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ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem

Author

Listed:
  • Mohamed Farhat

    (Electrical Power and Machines Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Salah Kamel

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

  • Ahmed M. Atallah

    (Electrical Power and Machines Engineering Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Mohamed H. Hassan

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

  • Ahmed M. Agwa

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 1321, Saudi Arabia
    Prince Faisal bin Khalid bin Sultan Research Chair in Renewable Energy Studies and Applications (PFCRE), Northern Border University, Arar 1321, Saudi Arabia)

Abstract

In this work, an enhanced slime mould algorithm (ESMA) based on neighborhood dimension learning (NDL) search strategy is proposed for solving the optimal power flow (OPF) problem. Before using the proposed ESMA for solving the OPF problem, its validity is verified by an experiment using 23 benchmark functions and compared with the original SMA, and three other recent optimization algorithms. Consequently, the ESMA is used to solve a modified power flow model including both conventional energy, represented by thermal power generators (TPGs), and renewable energy represented by wind power generators (WPGs) and solar photovoltaic generators (SPGs). Despite the important role of WPGs and SPGs in reducing CO 2 emissions, they represent a big challenge for the OPF problem due to their intermittent output powers. To forecast the intermittent output powers from SPGs and WPGs, Lognormal and Weibull probability density functions (PDFs) are used, respectively. The objective function of the OPF has two extra costs, penalty cost and reserve cost. The penalty cost is added to formulate the underestimation of the produced power from the WPGs and SPGs, while the reserve cost is added to formulate the case of overestimation. Moreover, to decrease CO 2 emissions from TPGs, a direct carbon tax is added to the objective function in some cases. The uncertainty of load demand represents also another challenge for the OPF that must be taken into consideration while solving it. In this study, the uncertainty of load demand is represented by the normal PDF. Simulation results of ESMA for solving the OPF are compared with the results of the conventional SMA and two further optimization methods. The simulation results obtained in this research show that ESMA is more effective in finding the optimal solution of the OPF problem with regard to minimizing the total power cost and the convergence of solution.

Suggested Citation

  • Mohamed Farhat & Salah Kamel & Ahmed M. Atallah & Mohamed H. Hassan & Ahmed M. Agwa, 2022. "ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem," Sustainability, MDPI, vol. 14(4), pages 1-33, February.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:4:p:2305-:d:752004
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    References listed on IDEAS

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    Cited by:

    1. Alencar, Marina Valença & da Silva, Diego Nunes & Nepomuceno, Leonardo & Martins, André Christóvão Pio & Balbo, Antonio Roberto & Soler, Edilaine Martins, 2024. "Discrete optimal power flow with prohibited zones, multiple-fuel options, and practical operational rules for control devices," Applied Energy, Elsevier, vol. 358(C).
    2. Mohammed Hamouda Ali & Ali M. El-Rifaie & Ahmed A. F. Youssef & Vladimir N. Tulsky & Mohamed A. Tolba, 2023. "Techno-Economic Strategy for the Load Dispatch and Power Flow in Power Grids Using Peafowl Optimization Algorithm," Energies, MDPI, vol. 16(2), pages 1-29, January.
    3. Mahmoud A. Ali & Salah Kamel & Mohamed H. Hassan & Emad M. Ahmed & Mohana Alanazi, 2022. "Optimal Power Flow Solution of Power Systems with Renewable Energy Sources Using White Sharks Algorithm," Sustainability, MDPI, vol. 14(10), pages 1-21, May.

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