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A Developed Frequency Control Strategy for Hybrid Two-Area Power System with Renewable Energy Sources Based on an Improved Social Network Search Algorithm

Author

Listed:
  • Mohamed Khamies

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Salah Kamel

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Mohamed H. Hassan

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Mohamed F. Elnaggar

    (Department of Electrical Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia
    Department of Electrical Power and Machines Engineering, Faculty of Engineering, Helwan University, Hewlan 11795, Egypt)

Abstract

In this paper, an effective frequency control strategy is proposed for emulating sufficient inertia power and improving frequency stability. The developed technique is based on applying virtual inertia control (VIC) with superconducting magnetic energy storage (SMES) instead of a traditional energy storage system (ESS) to compensate for the system inertia during the high penetration of renewable energy sources, taking into account the role of the controller in the secondary control loop (SCL). Unlike previous studies that depended on the designer experience in selecting the parameters of the inertia gain or the parameters of the SMES technology, the parameters of the proposed strategy are selected using optimization techniques. Moreover, an improved optimization algorithm called Improved Social Network Search algorithm (ISNS) is proposed to select the optimal parameters of the proposed control strategy. Moreover, the ISNS is improved to overcome the demerits of the traditional SNS algorithm, such as low speed convergence and global search capability. Accordingly, the ISNS algorithm is applied to a hybrid two-area power grid to determine the optimal parameters of the proposed control technique as follows: the proportional-integral derivative (PID) controller in the SCL. Additionally, the ISNS is applied to select the optimal control gains of the VIC-based SMES technology (e.g., the inertia gain, the proportional gain of the SMES, and the negative feedback gain of the SMES). Furthermore, the effectiveness of the proposed ISNS algorithm is validated by comparing its performance with that of the traditional SNS algorithm and other well-known algorithms (i.e., PSO, TSA, GWO, and WHO) considering different standard benchmark functions. Formerly, the effectiveness of the proposed frequency control technique was confirmed by comparing its performance with the system performance based on optimal VIC with ESS as well as without VIC considering different operating situations. The simulation results demonstrated the superiority of the proposed technique over other considered techniques, especially during high penetration of renewable power and lack of system inertia. As a result, the proposed technique is credible for modern power systems that take into account RESs.

Suggested Citation

  • Mohamed Khamies & Salah Kamel & Mohamed H. Hassan & Mohamed F. Elnaggar, 2022. "A Developed Frequency Control Strategy for Hybrid Two-Area Power System with Renewable Energy Sources Based on an Improved Social Network Search Algorithm," Mathematics, MDPI, vol. 10(9), pages 1-31, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:9:p:1584-:d:810473
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    References listed on IDEAS

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    1. Thongchart Kerdphol & Masayuki Watanabe & Yasunori Mitani & Veena Phunpeng, 2019. "Applying Virtual Inertia Control Topology to SMES System for Frequency Stability Improvement of Low-Inertia Microgrids Driven by High Renewables," Energies, MDPI, vol. 12(20), pages 1-16, October.
    2. Jiashen Teh & Ching-Ming Lai & Yu-Huei Cheng, 2018. "Improving the Penetration of Wind Power with Dynamic Thermal Rating System, Static VAR Compensator and Multi-Objective Genetic Algorithm," Energies, MDPI, vol. 11(4), pages 1-16, April.
    3. Lai, Ching-Ming & Teh, Jiashen, 2022. "Network topology optimisation based on dynamic thermal rating and battery storage systems for improved wind penetration and reliability," Applied Energy, Elsevier, vol. 305(C).
    4. Gianfreda, Angelica & Parisio, Lucia & Pelagatti, Matteo, 2018. "A review of balancing costs in Italy before and after RES introduction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 549-563.
    5. Changjun Zhang, 2017. "Improving power and stability," Nature Energy, Nature, vol. 2(10), pages 765-765, October.
    6. Mohammed Elsayed Lotfy & Tomonobu Senjyu & Mohammed Abdel-Fattah Farahat & Amal Farouq Abdel-Gawad & Atsuhi Yona, 2017. "A Frequency Control Approach for Hybrid Power System Using Multi-Objective Optimization," Energies, MDPI, vol. 10(1), pages 1-22, January.
    7. Thongchart Kerdphol & Fathin Saifur Rahman & Yasunori Mitani, 2018. "Virtual Inertia Control Application to Enhance Frequency Stability of Interconnected Power Systems with High Renewable Energy Penetration," Energies, MDPI, vol. 11(4), pages 1-16, April.
    8. Morales-España, Germán & Nycander, Elis & Sijm, Jos, 2021. "Reducing CO2 emissions by curtailing renewables: Examples from optimal power system operation," Energy Economics, Elsevier, vol. 99(C).
    9. Thongchart Kerdphol & Fathin S. Rahman & Yasunori Mitani & Komsan Hongesombut & Sinan Küfeoğlu, 2017. "Virtual Inertia Control-Based Model Predictive Control for Microgrid Frequency Stabilization Considering High Renewable Energy Integration," Sustainability, MDPI, vol. 9(5), pages 1-21, May.
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    Cited by:

    1. Adrian Marius Deaconu & Daniel Tudor Cotfas & Petru Adrian Cotfas, 2023. "Advanced Optimization Methods and Applications," Mathematics, MDPI, vol. 11(9), pages 1-7, May.

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