renewable energy System," Renewable Energy, Elsevier, vol. 96(PA), pages 1-10.
    6. Ramli, Makbul A.M. & Bouchekara, H.R.E.H. & Alghamdi, Abdulsalam S., 2018. "Optimal sizing of PV/wind/diesel hybrid microgrid system using multi-objective self-adaptive differential evolution algorithm," Renewable Energy, Elsevier, vol. 121(C), pages 400-411.
    7. Hassan M. H. Farh & Abdullrahman A. Al-Shamma’a & Abdullah M. Al-Shaalan & Abdulaziz Alkuhayli & Abdullah M. Noman & Tarek Kandil, 2022. "Technical and Economic Evaluation for Off-Grid Hybrid Renewable Energy System Using Novel Bonobo Optimizer," Sustainability, MDPI, vol. 14(3), pages 1-18, January.
    8. Zhang, Xiongwen & Tan, Siew-Chong & Li, Guojun & Li, Jun & Feng, Zhenping, 2013. "Components sizing of hybrid energy systems via the optimization of power dispatch simulations," Energy, Elsevier, vol. 52(C), pages 165-172.
    9. Fathy, Ahmed, 2015. "Reliable and efficient approach for mitigating the shading effect on photovoltaic module based on Modified Artificial Bee Colony algorithm," Renewable Energy, Elsevier, vol. 81(C), pages 78-88.
    10. Dalton, G.J. & Lockington, D.A. & Baldock, T.E., 2009. "Case study feasibility analysis of renewable energy supply options for small to medium-sized tourist accommodations," Renewable Energy, Elsevier, vol. 34(4), pages 1134-1144.
    11. El Sehiemy, Ragab A. & Selim, F. & Bentouati, Bachir & Abido, M.A., 2020. "A novel multi-objective hybrid particle swarm and salp optimization algorithm for technical-economical-environmental operation in power systems," Energy, Elsevier, vol. 193(C).
    12. Anoune, Kamal & Bouya, Mohsine & Astito, Abdelali & Abdellah, Abdellatif Ben, 2018. "Sizing methods and optimization techniques for PV-wind based hybrid renewable energy system: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 652-673.
    13. Mahmoud, Marwan M. & Ibrik, Imad H., 2006. "Techno-economic feasibility of energy supply of remote villages in Palestine by PV-systems, diesel generators and electric grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 10(2), pages 128-138, April.
    14. Azoumah, Y. & Yamegueu, D. & Ginies, P. & Coulibaly, Y. & Girard, P., 2011. "Sustainable electricity generation for rural and peri-urban populations of sub-Saharan Africa: The "flexy-energy" concept," Energy Policy, Elsevier, vol. 39(1), pages 131-141, January.
    15. Sinha, Sunanda & Chandel, S.S., 2015. "Review of recent trends in optimization techniques for solar photovoltaic–wind based hybrid energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 755-769.
    16. Jiyang Wang & Yuyang Gao & Xuejun Chen, 2018. "A Novel Hybrid Interval Prediction Approach Based on Modified Lower Upper Bound Estimation in Combination with Multi-Objective Salp Swarm Algorithm for Short-Term Load Forecasting," Energies, MDPI, vol. 11(6), pages 1-30, June.
    17. Deshmukh, M.K. & Deshmukh, S.S., 2008. "Modeling of hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 12(1), pages 235-249, January.
    18. Rehman, Shafiqur & Al-Abbadi, Naif M., 2007. "Wind shear coefficients and energy yield for Dhahran, Saudi Arabia," Renewable Energy, Elsevier, vol. 32(5), pages 738-749.
    19. Guangqian, Du & Bekhrad, Kaveh & Azarikhah, Pouria & Maleki, Akbar, 2018. "A hybrid algorithm based optimization on modeling of grid independent biodiesel-based hybrid solar/wind systems," Renewable Energy, Elsevier, vol. 122(C), pages 551-560.
    20. Jaramillo, O.A. & Borja, M.A., 2004. "Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case," Renewable Energy, Elsevier, vol. 29(10), pages 1613-1630.
    21. Mohammed Kharrich & Salah Kamel & Ali S. Alghamdi & Ahmad Eid & Mohamed I. Mosaad & Mohammed Akherraz & Mamdouh Abdel-Akher, 2021. "Optimal Design of an Isolated Hybrid Microgrid for Enhanced Deployment of Renewable Energy Sources in Saudi Arabia," Sustainability, MDPI, vol. 13(9), pages 1-26, April.
    22. Fathabadi, Hassan, 2017. "Novel grid-connected solar/wind powered electric vehicle charging station with vehicle-to-grid technology," Energy, Elsevier, vol. 132(C), pages 1-11.
    23. Sukamongkol, Y. & Chungpaibulpatana, S. & Ongsakul, W., 2002. "A simulation model for predicting the performance of a solar photovoltaic system with alternating current loads," Renewable Energy, Elsevier, vol. 27(2), pages 237-258.
    24. Daud, Abdel-Karim & Ismail, Mahmoud S., 2012. "Design of isolated hybrid systems minimizing costs and pollutant emissions," Renewable Energy, Elsevier, vol. 44(C), pages 215-224.
    25. Sinha, Sunanda & Chandel, S.S., 2014. "Review of software tools for hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 192-205.
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    5. Ahmed, Eihab E.E. & Demirci, Alpaslan & Tercan, Said Mirza, 2023. "Optimal sizing and techno-enviro-economic feasibility assessment of solar tracker-based hybrid energy systems for rural electrification in Sudan," Renewable Energy, Elsevier, vol. 205(C), pages 1057-1070.

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