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Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid

Author

Listed:
  • Asad Ali

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Muhammad Salman Fakhar

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan
    National Transmission and Despatch Company (NTDC), Lahore 54000, Pakistan)

  • Syed Abdul Rahman Kashif

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Ghulam Abbas

    (Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan)

  • Irfan Ahmad Khan

    (Clean and Resilient Energy Systems (CARES) Lab, Electrical and Computer Engineering Department, Texas A&M University, Galveston, TX 77553, USA)

  • Akhtar Rasool

    (Department of Electrical Engineering, University of Botswana, Gaborone, Botswana)

  • Nasim Ullah

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

Abstract

In developing countries, many areas are deprived of electrical energy. Access to cleaner, more affordable energy is critical for improving the poor’s living conditions in developing countries. With the advent of smart grid technology, the integration and coordination of small grids, known as nanogrids, has become very easy. The purpose of this research is to propose a nanogrid model that will serve the purpose of providing the facility of electrical power to the poor rural community in Pakistan using hybrid renewable energy sources. This paper targets the electrification of a poor rural community of Akora Khatak, a small district located in Pakistan. The mathematical modeling of solar and wind energy, neural network-based forecasting of solar irradiance and wind velocity, and the social analysis to calculate the payback period for the community have been discussed in this paper.

Suggested Citation

  • Asad Ali & Muhammad Salman Fakhar & Syed Abdul Rahman Kashif & Ghulam Abbas & Irfan Ahmad Khan & Akhtar Rasool & Nasim Ullah, 2022. "Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid," Energies, MDPI, vol. 15(23), pages 1-31, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8933-:d:984455
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    References listed on IDEAS

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