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Research Article |

An Advanced Artificial Neural Network Energy Management in Standalone PV Systems

Author(s): Emhamed Alzaroog1, Mohsen Ben Ammar2, Mohamed Ali Zdiri3 and Hsan Hadj Abdallah4

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 10, Issue 4

Publisher : FOREX Publication

Published : 20 November 2022

e-ISSN : 2347-470X

Page(s) : 1005-1012

DOI: https://doi.org/10.37391/IJEER.100442

Abstract

With the ever-increasing prevalent power crisis and pollution of the environment, solar power, has attracted greater attention as a new and clean energy source. It provides an alternative solution for isolated sites with an unavailable grid connection. However, it is not without any drawbacks, mainly its intermittent nature, related primarily owing to its reliance on meteorological variables such as the temperature outside and the amount of sunlight. In effect, the PV systems that produced electrical energy could well display an electricity excess or deficit at the loads level, likely to result in system service discontinuity. In this respect, the present paper is designed to provide an intelligent management strategy to PV station owners with a dump load. It can involve serving two customers simultaneously according to the following scenarios: the PV production installation of the customer1 is greater than their required load; however, the customer2's neighboring station does not have enough power to cover its electrical load. This case brings electrical energy from the initial station to make up for the shortfall, and vice versa. Lithium-ion batteries step in the case when the essential electrical power cannot be delivered either by the local station or the neighboring one or to keep the accumulated power excess. If one of the stations (1 or 2) detects a power surplus and the batteries are completely charged, the generated power excess must be redirected to a secondary load, commonly known as the dump load. Relying on the artificial neural network controller, the suggested exchange control is used for two independent PV-battery stations with dump load. The MATLAB/Simulink attained simulation turns out to demonstrate the advanced controller’s noticeable performance and effectiveness in managing the standalone PV system’s operability in terms of continuous electrical energy delivery flow to the resistive load while reducing power waste and increasing the lithium-ion battery lifespan.

Keywords: Solar energy, PV system, Artificial Neural Network control, Dump load, Lithium-ion batteries, Energy control.




Emhamed Alzaroog*, PhD student, Department of Electrical Engineering, CEM Laboratory, ENIS, University of Sfax, Tunisia; Email: ealzarooz@gmail.com

Mohsen Ben Ammar, Associate Professor, Department of Electrical Engineering, CEM Laboratory, ENIS-University of Sfax, Tunisia

Mohamed Ali Zdiri, Assistant Contractual, Department of Electrical Engineering, CEM Laboratory, ENIS, University of Sfax, Tunisia

Hsan Hadj Abdallah, Professor, Department of Electrical Engineering, CEM Laboratory, ENIS, University of Sfax, Tunisia

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Emhamed Alzaroog, Mohsen Ben Ammar, Mohamed Ali Zdiri and Hsan Hadj Abdallah (2022), An Advanced Artificial Neural Network Energy Management in Standalone PV Systems. IJEER 10(4), 1005-1012. DOI: 10.37391/IJEER.100442.