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An Improved Algorithm for Optimal Load Shedding in Power Systems

Author

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  • Raja Masood Larik

    (School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bharu Skudai 81310, Malaysia
    Department of Electrical Engineering, NED University of Engineering and Technology Karachi, Sindh 75270, Pakistan)

  • Mohd Wazir Mustafa

    (School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bharu Skudai 81310, Malaysia)

  • Muhammad Naveed Aman

    (Department of Computer Science, National University of Singapore, Singapore 119077, Singapore)

  • Touqeer Ahmed Jumani

    (School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bharu Skudai 81310, Malaysia
    Mehran University of Engineering and Technology SZAB campus Khairpur Mirs, Sindh 66020, Pakistan)

  • Suhaib Sajid

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

  • Manoj Kumar Panjwani

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China
    Department of Energy Systems Engineering, Sukkur IBA University Pakistan, Sindh 65200, Pakistan)

Abstract

A blackout is usually the result of load increasing beyond the transmission capacity of the power system. A collapsing system enters a contingency state before the blackout. This contingency state is characterized by a decline in the bus voltage magnitudes. To avoid blackouts, power systems may start shedding load when a contingency state occurs called under voltage load shedding (UVLS). The success of a UVLS scheme in arresting the contingency state depends on shedding the optimum amount of load at the optimum time and location. This paper proposes a hybrid algorithm based on genetic algorithms (GA) and particle swarm optimization (PSO). The proposed algorithm can be used to find the optimal amount of load shed for systems under stress (overloaded) in smart grids. The proposed algorithm uses the fast voltage stability index (FVSI) to determine the weak buses in the system and then calculates the optimal amount of load shed to recover a collapsing system. The performance analysis shows that the proposed algorithm can improve the voltage profile by 0.022 per units with up to 75% less load shedding and a convergence time that is 53% faster than GA.

Suggested Citation

  • Raja Masood Larik & Mohd Wazir Mustafa & Muhammad Naveed Aman & Touqeer Ahmed Jumani & Suhaib Sajid & Manoj Kumar Panjwani, 2018. "An Improved Algorithm for Optimal Load Shedding in Power Systems," Energies, MDPI, vol. 11(7), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1808-:d:157270
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    References listed on IDEAS

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    1. Michal Wydra, 2018. "Performance and Accuracy Investigation of the Two-Step Algorithm for Power System State and Line Temperature Estimation," Energies, MDPI, vol. 11(4), pages 1-20, April.
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    Cited by:

    1. Hazwani Mohd Rosli & Hazlie Mokhlis & Nurulafiqah Nadzirah Mansor & Norazliani Md Sapari & Syahirah Abd Halim & Li Wang & Mohamad Fani Sulaima, 2023. "A Binary Archimedes Optimization Algorithm and Weighted Sum Method for UFLS in Islanded Distribution Systems Considering the Stability Index and Load Priority," Energies, MDPI, vol. 16(13), pages 1-21, July.
    2. Abbasizadeh, Ali & Azad-Farsani, Ehsan, 2024. "Cyber-constrained load shedding for smart grid resilience enhancement," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    3. Olumuyiwa T. Amusan & Nnamdi I. Nwulu & Saheed Lekan Gbadamosi, 2022. "Identification of Weak Buses for Optimal Load Shedding Using Differential Evolution," Sustainability, MDPI, vol. 14(6), pages 1-12, March.
    4. Xiaoming Mao & Junxian Chen, 2019. "A Fast Method to Compute the Dynamic Response of Induction Motor Loads Considering the Negative-Sequence Components in Stability Studies," Energies, MDPI, vol. 12(9), pages 1-19, May.
    5. Ahsanullah Memon & Mohd Wazir Mustafa & Muhammad Naveed Aman & Mukhtar Ullah & Tariq Kamal & Abdul Hafeez, 2021. "Dynamic Low Voltage Ride through Detection and Mitigation in Brushless Doubly Fed Induction Generators," Energies, MDPI, vol. 14(15), pages 1-17, July.
    6. Lutfu Saribulut & Gorkem Ok & Arman Ameen, 2023. "A Case Study on National Electricity Blackout of Turkey," Energies, MDPI, vol. 16(11), pages 1-20, May.
    7. Touqeer Ahmed Jumani & Mohd Wazir Mustafa & Nawaf N. Hamadneh & Samer H. Atawneh & Madihah Md. Rasid & Nayyar Hussain Mirjat & Muhammad Akram Bhayo & Ilyas Khan, 2020. "Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids," Energies, MDPI, vol. 13(16), pages 1-22, August.
    8. Salman Habib & Ghulam Abbas & Touqeer A. Jumani & Aqeel Ahmed Bhutto & Sohrab Mirsaeidi & Emad M. Ahmed, 2022. "Improved Whale Optimization Algorithm for Transient Response, Robustness, and Stability Enhancement of an Automatic Voltage Regulator System," Energies, MDPI, vol. 15(14), pages 1-18, July.

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