IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v260y2022ics0360544222018047.html
   My bibliography  Save this article

An effective optimization strategy for design of standalone hybrid renewable energy systems

Author

Listed:
  • El-Sattar, Hoda Abd
  • Kamel, Salah
  • Hassan, Mohamed H.
  • Jurado, Francisco

Abstract

In this research, a hybrid algorithm called a Gradient Artificial Hummingbird Algorithm (GAHA) is developed for reducing energy cost (EC) of microgrid system; this hybrid approach is based on the combination between Gradient based optimizer (GBO) and Artificial Hummingbird Algorithm (AHA). This proposed GAHA is firstly tested on a group of 23 benchmark test functions, and its obtained results are compared with five well-known algorithms including supply-demand-based optimization (SDO), wild horse optimizer (WHO), grey wolf optimizer (GWO), tunicate swarm algorithm (TSA) and original AHA algorithm. The obtained results using the GAHA algorithm show better performance in most cases and comparative results in other cases. Moreover, the developed GAHA algorithm is applied to obtain the optimal configuration of an isolated hybrid system, which is consists of photovoltaic (PV) modules, wind turbine (WT), biomass system, and battery storage system. The proposed standalone hybrid system is implemented for feeding loads in the new Tiba city, in northeast of Luxor in southern Egypt. GAHA technology is applied to four different system configurations to obtain the optimal system design. The GAHA optimization findings are compared with the findings from other optimization algorithms namely the original AHA, Sine Cosine Algorithm (SCA), and whale optimization algorithm (WOA) methods. The GAHA method achieved the best results for all the suggested configuration scenarios compared to the rest of the algorithms used.

Suggested Citation

  • El-Sattar, Hoda Abd & Kamel, Salah & Hassan, Mohamed H. & Jurado, Francisco, 2022. "An effective optimization strategy for design of standalone hybrid renewable energy systems," Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:energy:v:260:y:2022:i:c:s0360544222018047
    DOI: 10.1016/j.energy.2022.124901
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222018047
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124901?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. Abdelhady Ramadan & Salah Kamel & Mohamed H. Hassan & Marcos Tostado-Véliz & Ali M. Eltamaly, 2021. "Parameter Estimation of Static/Dynamic Photovoltaic Models Using a Developed Version of Eagle Strategy Gradient-Based Optimizer," Sustainability, MDPI, vol. 13(23), pages 1-29, November.
    2. Fahd A. Alturki & Emad Mahrous Awwad, 2021. "Sizing and Cost Minimization of Standalone Hybrid WT/PV/Biomass/Pump-Hydro Storage-Based Energy Systems," Energies, MDPI, vol. 14(2), pages 1-20, January.
    3. Mohammed Kharrich & Salah Kamel & Mohamed H. Hassan & Salah K. ElSayed & Ibrahim B. M. Taha, 2021. "An Improved Heap-Based Optimizer for Optimal Design of a Hybrid Microgrid Considering Reliability and Availability Constraints," Sustainability, MDPI, vol. 13(18), pages 1-25, September.
    4. Weiqiang Dong & Yanjun Li & Ji Xiang, 2016. "Optimal Sizing of a Stand-Alone Hybrid Power System Based on Battery/Hydrogen with an Improved Ant Colony Optimization," Energies, MDPI, vol. 9(10), pages 1-17, September.
    5. Smarajit Ghosh & Vinod Karar, 2018. "Assimilation of Optimal Sized Hybrid Photovoltaic-Biomass System by Dragonfly Algorithm with Grid," Energies, MDPI, vol. 11(7), pages 1-19, July.
    6. Mazzeo, Domenico & Herdem, Münür Sacit & Matera, Nicoletta & Bonini, Matteo & Wen, John Z. & Nathwani, Jatin & Oliveti, Giuseppe, 2021. "Artificial intelligence application for the performance prediction of a clean energy community," Energy, Elsevier, vol. 232(C).
    7. Li, Jinze & Liu, Pei & Li, Zheng, 2020. "Optimal design and techno-economic analysis of a solar-wind-biomass off-grid hybrid power system for remote rural electrification: A case study of west China," Energy, Elsevier, vol. 208(C).
    8. Al-Sharafi, Abdullah & Sahin, Ahmet Z. & Ayar, Tahir & Yilbas, Bekir S., 2017. "Techno-economic analysis and optimization of solar and wind energy systems for power generation and hydrogen production in Saudi Arabia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 33-49.
    9. 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.
    10. Mazzeo, Domenico & Oliveti, Giuseppe & Baglivo, Cristina & Congedo, Paolo M., 2018. "Energy reliability-constrained method for the multi-objective optimization of a photovoltaic-wind hybrid system with battery storage," Energy, Elsevier, vol. 156(C), pages 688-708.
    11. Suzan Abdelhady & Mohamed A. Shalaby & Ahmed Shaban, 2021. "Techno-Economic Analysis for the Optimal Design of a National Network of Agro-Energy Biomass Power Plants in Egypt," Energies, MDPI, vol. 14(11), pages 1-26, May.
    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. El-Sattar, Hoda Abd & Hassan, Mohamed H. & Vera, David & Jurado, Francisco & Kamel, Salah, 2024. "Maximizing hybrid microgrid system performance: A comparative analysis and optimization using a gradient pelican algorithm," Renewable Energy, Elsevier, vol. 227(C).
    2. Falama, Ruben Zieba & Saidi, Abdelaziz Salah & Soulouknga, Marcel Hamda & Salah, Chokri Ben, 2023. "A techno-economic comparative study of renewable energy systems based different storage devices," Energy, Elsevier, vol. 266(C).
    3. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    4. Gowtham Vedulla & Anbazhagan Geetha & Ramalingam Senthil, 2022. "Review of Strategies to Mitigate Dust Deposition on Solar Photovoltaic Systems," Energies, MDPI, vol. 16(1), pages 1-28, December.
    5. Upasana Lakhina & Nasreen Badruddin & Irraivan Elamvazuthi & Ajay Jangra & Truong Hoang Bao Huy & Josep M. Guerrero, 2023. "An Enhanced Multi-Objective Optimizer for Stochastic Generation Optimization in Islanded Renewable Energy Microgrids," Mathematics, MDPI, vol. 11(9), pages 1-24, April.
    6. 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).

    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. Thirunavukkarasu, M. & Sawle, Yashwant & Lala, Himadri, 2023. "A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    2. Al-Shetwi, Ali Q. & Atawi, Ibrahem E. & Abuelrub, Ahmad & Hannan, M.A., 2023. "Techno-economic assessment and optimal design of hybrid power generation-based renewable energy systems," Technology in Society, Elsevier, vol. 75(C).
    3. Takele Ferede Agajie & Armand Fopah-Lele & Isaac Amoussou & Ahmed Ali & Baseem Khan & Emmanuel Tanyi, 2023. "Optimal Design and Mathematical Modeling of Hybrid Solar PV–Biogas Generator with Energy Storage Power Generation System in Multi-Objective Function Cases," Sustainability, MDPI, vol. 15(10), pages 1-26, May.
    4. Jurasz, Jakub & Guezgouz, Mohammed & Campana, Pietro E. & Kies, Alexander, 2022. "On the impact of load profile data on the optimization results of off-grid energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    5. Ahmed S. Menesy & Hamdy M. Sultan & Ibrahim O. Habiballah & Hasan Masrur & Kaisar R. Khan & Muhammad Khalid, 2023. "Optimal Configuration of a Hybrid Photovoltaic/Wind Turbine/Biomass/Hydro-Pumped Storage-Based Energy System Using a Heap-Based Optimization Algorithm," Energies, MDPI, vol. 16(9), pages 1-26, April.
    6. Durgamadhab Swain & Meera Viswavandya & Ritesh Dash & Kalvakurthi Jyotheeswara Reddy & Dhanamjayulu Chittathuru & Arunkumar Gopal & Baseem Khan & Manam Ravindra, 2023. "P2P Coordinated Control between SPV and STATCOM in a Microgrid for Power Quality Compensation Using LSTM–Genetic Algorithm," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    7. Lemence, Allen Lemuel G. & Tamayao, Mili-Ann M., 2021. "Energy consumption profile estimation and benefits of hybrid solar energy system adoption for rural health units in the Philippines," Renewable Energy, Elsevier, vol. 178(C), pages 651-668.
    8. Michael O. Ukoba & Ogheneruona E. Diemuodeke & Mohammed Alghassab & Henry I. Njoku & Muhammad Imran & Zafar A. Khan, 2020. "Composite Multi-Criteria Decision Analysis for Optimization of Hybrid Renewable Energy Systems for Geopolitical Zones in Nigeria," Sustainability, MDPI, vol. 12(14), pages 1-27, July.
    9. Wu, Xiao & Xi, Han & Qiu, Ruohan & Lee, Kwang Y., 2023. "Low carbon optimal planning of the steel mill gas utilization system," Applied Energy, Elsevier, vol. 343(C).
    10. Terfa, H. & Baghli, L. & Bhandari, R., 2022. "Impact of renewable energy micro-power plants on power grids over Africa," Energy, Elsevier, vol. 238(PA).
    11. Das, Pronob & Das, Barun K. & Rahman, Mushfiqur & Hassan, Rakibul, 2022. "Evaluating the prospect of utilizing excess energy and creating employments from a hybrid energy system meeting electricity and freshwater demands using multi-objective evolutionary algorithms," Energy, Elsevier, vol. 238(PB).
    12. Zhou, Yuekuan & Cao, Sunliang & Hensen, Jan L.M., 2021. "An energy paradigm transition framework from negative towards positive district energy sharing networks—Battery cycling aging, advanced battery management strategies, flexible vehicles-to-buildings in," Applied Energy, Elsevier, vol. 288(C).
    13. Pereira, Géssica Michelle dos Santos & Weigert, Gabriela Rosalee & Macedo, Pablo Lopes & Silva, Kiane Alves e & Segura Salas, Cresencio Silvio & Gonçalves, Antônio Maurício de Matos & Nascimento, Hebe, 2022. "Quasi-dynamic operation and maintenance plan for photovoltaic systems in remote areas: The framework of Pantanal-MS," Renewable Energy, Elsevier, vol. 181(C), pages 404-416.
    14. 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.
    15. Isabelo Rabuya & Melissa Libres & Michael Lochinvar Abundo & Evelyn Taboada, 2021. "Moving Up the Electrification Ladder in Off-Grid Settlements with Rooftop Solar Microgrids," Energies, MDPI, vol. 14(12), pages 1-32, June.
    16. Chinna Alluraiah Nallolla & Vijayapriya Perumal, 2022. "Optimal Design of a Hybrid Off-Grid Renewable Energy System Using Techno-Economic and Sensitivity Analysis for a Rural Remote Location," Sustainability, MDPI, vol. 14(22), pages 1-25, November.
    17. José Antonio Soriano & Reyes García-Contreras & Antonio José Carpio de Los Pinos, 2021. "Study of the Thermochemical Properties of Lignocellulosic Biomass from Energy Crops," Energies, MDPI, vol. 14(13), pages 1-18, June.
    18. D'Adamo, Idiano & Gastaldi, Massimo & Morone, Piergiuseppe & Ozturk, Ilhan, 2022. "Economics and policy implications of residential photovoltaic systems in Italy's developed market," Utilities Policy, Elsevier, vol. 79(C).
    19. Md. Arif Hossain & Ashik Ahmed & Shafiqur Rahman Tito & Razzaqul Ahshan & Taiyeb Hasan Sakib & Sarvar Hussain Nengroo, 2022. "Multi-Objective Hybrid Optimization for Optimal Sizing of a Hybrid Renewable Power System for Home Applications," Energies, MDPI, vol. 16(1), pages 1-19, December.
    20. Abdul Munim Rehmani & Syed Ali Abbas Kazmi & Abdullah Altamimi & Zafar A. Khan & Muhammad Awais, 2023. "Techno-Economic-Environmental Assessment of an Isolated Rural Micro-Grid from a Mid-Career Repowering Perspective," Sustainability, MDPI, vol. 15(3), pages 1-35, January.

    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:energy:v:260:y:2022:i:c:s0360544222018047. 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/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.