Abstract

The analysis of different components of a grid-linked hybrid energy system (HES) comprising a photovoltaic (PV) system is presented in this work. Due to the increase of the population and industries, power consumption is increasing every day. Due to environmental issues, traditional power plants alone are insufficient to supply customer demand. In this case, the most important thing is to discover another approach to meet customer demands. Most wealthy countries are now concentrating their efforts on developing sustainable materials and investing considerable amounts of money in product development. Wind, solar, fuel cells, and hydro/water resources are among the most environmentally benign renewable sources. To control the variability of PV generation, this sort of application necessitates the usage of energy storage systems (ESSs). Lithium-ion (Li-ion) batteries are the most often used ESSs; however, they have a short lifespan due to the applied stress. Hybrid energy storage systems (HESSs) started to evolve as a way to decrease the pressure on Li-ion batteries and increase their lifetime. This study represents a great power management technique for a PV system with Li-ion batteries and supercapacitor (SC) HESS based on an artificial neural network. The effectiveness of the suggested power management technique is demonstrated and validated using a conventional PV system. Computational models with short-term and long-term durations were used to illustrate their effectiveness. The findings reveal that Li-ion battery dynamical stress and peak value are reduced, resulting in longer battery life.

Highlights

  • In today’s world, energy is critical to industrialization, urbanization, and a country’s economic progress

  • When compared to a few of the existing methodologies, the findings clearly show that the proposed approach outperforms them [14]

  • The energy management controllers for a photovoltaic hybrid power system with such a power supply are presented in this research, which is developed on an artificial neural network

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Summary

Introduction

In today’s world, energy is critical to industrialization, urbanization, and a country’s economic progress. Energy sources are the primary source of pollutants in the environment, including air and water contamination. Renewable technologies, according to Kumar’s research, are ubiquitous and affordable and polluted air [1]. People can use a variety of sources, including conventional sources such as coal, natural gas, and fossil fuels, as well as renewable technologies such as solar, wind, and hydroelectric. A fuel cell functions as a battery, storing energy in the form of hydrogen [2]. It produces water and electricity by reacting hydrogen with atmospheric oxygen. Multiple energy sources are required, such as the use of batteries in a rechargeable battery to harvest oxygen from the water or air, which results in a greater hydrogen storage price

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