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Genetic Algorithm based Fuzzy Logic Controller for Optimal Charging-Discharging of Energy Storage in Microgrid applications

Battery management systems; Charging (batteries); Computer circuits; Energy storage; Fuzzy logic; Genetic algorithms; Membership functions; Microgrids; Particle swarm optimization (PSO); Secondary batteries; Water treatment; Battery state of charge; Charging/discharging; Fuzzy logic controllers; Mic...

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Main Authors: Faisal M., Hannan M.A., Ker P.J., Muttaqi K.M.
Other Authors: 57215018777
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-270572023-05-29T17:39:07Z Genetic Algorithm based Fuzzy Logic Controller for Optimal Charging-Discharging of Energy Storage in Microgrid applications Faisal M. Hannan M.A. Ker P.J. Muttaqi K.M. 57215018777 7103014445 37461740800 55582332500 Battery management systems; Charging (batteries); Computer circuits; Energy storage; Fuzzy logic; Genetic algorithms; Membership functions; Microgrids; Particle swarm optimization (PSO); Secondary batteries; Water treatment; Battery state of charge; Charging/discharging; Fuzzy logic controllers; Microgrid; Optimal charging; Power; Renewable technology; States of charges; Storage systems; Swarm optimization; Controllers Microgrid (MG) concept with renewable technologies have the challenges of supplying reliable power considering the intermittent nature of the sources. Energy storage system (ESS) has become a viable solution to control the power fluctuation and thus providing the reliable power to the consumer. However, commonly used charging-discharging control techniques have the limitations of solving overcharging or over-discharging problem, fast charging capability, and rapid response time. To overcome these problems, fuzzy logic controller (FLC) has been proposed to control the charging-discharging due to its easy implementation, no mathematical calculation, and simplicity. However, existing FLC technologies have the limitations in considering the battery control parameters, and selecting the safe operating region (20% to 80%) of the battery state of charge (SOC). Therefore, this research proposes an improved FLC considering the available power from grid and distributed sources, load demand, battery SOC and temperature. To improve the performance of the controller, membership functions (MFs) of the FLC have been optimized by using genetic algorithm (GA). To prove the superiority of GA, another widely used optimization algorithm, particle swarm optimization (PSO) is applied with the same load variation. Obtained results show that, the minimum and maximum SOC level for fuzzy-GA only system has been improved compared to fuzzy only and fuzzy-PSO system. Therefore, it can be concluded that, the developed model works efficiently in controlling the charging and discharging of the battery. The authors are in progress to apply the controller system for MG connected waste water treatment plant. � 2022 IEEE. Final 2023-05-29T09:39:07Z 2023-05-29T09:39:07Z 2022 Conference Paper 10.1109/IAS54023.2022.9939768 2-s2.0-85142794915 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85142794915&doi=10.1109%2fIAS54023.2022.9939768&partnerID=40&md5=2b0f644f2e8c0292c897e23048ea1dcc https://irepository.uniten.edu.my/handle/123456789/27057 2022-October 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/
description Battery management systems; Charging (batteries); Computer circuits; Energy storage; Fuzzy logic; Genetic algorithms; Membership functions; Microgrids; Particle swarm optimization (PSO); Secondary batteries; Water treatment; Battery state of charge; Charging/discharging; Fuzzy logic controllers; Microgrid; Optimal charging; Power; Renewable technology; States of charges; Storage systems; Swarm optimization; Controllers
author2 57215018777
author_facet 57215018777
Faisal M.
Hannan M.A.
Ker P.J.
Muttaqi K.M.
format Conference Paper
author Faisal M.
Hannan M.A.
Ker P.J.
Muttaqi K.M.
spellingShingle Faisal M.
Hannan M.A.
Ker P.J.
Muttaqi K.M.
Genetic Algorithm based Fuzzy Logic Controller for Optimal Charging-Discharging of Energy Storage in Microgrid applications
author_sort Faisal M.
title Genetic Algorithm based Fuzzy Logic Controller for Optimal Charging-Discharging of Energy Storage in Microgrid applications
title_short Genetic Algorithm based Fuzzy Logic Controller for Optimal Charging-Discharging of Energy Storage in Microgrid applications
title_full Genetic Algorithm based Fuzzy Logic Controller for Optimal Charging-Discharging of Energy Storage in Microgrid applications
title_fullStr Genetic Algorithm based Fuzzy Logic Controller for Optimal Charging-Discharging of Energy Storage in Microgrid applications
title_full_unstemmed Genetic Algorithm based Fuzzy Logic Controller for Optimal Charging-Discharging of Energy Storage in Microgrid applications
title_sort genetic algorithm based fuzzy logic controller for optimal charging-discharging of energy storage in microgrid applications
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806427767635443712
score 13.222552

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