Optimal Scheduling of Neural Network-Based Estimated Renewable Energy Nanogrid

Asad Ali, Muhammad Salman Fakhar, Syed Abdul Rahman Kashif, Ghulam Abbas (), Irfan Ahmad Khan (), Akhtar Rasool and Nasim Ullah
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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
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

Energies, 2022, vol. 15, issue 23, 1-31

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.

Keywords: renewable energy resources; neural networks; optimization techniques; wind energy conversion system; forecasting; nanogrid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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