Cover Image

Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy

High cost of renewable energy systems has led to its slow adoption in many countries. Hence, it is vital to select an appropriate size of the system in order to reduce the cost and excess energy produced as well as to maximize the available resources. The sizing of hybrid system must satisfy the LPS...

Full description

Saved in:
Bibliographic Details
Main Authors: Rajkumar R.K., Ramachandaramurthy V.K., Yong B.L., Chia D.B.
Other Authors: 35093088900
Format: Article
Published: Elsevier Ltd 2023
Subjects:
Adaptive Neuro-Fuzzy Inference System
Loss of power supply probability
Photovoltaic
Algorithms
Costs
Electric power systems
Fuzzy inference
Fuzzy systems
Hybrid systems
Loss of load probability
Adaptive neuro-fuzzy inference system
Excess energy
High costs
Hybrid optimization
Neuro-Fuzzy
Optimization methodology
Optimized system
PSCAD/EMTDC
Renewable energy systems
Renewable sources
Renewables
accuracy assessment
cost-benefit analysis
energy efficiency
genetic algorithm
numerical model
optimization
renewable resource
Optimization
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.uniten.dspace-29600
record_format dspace
spelling my.uniten.dspace-296002023-12-28T15:05:46Z Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy Rajkumar R.K. Ramachandaramurthy V.K. Yong B.L. Chia D.B. 35093088900 6602912020 36351346900 36350595200 Adaptive Neuro-Fuzzy Inference System Loss of power supply probability Photovoltaic Algorithms Costs Electric power systems Fuzzy inference Fuzzy systems Hybrid systems Loss of load probability Adaptive neuro-fuzzy inference system Excess energy High costs Hybrid optimization Loss of power supply probability Neuro-Fuzzy Optimization methodology Optimized system Photovoltaic PSCAD/EMTDC Renewable energy systems Renewable sources Renewables accuracy assessment cost-benefit analysis energy efficiency genetic algorithm numerical model optimization renewable resource Optimization High cost of renewable energy systems has led to its slow adoption in many countries. Hence, it is vital to select an appropriate size of the system in order to reduce the cost and excess energy produced as well as to maximize the available resources. The sizing of hybrid system must satisfy the LPSP (Loss of Power Supply Probability) which determines the ability of the system to meet the load requirements. Once the lowest configurations are determined, the cost of the system must then be taken into consideration to determine the system with the lowest cost. The optimization methodology proposed in this paper uses the ANFIS (Adaptive Neuro-Fuzzy Inference System) to model the PV and wind sources. The algorithm developed is compared to HOMER (Hybrid Optimization Model for Electric Renewables) and HOGA (Hybrid Optimization by Genetic Algorithms) software and the results demonstrate an accuracy of 96% for PV and wind. The optimized system is simulated in PSCAD/EMTDC and the results show that low excess energy is achieved. The optimized system is also able to supply power to the load without any renewable sources for a longer period, while conforming to the desired LPSP. � 2011 Elsevier Ltd. Final 2023-12-28T07:05:45Z 2023-12-28T07:05:45Z 2011 Article 10.1016/j.energy.2011.06.017 2-s2.0-79961026342 https://www.scopus.com/inward/record.uri?eid=2-s2.0-79961026342&doi=10.1016%2fj.energy.2011.06.017&partnerID=40&md5=f26ee37f27e5eafa191ea828d51a0dc9 https://irepository.uniten.edu.my/handle/123456789/29600 36 8 5148 5153 Elsevier Ltd 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 Adaptive Neuro-Fuzzy Inference System
Loss of power supply probability
Photovoltaic
Algorithms
Costs
Electric power systems
Fuzzy inference
Fuzzy systems
Hybrid systems
Loss of load probability
Adaptive neuro-fuzzy inference system
Excess energy
High costs
Hybrid optimization
Loss of power supply probability
Neuro-Fuzzy
Optimization methodology
Optimized system
Photovoltaic
PSCAD/EMTDC
Renewable energy systems
Renewable sources
Renewables
accuracy assessment
cost-benefit analysis
energy efficiency
genetic algorithm
numerical model
optimization
renewable resource
Optimization
spellingShingle Adaptive Neuro-Fuzzy Inference System
Loss of power supply probability
Photovoltaic
Algorithms
Costs
Electric power systems
Fuzzy inference
Fuzzy systems
Hybrid systems
Loss of load probability
Adaptive neuro-fuzzy inference system
Excess energy
High costs
Hybrid optimization
Loss of power supply probability
Neuro-Fuzzy
Optimization methodology
Optimized system
Photovoltaic
PSCAD/EMTDC
Renewable energy systems
Renewable sources
Renewables
accuracy assessment
cost-benefit analysis
energy efficiency
genetic algorithm
numerical model
optimization
renewable resource
Optimization
Rajkumar R.K.
Ramachandaramurthy V.K.
Yong B.L.
Chia D.B.
Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy
description High cost of renewable energy systems has led to its slow adoption in many countries. Hence, it is vital to select an appropriate size of the system in order to reduce the cost and excess energy produced as well as to maximize the available resources. The sizing of hybrid system must satisfy the LPSP (Loss of Power Supply Probability) which determines the ability of the system to meet the load requirements. Once the lowest configurations are determined, the cost of the system must then be taken into consideration to determine the system with the lowest cost. The optimization methodology proposed in this paper uses the ANFIS (Adaptive Neuro-Fuzzy Inference System) to model the PV and wind sources. The algorithm developed is compared to HOMER (Hybrid Optimization Model for Electric Renewables) and HOGA (Hybrid Optimization by Genetic Algorithms) software and the results demonstrate an accuracy of 96% for PV and wind. The optimized system is simulated in PSCAD/EMTDC and the results show that low excess energy is achieved. The optimized system is also able to supply power to the load without any renewable sources for a longer period, while conforming to the desired LPSP. � 2011 Elsevier Ltd.
author2 35093088900
author_facet 35093088900
Rajkumar R.K.
Ramachandaramurthy V.K.
Yong B.L.
Chia D.B.
format Article
author Rajkumar R.K.
Ramachandaramurthy V.K.
Yong B.L.
Chia D.B.
author_sort Rajkumar R.K.
title Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy
title_short Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy
title_full Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy
title_fullStr Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy
title_full_unstemmed Techno-economical optimization of hybrid pv/wind/battery system using Neuro-Fuzzy
title_sort techno-economical optimization of hybrid pv/wind/battery system using neuro-fuzzy
publisher Elsevier Ltd
publishDate 2023
_version_ 1806426517945712640
score 13.222552

Similar Items