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Hybrid DC–AC Microgrid Energy Management System Using an Artificial Gorilla Troops Optimizer Optimized Neural Network

Author

Listed:
  • Sathesh Murugan

    (Department of Electronics and Communication Engineering, Saveetha School of Engineering, Chennai 602105, India)

  • Mohana Jaishankar

    (Department of Electronics and Communication Engineering, Saveetha School of Engineering, Chennai 602105, India)

  • Kamaraj Premkumar

    (Department of Electrical and Electronics Engineering, Rajalakshmi Engineering College, Chennai 602105, India)

Abstract

In this research, we introduce an artificial gorilla troop optimizer for use in artificial neural networks that manage energy consumption in DC–AC hybrid distribution networks. It is being proposed to implement an energy management system that takes into account distributed generation, load demand, and battery-charge level. Using the profile data, an artificial neural network was trained on the charging and discharging characteristics of an energy storage system under a variety of distribution-network power situations. As an added bonus, the percentage of mistakes was maintained far below 10%. An artificial neural network is used in the proposed energy management system, and it has been taught to operate in the best possible manner by using an optimizer inspired by gorillas called artificial gorilla troops. The artificial gorilla troops optimizer optimize the weights and bias of the neural network based on the power of the distributed generator, the power of the grid, and the reference direct axis current to obtain most suitable energy management system. In order to simulate and evaluate the proposed energy management system, small-scale hybrid DC/AC microgrids have been created and tested. When compared to other systems in the literature, the artificial gorilla troops optimizer enhanced neural network energy management system has been shown to deliver 99.55% efficiency, making it the clear winner.

Suggested Citation

  • Sathesh Murugan & Mohana Jaishankar & Kamaraj Premkumar, 2022. "Hybrid DC–AC Microgrid Energy Management System Using an Artificial Gorilla Troops Optimizer Optimized Neural Network," Energies, MDPI, vol. 15(21), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8187-:d:961599
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    References listed on IDEAS

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    1. Sadiq Ahmad & Ayaz Ahmad & Muhammad Naeem & Waleed Ejaz & Hyung Seok Kim, 2018. "A Compendium of Performance Metrics, Pricing Schemes, Optimization Objectives, and Solution Methodologies of Demand Side Management for the Smart Grid," Energies, MDPI, vol. 11(10), pages 1-33, October.
    2. Rajesh, K.S. & Dash, S.S. & Rajagopal, Ragam & Sridhar, R., 2017. "A review on control of ac microgrid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 814-819.
    3. Sadaqat Ali & Zhixue Zheng & Michel Aillerie & Jean-Paul Sawicki & Marie-Cécile Péra & Daniel Hissel, 2021. "A Review of DC Microgrid Energy Management Systems Dedicated to Residential Applications," Energies, MDPI, vol. 14(14), pages 1-26, July.
    4. Planas, Estefanía & Andreu, Jon & Gárate, José Ignacio & Martínez de Alegría, Iñigo & Ibarra, Edorta, 2015. "AC and DC technology in microgrids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 726-749.
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