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PSO-Based Smart Grid Application for Sizing and Optimization of Hybrid Renewable Energy Systems

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  • Mohamed A Mohamed
  • Ali M Eltamaly
  • Abdulrahman I Alolah

Abstract

This paper introduces an optimal sizing algorithm for a hybrid renewable energy system using smart grid load management application based on the available generation. This algorithm aims to maximize the system energy production and meet the load demand with minimum cost and highest reliability. This system is formed by photovoltaic array, wind turbines, storage batteries, and diesel generator as a backup source of energy. Demand profile shaping as one of the smart grid applications is introduced in this paper using load shifting-based load priority. Particle swarm optimization is used in this algorithm to determine the optimum size of the system components. The results obtained from this algorithm are compared with those from the iterative optimization technique to assess the adequacy of the proposed algorithm. The study in this paper is performed in some of the remote areas in Saudi Arabia and can be expanded to any similar regions around the world. Numerous valuable results are extracted from this study that could help researchers and decision makers.

Suggested Citation

  • Mohamed A Mohamed & Ali M Eltamaly & Abdulrahman I Alolah, 2016. "PSO-Based Smart Grid Application for Sizing and Optimization of Hybrid Renewable Energy Systems," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-22, August.
    • Handle: RePEc:plo:pone00:0159702
    DOI: 10.1371/journal.pone.0159702
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    Cited by:

    1. 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.
    2. Ghaith, Ahmad F. & Epplin, Francis M. & Frazier, R. Scott, 2017. "Economics of grid-tied household solar panel systems versus grid-only electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 407-424.
    3. Zehua Wang & Fachao Liang & Sheng-Hau Lin, 2023. "Can socially sustainable development be achieved through homestead withdrawal? A hybrid multiple-attributes decision analysis," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-18, December.
    4. Mohammad AlHashmi & Gyan Chhipi-Shrestha & Kh Md. Nahiduzzaman & Kasun Hewage & Rehan Sadiq, 2021. "Framework for Developing a Low-Carbon Energy Demand in Residential Buildings Using Community-Government Partnership: An Application in Saudi Arabia," Energies, MDPI, vol. 14(16), pages 1-36, August.
    5. Zhang, M.M. & Zhou, D.Q. & Zhou, P. & Chen, H.T., 2017. "Optimal design of subsidy to stimulate renewable energy investments: The case of China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 873-883.
    6. Suchitra Dayalan & Sheikh Suhaib Gul & Rajarajeswari Rathinam & George Fernandez Savari & Shady H. E. Abdel Aleem & Mohamed A. Mohamed & Ziad M. Ali, 2022. "Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization," Sustainability, MDPI, vol. 14(17), pages 1-25, September.
    7. Galindo Noguera, Ana Lisbeth & Mendoza Castellanos, Luis Sebastian & Silva Lora, Electo Eduardo & Melian Cobas, Vladimir Rafael, 2018. "Optimum design of a hybrid diesel-ORC / photovoltaic system using PSO: Case study for the city of Cujubim, Brazil," Energy, Elsevier, vol. 142(C), pages 33-45.
    8. Mohamed, Mohamed A. & Eltamaly, Ali M. & Alolah, Abdulrahman I., 2017. "Swarm intelligence-based optimization of grid-dependent hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 515-524.
    9. Omer Saleem & Shehryaar Ali & Jamshed Iqbal, 2023. "Robust MPPT Control of Stand-Alone Photovoltaic Systems via Adaptive Self-Adjusting Fractional Order PID Controller," Energies, MDPI, vol. 16(13), pages 1-20, June.
    10. Mohamed, Mohamed A. & Tajik, Elham & Awwad, Emad Mahrous & El-Sherbeeny, Ahmed M. & Elmeligy, Mohammed A. & Ali, Ziad M., 2020. "A two-stage stochastic framework for effective management of multiple energy carriers," Energy, Elsevier, vol. 197(C).
    11. Mohamed, Mohamed A. & Zaki Diab, Ahmed A. & Rezk, Hegazy, 2019. "Partial shading mitigation of PV systems via different meta-heuristic techniques," Renewable Energy, Elsevier, vol. 130(C), pages 1159-1175.
    12. Mohamed, Mohamed A. & Chabok, Hossein & Awwad, Emad Mahrous & El-Sherbeeny, Ahmed M. & Elmeligy, Mohammed A. & Ali, Ziad M., 2020. "Stochastic and distributed scheduling of shipboard power systems using MθFOA-ADMM," Energy, Elsevier, vol. 206(C).

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