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Cryogenic-Energy-Storage-Based Optimized Green Growth of an Integrated and Sustainable Energy System

Author

Listed:
  • Muhammad Shahzad Nazir

    (Faculty of Automation, Huaiyin Institute of Technology, Huai’an 223003, China)

  • Ahmed N. Abdalla

    (Faculty of Information and Electronic Engineering, Huaiyin Institute of Technology, Huai’an 223003, China)

  • Ahmed Sayed M. Metwally

    (Department of Mathematics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia)

  • Muhammad Imran

    (Department of Biochemistry, Government College University Faisalabad, Faisalabad 38000, Pakistan)

  • Patrizia Bocchetta

    (Dipartimento di Ingegneria dell’Innovazione, Università del Salento, Via Monteroni, 73100 Lecce, Italy)

  • Muhammad Sufyan Javed

    (School of Physical Science and Technology, Lanzhou University, Lanzhou 730000, China)

Abstract

The advancement of using the cryogenic energy storage (CES) system has enabled efficient utilization of abandoned wind and solar energy, and the system can be dispatched in the peak hours of regional power load demand to release energy. It can fill the demand gap, which is conducive to the peak regulation of the power system and can further promote the rapid development of new energy. This study optimizes the various types of energy complementary to the CES system using hybrid gravitational search algorithm-local search optimization ( h GSA-LS). First, the mathematical model of the energy storage system (ESS) including the CES system is briefly described. Second, an economic scheduling optimization model of the IES is constructed by minimizing the operating cost of the system. Third, the h GSA-LS methods to solve the optimization problem are proposed. Simulations show that the h GSA-LS methodology is more efficient. The simulation results verify the feasibility of CES compared with traditional systems in terms of economic benefits, new energy consumption rate, primary energy saving rate, and carbon emissions under different fluctuations in energy prices. Optimization of the system operation using the proposed h GSA-LS algorithm takes 5.87 s; however, the GA, PSO, and GSA require 12.56, 10.33, and 7.95 s, respectively. Thus, the h GSA-LS algorithm shows a comparatively better performance than GA, PSO, and GSA in terms of time.

Suggested Citation

  • Muhammad Shahzad Nazir & Ahmed N. Abdalla & Ahmed Sayed M. Metwally & Muhammad Imran & Patrizia Bocchetta & Muhammad Sufyan Javed, 2022. "Cryogenic-Energy-Storage-Based Optimized Green Growth of an Integrated and Sustainable Energy System," Sustainability, MDPI, vol. 14(9), pages 1-18, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:5301-:d:804033
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    References listed on IDEAS

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    Cited by:

    1. Riyadh Kamil Chillab & Aqeel S. Jaber & Mouna Ben Smida & Anis Sakly, 2023. "Optimal DG Location and Sizing to Minimize Losses and Improve Voltage Profile Using Garra Rufa Optimization," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    2. Wan Chen & Zujun Ding & Jun Liu & Jiarong Kan & Muhammad Shahzad Nazir & Yeqin Wang, 2023. "Half-Bridge Lithium-Ion Battery Equalizer Based on Phase-Shift Strategy," Sustainability, MDPI, vol. 15(2), pages 1-13, January.
    3. Chung-Kwan Lo & Xiaowei Huang & Ka-Luen Cheung, 2022. "Toward a Design Framework for Mathematical Modeling Activities: An Analysis of Official Exemplars in Hong Kong Mathematics Education," Sustainability, MDPI, vol. 14(15), pages 1-17, August.
    4. Wan Chen & Baolian Liu & Muhammad Shahzad Nazir & Ahmed N. Abdalla & Mohamed A. Mohamed & Zujun Ding & Muhammad Shoaib Bhutta & Mehr Gul, 2022. "An Energy Storage Assessment: Using Frequency Modulation Approach to Capture Optimal Coordination," Sustainability, MDPI, vol. 14(14), pages 1-15, July.

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