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A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand

Author

Listed:
  • Aml Sayed

    (Department of Electrical Engineering, Faculty of Engineering, South Valley University, Qena 83523, Egypt)

  • Mohamed Ebeed

    (Department of Electrical Engineering, Faculty of Engineering, Sohag University, Sohag 82524, Egypt)

  • Ziad M. Ali

    (Electrical Engineering Department, College of Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addawaser 11991, Saudi Arabia
    Electrical Engineering Department, Aswan Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Adel Bedair Abdel-Rahman

    (Electronics and Communications Engineering Department, Egypt-Japan University of Science and Technology, Alexandria 21934, Egypt
    Electronics and Communications Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt)

  • Mahrous Ahmed

    (Department of Electrical, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Shady H. E. Abdel Aleem

    (Department of Electrical Engineering, Valley High Institute of Engineering and Technology, Science Valley Academy, Qalyubia 44971, Egypt)

  • Adel El-Shahat

    (Energy Technology Program, School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA)

  • Mahmoud Rihan

    (Department of Electrical Engineering, Faculty of Engineering, South Valley University, Qena 83523, Egypt)

Abstract

Unit commitment problem (UCP) is classified as a mixed-integer, large combinatorial, high-dimensional and nonlinear optimization problem. This paper suggests solving the UCP under deterministic and stochastic load demand using a hybrid technique that includes the modified particle swarm optimization (MPSO) along with equilibrium optimizer (EO), termed as MPSO-EO. The proposed approach is tested firstly on 15 benchmark test functions, and then it is implemented to solve the UCP under two test systems. The results are basically compared to that of standard EO and previously applied optimization techniques in solving the UCP. In test system 1, the load demand is deterministic. The proposed technique is in the best three solutions for the 10-unit system with cost savings of 309.95 USD over standard EO and for the 20-unit system it shows the best results over all algorithms in comparison with cost savings of 1951.5 USD over standard EO. In test system 2, the load demand is considered stochastic, and only the 10-unit system is studied. The proposed technique outperforms the standard EO with cost savings of 40.93 USD. The simulation results demonstrate that MPSO-EO has fairly good performance for solving the UCP with significant total operating cost savings compared to standard EO compared with other reported techniques.

Suggested Citation

  • Aml Sayed & Mohamed Ebeed & Ziad M. Ali & Adel Bedair Abdel-Rahman & Mahrous Ahmed & Shady H. E. Abdel Aleem & Adel El-Shahat & Mahmoud Rihan, 2021. "A Hybrid Optimization Algorithm for Solving of the Unit Commitment Problem Considering Uncertainty of the Load Demand," Energies, MDPI, vol. 14(23), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8014-:d:692191
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    References listed on IDEAS

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    1. Anand, Himanshu & Narang, Nitin & Dhillon, J.S., 2019. "Multi-objective combined heat and power unit commitment using particle swarm optimization," Energy, Elsevier, vol. 172(C), pages 794-807.
    2. Abdullah Khan & Hashim Hizam & Noor Izzri Abdul-Wahab & Mohammad Lutfi Othman, 2020. "Solution of Optimal Power Flow Using Non-Dominated Sorting Multi Objective Based Hybrid Firefly and Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 13(16), pages 1-24, August.
    3. Ana Viana & Jorge de Sousa & Manuel Matos, 2003. "Using GRASP to Solve the Unit Commitment Problem," Annals of Operations Research, Springer, vol. 120(1), pages 117-132, April.
    4. Pan, Jeng-Shyang & Hu, Pei & Chu, Shu-Chuan, 2021. "Binary fish migration optimization for solving unit commitment," Energy, Elsevier, vol. 226(C).
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    Cited by:

    1. Layon Mescolin de Oliveira & Ivo Chaves da Silva Junior & Ramon Abritta, 2022. "Search Space Reduction for the Thermal Unit Commitment Problem through a Relevance Matrix," Energies, MDPI, vol. 15(19), pages 1-16, September.
    2. Layon Mescolin de Oliveira & Ivo Chaves da Silva Junior & Ramon Abritta, 2023. "A Space Reduction Heuristic for Thermal Unit Commitment Considering Ramp Constraints and Large-Scale Generation Systems," Energies, MDPI, vol. 16(14), pages 1-15, July.
    3. Basma Salah & Hany M. Hasanien & Fadia M. A. Ghali & Yasser M. Alsayed & Shady H. E. Abdel Aleem & Adel El-Shahat, 2022. "African Vulture Optimization-Based Optimal Control Strategy for Voltage Control of Islanded DC Microgrids," Sustainability, MDPI, vol. 14(19), pages 1-26, September.

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