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Economic Load Dispatch of a Grid-Tied DC Microgrid Using the Interior Search Algorithm

Author

Listed:
  • Kamil Khan

    (US Pakistan Centre for Advanced Studies in Energy, UET Peshawar 25000, Pakistan)

  • Ahmad Kamal

    (US Pakistan Centre for Advanced Studies in Energy, UET Peshawar 25000, Pakistan)

  • Abdul Basit

    (US Pakistan Centre for Advanced Studies in Energy, UET Peshawar 25000, Pakistan)

  • Tanvir Ahmad

    (US Pakistan Centre for Advanced Studies in Energy, UET Peshawar 25000, Pakistan)

  • Haider Ali

    (Department of Electrical and Electronic Engineering Technology, UOT Nowshera 24100, Pakistan)

  • Anwar Ali

    (School of Information Science and Technology, Zhejiang Sci-Tech University, Hangzhou 310018, China)

Abstract

This paper presents the effectiveness of the interior search algorithm in economic power scheduling of a grid-tied DC microgrid with renewable generation (wind and photovoltaic) and battery energy storage. The study presents the modelling and simulation of various DC/DC converters for tracking maximum power from wind and photovoltaic sources and the bidirectional power flow of battery energy storage. The DC microgrid and its controllers were modelled and simulated in MATLAB/Simulink. The generating units were dispatched economically using the interior search algorithm with the objective to minimize the operating cost of the microgrid. The simulated results verify the effectiveness of the interior search algorithm as the daily cost of microgrid operation was reduced by 11.25%.

Suggested Citation

  • Kamil Khan & Ahmad Kamal & Abdul Basit & Tanvir Ahmad & Haider Ali & Anwar Ali, 2019. "Economic Load Dispatch of a Grid-Tied DC Microgrid Using the Interior Search Algorithm," Energies, MDPI, vol. 12(4), pages 1-13, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:634-:d:206524
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    References listed on IDEAS

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    Cited by:

    1. Le Chi Kien & Thanh Long Duong & Van-Duc Phan & Thang Trung Nguyen, 2020. "Maximizing Total Profit of Thermal Generation Units in Competitive Electric Market by Using a Proposed Particle Swarm Optimization," Sustainability, MDPI, vol. 12(3), pages 1-35, February.
    2. Marcel Nicola & Claudiu-Ionel Nicola & Dan Selișteanu, 2022. "Improvement of the Control of a Grid Connected Photovoltaic System Based on Synergetic and Sliding Mode Controllers Using a Reinforcement Learning Deep Deterministic Policy Gradient Agent," Energies, MDPI, vol. 15(7), pages 1-32, March.
    3. Marcel Nicola & Claudiu-Ionel Nicola, 2021. "Fractional-Order Control of Grid-Connected Photovoltaic System Based on Synergetic and Sliding Mode Controllers," Energies, MDPI, vol. 14(2), pages 1-25, January.

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