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Identification of Weak Buses for Optimal Load Shedding Using Differential Evolution

Author

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  • Olumuyiwa T. Amusan

    (Center for Cyber Physical Food, Energy and Water Systems, University of Johannesburg, Johannesburg 2006, South Africa)

  • Nnamdi I. Nwulu

    (Center for Cyber Physical Food, Energy and Water Systems, University of Johannesburg, Johannesburg 2006, South Africa)

  • Saheed Lekan Gbadamosi

    (Center for Cyber Physical Food, Energy and Water Systems, University of Johannesburg, Johannesburg 2006, South Africa)

Abstract

For the sustainability of power supply and operation systems, planners aim to deliver power at an optimum value to consumers, while maintaining stability in the system. The load-shedding approach has proven to be an effective means of achieving the desired stability. This paper presents a nodal analysis to establish critical bus identification in the power grid. A power simulation for load shedding was created using the power system analysis toolbox (PSAT) for identifying and isolating weak buses on the power system. A computational algorithm was developed using differential evolution (DE) for minimizing service interruptions and blackouts, and was tested against the conventional genetic algorithm (GA). The proposed algorithm was implemented on an IEEE 30-bus test system. The simulation results were analyzed before and after the application of DE. It was observed that after the application of DE, load shedding gives an efficient result of 10.6%, 8.7%, and 13.4% improvement at buses 26, 29, and 30, respectively, after being tested using a genetic algorithm (GA), with a result of 10.2%, 7.6% and 13.1% on the same respective buses. This work will further expand the reliability and availability of power systems toward a sustainable, steady power supply that is void of nodal or bus cutoffs.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:6:p:3146-:d:766218
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    References listed on IDEAS

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    1. 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.
    2. Tao, Zhenmin & Moncada, Jorge Andrés & Poncelet, Kris & Delarue, Erik, 2021. "Review and analysis of investment decision making algorithms in long-term agent-based electric power system simulation models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 136(C).
    3. Jafar Jallad & Saad Mekhilef & Hazlie Mokhlis & Javed Laghari & Ola Badran, 2018. "Application of Hybrid Meta-Heuristic Techniques for Optimal Load Shedding Planning and Operation in an Islanded Distribution Network Integrated with Distributed Generation," Energies, MDPI, vol. 11(5), pages 1-25, May.
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    Cited by:

    1. Gugulethu Nogaya & Nnamdi I. Nwulu & Saheed Lekan Gbadamosi, 2022. "Repurposing South Africa’s Retiring Coal-Fired Power Stations for Renewable Energy Generation: A Techno-Economic Analysis," Energies, MDPI, vol. 15(15), pages 1-13, August.
    2. Florin-Constantin Baiceanu & Ovidiu Ivanov & Razvan-Constantin Beniuga & Bogdan-Constantin Neagu & Ciprian-Mircea Nemes, 2023. "A Continuous Multistage Load Shedding Algorithm for Industrial Processes Based on Metaheuristic Optimization," Mathematics, MDPI, vol. 11(12), pages 1-19, June.

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