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Optimal Power Sharing in Microgrids Using the Artificial Bee Colony Algorithm

Author

Listed:
  • Kalim Ullah

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Quanyuan Jiang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Guangchao Geng

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Sahar Rahim

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Rehan Ali Khan

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

In smart grids, a hybrid renewable energy system that combines multiple renewable energy sources (RESs) with storage and backup systems can provide the most cost-effective and stable energy supply. However, one of the most pressing issues addressed by recent research is how best to design the components of hybrid renewable energy systems to meet all load requirements at the lowest possible cost and with the best level of reliability. Due to the difficulty of optimizing hybrid renewable energy systems, it is critical to find an efficient optimization method that provides a reliable solution. Therefore, in this study, power transmission between microgrids is optimized to minimize the cost for the overall system and for each microgrid. For this purpose, artificial bee colony (ABC) is used as an optimization algorithm that aims to minimize the cost and power transmission from outside the microgrid. The ABC algorithm outperforms other population-based algorithms, with the added advantage of requiring fewer control parameters. The ABC algorithm also features good resilience, fast convergence, and great versatility. In this study, several experiments were conducted to show the productivity of the proposed ABC-based approach. The simulation results show that the proposed method is an effective optimization approach because it can achieve the global optimum in a very simple and computationally efficient way.

Suggested Citation

  • Kalim Ullah & Quanyuan Jiang & Guangchao Geng & Sahar Rahim & Rehan Ali Khan, 2022. "Optimal Power Sharing in Microgrids Using the Artificial Bee Colony Algorithm," Energies, MDPI, vol. 15(3), pages 1-22, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1067-:d:739609
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    References listed on IDEAS

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    1. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
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    Cited by:

    1. Malika Fodil & Ali Djerioui & Mohamed Ladjal & Abdelhakim Saim & Fouad Berrabah & Hemza Mekki & Samir Zeghlache & Azeddine Houari & Mohamed Fouad Benkhoris, 2023. "Optimization of PI Controller Parameters by GWO Algorithm for Five-Phase Asynchronous Motor," Energies, MDPI, vol. 16(10), pages 1-14, May.
    2. Upasana Lakhina & Nasreen Badruddin & Irraivan Elamvazuthi & Ajay Jangra & Truong Hoang Bao Huy & Josep M. Guerrero, 2023. "An Enhanced Multi-Objective Optimizer for Stochastic Generation Optimization in Islanded Renewable Energy Microgrids," Mathematics, MDPI, vol. 11(9), pages 1-24, April.
    3. Jong-Woon Park & Min-Mo Koo & Hyun-Uk Seo & Dong-Kuk Lim, 2023. "Optimizing the Design of an Interior Permanent Magnet Synchronous Motor for Electric Vehicles with a Hybrid ABC-SVM Algorithm," Energies, MDPI, vol. 16(13), pages 1-14, June.
    4. Kalim Ullah & Quanyuan Jiang & Guangchao Geng & Rehan Ali Khan & Sheraz Aslam & Wahab Khan, 2022. "Optimization of Demand Response and Power-Sharing in Microgrids for Cost and Power Losses," Energies, MDPI, vol. 15(9), pages 1-22, April.

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