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Hybrid ABC-BAT for Solving Short-Term Hydrothermal Scheduling Problems

Author

Listed:
  • Smarajit Ghosh

    (Department of Electrical and Instrumentation Engineering; Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India)

  • Manvir Kaur

    (Department of Electrical and Instrumentation Engineering; Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India)

  • Suman Bhullar

    (Department of Electrical and Instrumentation Engineering; Thapar Institute of Engineering and Technology, Patiala, Punjab 147004, India)

  • Vinod Karar

    (Optical Devices and Systems, CSIR-Central Scientific Instruments Organization, Sector 30-C, Chandigarh 160030, India)

Abstract

The main objective of short-term hydrothermal scheduling is the optimal allocation of the hydro and thermal generating units, so that the total cost of thermal plants can be minimized. The time of operation of the functioning units are considered to be 24 h. To achieve this objective, the hybrid algorithm combination of Artificial Bee Colony (ABC) and the BAT algorithm were applied. The swarming behavior of the algorithm searches the food source for which the objective function of the cost is to be considered; here, we have used two search algorithms, one to optimize the cost function, and another to improve the performance of the system. In the present work, the optimum scheduling of hydro and thermal units is proposed, where these units are acting as a relay unit. The short term hydrothermal scheduling problem was tested in a Chilean system, and confirmed by comparison with other hybrid techniques such as Artificial Bee Colony–Quantum Evolutionary and Artificial Bee Colony–Particle Swarm Optimization. The efficiency of the proposed hybrid algorithm is established by comparing it to these two hybrid algorithms.

Suggested Citation

  • Smarajit Ghosh & Manvir Kaur & Suman Bhullar & Vinod Karar, 2019. "Hybrid ABC-BAT for Solving Short-Term Hydrothermal Scheduling Problems," Energies, MDPI, vol. 12(3), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:551-:d:204770
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    References listed on IDEAS

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    1. Zhang, Jingrui & Lin, Shuang & Liu, Houde & Chen, Yalin & Zhu, Mingcheng & Xu, Yinliang, 2017. "A small-population based parallel differential evolution algorithm for short-term hydrothermal scheduling problem considering power flow constraints," Energy, Elsevier, vol. 123(C), pages 538-554.
    2. Basu, M., 2011. "Artificial immune system for fixed head hydrothermal power system," Energy, Elsevier, vol. 36(1), pages 606-612.
    3. Mahor, Amita & Prasad, Vishnu & Rangnekar, Saroj, 2009. "Economic dispatch using particle swarm optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 2134-2141, October.
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    2. Nebojsa Bacanin & Timea Bezdan & Eva Tuba & Ivana Strumberger & Milan Tuba, 2020. "Monarch Butterfly Optimization Based Convolutional Neural Network Design," Mathematics, MDPI, vol. 8(6), pages 1-33, June.
    3. P. M. R. Bento & S. J. P. S. Mariano & M. R. A. Calado & L. A. F. M. Ferreira, 2020. "A Novel Lagrangian Multiplier Update Algorithm for Short-Term Hydro-Thermal Coordination," Energies, MDPI, vol. 13(24), pages 1-19, December.
    4. Siqing Sheng & Qing Gu, 2019. "A Day-ahead and Day-in Decision Model Considering the Uncertainty of Multiple Kinds of Demand Response," Energies, MDPI, vol. 12(9), pages 1-26, May.
    5. Saqib Akram & Muhammad Salman Fakhar & Syed Abdul Rahman Kashif & Ghulam Abbas & Nasim Ullah & Alsharef Mohammad & Mohamed Emad Farrag, 2022. "Introducing Adaptive Machine Learning Technique for Solving Short-Term Hydrothermal Scheduling with Prohibited Discharge Zones," Sustainability, MDPI, vol. 14(18), pages 1-18, September.

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