Optimized Intelligent Controller for Energy Storage based Microgrid towards Sustainable Energy Future

This study focuses on a sustainable microgrid-based hybrid energy system (HES), primarily focusing on analyzing the performance of the fuel cell and its impact on the overall HES into optimizing system performance. This system relies on a single renewable energy source, a photovoltaic (PV) system th...

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Main Authors: Abu S.M., Hannan M.A., Mansor M., Ker P.J., Yaw Long C.
Other Authors: 58116063000
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2024
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spelling my.uniten.dspace-343512024-10-14T11:19:13Z Optimized Intelligent Controller for Energy Storage based Microgrid towards Sustainable Energy Future Abu S.M. Hannan M.A. Mansor M. Ker P.J. Yaw Long C. 58116063000 7103014445 6701749037 37461740800 58902792500 battery fuel cell hybrid energy storage integrated grid hydrogen optimization algorithm solar supercapacitor sustainable Battery storage Benchmarking Electric batteries Electric loads Energy conservation Hybrid systems Hydrogen storage MATLAB Particle swarm optimization (PSO) Renewable energy Solar power generation Supercapacitor Two term control systems Uninterruptible power systems Battery Energy future Hybrid energy storage Hybrid energy storage integrated grid Hybrid energy system Microgrid Optimization algorithms Solar Sustainable Sustainable energy Fuel cells This study focuses on a sustainable microgrid-based hybrid energy system (HES), primarily focusing on analyzing the performance of the fuel cell and its impact on the overall HES into optimizing system performance. This system relies on a single renewable energy source, a photovoltaic (PV) system that is integrated with the energy storage system (ESS) including hydrogen-based fuel cell, battery, and supercapacitor for effective power management. The optimization of HES performance is achieved through fine-tuning of the proportional-integral (PI) controller using the particle swarm optimization (PSO) algorithm. The load profile utilized in the microgrid (MG) is characterized by a constant power output, ensuring a stable and uninterrupted supply of electricity from 6am to 6pm in a 24-hour time period. This approach is comparable to meeting the specific demands of industrial and critical facilities, such as manufacturing plants and hospitals, where continuous power is important. The selection of a constant load profile is benchmark with the alignment of Denham Hydrogen Demonstration Plant, Western Australia, enhancing the MG's overall reliability and validation into real-world application. Through simulation and analysis using MATLAB Simulink, the results demonstrate the remarkable impact of PSO on enhancing the fuel cell system's efficiency with air consumption, and fuel consumption reduction by utilization of 90% of the H2 to electrical energy. Significantly, the optimized total source power output enables seamless energy storage and intelligent load matching, leading to a stable and reliable grid power supply. This research study findings highlights the essential role of PSO in elevating sustainability and maximizing resource utilization within microgrid-based hybrid energy systems, establishing a pathway towards a greener and more sustainable energy future. � 2023 IEEE. Final 2024-10-14T03:19:13Z 2024-10-14T03:19:13Z 2023 Conference Paper 10.1109/ETFG55873.2023.10407475 2-s2.0-85185816290 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85185816290&doi=10.1109%2fETFG55873.2023.10407475&partnerID=40&md5=48fbe720fd0c268642fe468be7a38eed https://irepository.uniten.edu.my/handle/123456789/34351 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
topic battery
fuel cell
hybrid energy storage integrated grid
hydrogen
optimization algorithm
solar
supercapacitor
sustainable
Battery storage
Benchmarking
Electric batteries
Electric loads
Energy conservation
Hybrid systems
Hydrogen storage
MATLAB
Particle swarm optimization (PSO)
Renewable energy
Solar power generation
Supercapacitor
Two term control systems
Uninterruptible power systems
Battery
Energy future
Hybrid energy storage
Hybrid energy storage integrated grid
Hybrid energy system
Microgrid
Optimization algorithms
Solar
Sustainable
Sustainable energy
Fuel cells
spellingShingle battery
fuel cell
hybrid energy storage integrated grid
hydrogen
optimization algorithm
solar
supercapacitor
sustainable
Battery storage
Benchmarking
Electric batteries
Electric loads
Energy conservation
Hybrid systems
Hydrogen storage
MATLAB
Particle swarm optimization (PSO)
Renewable energy
Solar power generation
Supercapacitor
Two term control systems
Uninterruptible power systems
Battery
Energy future
Hybrid energy storage
Hybrid energy storage integrated grid
Hybrid energy system
Microgrid
Optimization algorithms
Solar
Sustainable
Sustainable energy
Fuel cells
Abu S.M.
Hannan M.A.
Mansor M.
Ker P.J.
Yaw Long C.
Optimized Intelligent Controller for Energy Storage based Microgrid towards Sustainable Energy Future
description This study focuses on a sustainable microgrid-based hybrid energy system (HES), primarily focusing on analyzing the performance of the fuel cell and its impact on the overall HES into optimizing system performance. This system relies on a single renewable energy source, a photovoltaic (PV) system that is integrated with the energy storage system (ESS) including hydrogen-based fuel cell, battery, and supercapacitor for effective power management. The optimization of HES performance is achieved through fine-tuning of the proportional-integral (PI) controller using the particle swarm optimization (PSO) algorithm. The load profile utilized in the microgrid (MG) is characterized by a constant power output, ensuring a stable and uninterrupted supply of electricity from 6am to 6pm in a 24-hour time period. This approach is comparable to meeting the specific demands of industrial and critical facilities, such as manufacturing plants and hospitals, where continuous power is important. The selection of a constant load profile is benchmark with the alignment of Denham Hydrogen Demonstration Plant, Western Australia, enhancing the MG's overall reliability and validation into real-world application. Through simulation and analysis using MATLAB Simulink, the results demonstrate the remarkable impact of PSO on enhancing the fuel cell system's efficiency with air consumption, and fuel consumption reduction by utilization of 90% of the H2 to electrical energy. Significantly, the optimized total source power output enables seamless energy storage and intelligent load matching, leading to a stable and reliable grid power supply. This research study findings highlights the essential role of PSO in elevating sustainability and maximizing resource utilization within microgrid-based hybrid energy systems, establishing a pathway towards a greener and more sustainable energy future. � 2023 IEEE.
author2 58116063000
author_facet 58116063000
Abu S.M.
Hannan M.A.
Mansor M.
Ker P.J.
Yaw Long C.
format Conference Paper
author Abu S.M.
Hannan M.A.
Mansor M.
Ker P.J.
Yaw Long C.
author_sort Abu S.M.
title Optimized Intelligent Controller for Energy Storage based Microgrid towards Sustainable Energy Future
title_short Optimized Intelligent Controller for Energy Storage based Microgrid towards Sustainable Energy Future
title_full Optimized Intelligent Controller for Energy Storage based Microgrid towards Sustainable Energy Future
title_fullStr Optimized Intelligent Controller for Energy Storage based Microgrid towards Sustainable Energy Future
title_full_unstemmed Optimized Intelligent Controller for Energy Storage based Microgrid towards Sustainable Energy Future
title_sort optimized intelligent controller for energy storage based microgrid towards sustainable energy future
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2024
_version_ 1814061051771092992