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Solving Renewables-Integrated Economic Load Dispatch Problem by Variant of Metaheuristic Bat-Inspired Algorithm

Author

Listed:
  • Faisal Tariq

    (Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan)

  • Salem Alelyani

    (Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia
    College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia)

  • Ghulam Abbas

    (Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan)

  • Ayman Qahmash

    (Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia
    College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia)

  • Mohammad Rashid Hussain

    (Center for Artificial Intelligence (CAI), King Khalid University, Abha 61421, Saudi Arabia
    College of Computer Science, King Khalid University, Abha 61421, Saudi Arabia)

Abstract

One of the most important concerns in the planning and operation of an electric power generation system is the effective scheduling of all power generation facilities to meet growing power demand. Economic load dispatch (ELD) is a phenomenon where an optimal combination of power generating units is selected in such a way as to minimize the total fuel cost while satisfying the load demand, subject to operational constraints. Different numerical and metaheuristic optimization techniques have gained prominent importance and are widely used to solve the nonlinear problem. Although metaheuristic techniques have a good convergence rate than numerical techniques, however, their implementation seems difficult in the presence of nonlinear and dynamic parameters. This work is devoted to solving the ELD problem with the integration of variable energy resources using a modified directional bat algorithm (dBA). Then the proposed technique is validated via different realistic test cases consisting of thermal and renewable energy sources (RESs). From simulation results, it is observed that dBA reduces the operational cost with less computational time and has better convergence characteristics than that of standard BA and other popular techniques like particle swarm optimization (PSO) and genetic algorithm (GA).

Suggested Citation

  • Faisal Tariq & Salem Alelyani & Ghulam Abbas & Ayman Qahmash & Mohammad Rashid Hussain, 2020. "Solving Renewables-Integrated Economic Load Dispatch Problem by Variant of Metaheuristic Bat-Inspired Algorithm," Energies, MDPI, vol. 13(23), pages 1-36, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6225-:d:451494
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    References listed on IDEAS

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    5. Manzoor Ellahi & Ghulam Abbas & Irfan Khan & Paul Mario Koola & Mashood Nasir & Ali Raza & Umar Farooq, 2019. "Recent Approaches of Forecasting and Optimal Economic Dispatch to Overcome Intermittency of Wind and Photovoltaic (PV) Systems: A Review," Energies, MDPI, vol. 12(22), pages 1-30, November.
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    Cited by:

    1. Cristian Hoyos-Velandia & Lina Ramirez-Hurtado & Jaime Quintero-Restrepo & Ricardo Moreno-Chuquen & Francisco Gonzalez-Longatt, 2022. "Cost Functions for Generation Dispatching in Microgrids for Non-Interconnected Zones in Colombia," Energies, MDPI, vol. 15(7), pages 1-14, March.
    2. Aokang Pang & Huijun Liang & Chenhao Lin & Lei Yao, 2023. "A Surrogate-Assisted Adaptive Bat Algorithm for Large-Scale Economic Dispatch," Energies, MDPI, vol. 16(2), pages 1-23, January.
    3. Khairul Eahsun Fahim & Liyanage C. De Silva & Fayaz Hussain & Hayati Yassin, 2023. "A State-of-the-Art Review on Optimization Methods and Techniques for Economic Load Dispatch with Photovoltaic Systems: Progress, Challenges, and Recommendations," Sustainability, MDPI, vol. 15(15), pages 1-29, August.
    4. Ghulam Abbas & Irfan Ahmad Khan & Naveed Ashraf & Muhammad Taskeen Raza & Muhammad Rashad & Raheel Muzzammel, 2023. "On Employing a Constrained Nonlinear Optimizer to Constrained Economic Dispatch Problems," Sustainability, MDPI, vol. 15(13), pages 1-23, June.

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