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Influence of Renewable Energy Sources on Day Ahead Optimal Power Flow Based on Meteorological Data Forecast Using Machine Learning: A Case Study of Johor Province

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dc.citedby2
dc.contributor.authorTouma H.J.en_US
dc.contributor.authorMansor M.en_US
dc.contributor.authorRahman M.S.A.en_US
dc.contributor.authorMokhlis H.en_US
dc.contributor.authorYing Y.J.en_US
dc.contributor.authorid57222640905en_US
dc.contributor.authorid6701749037en_US
dc.contributor.authorid36609854400en_US
dc.contributor.authorid8136874200en_US
dc.contributor.authorid56119339200en_US
dc.date.accessioned2024-10-14T03:18:50Z
dc.date.available2024-10-14T03:18:50Z
dc.date.issued2023
dc.description.abstractThis article investigates a day ahead optimal power flow considering the intermittent nature of renewable energy sources that involved with weather conditions. The article integrates the machine learning into power system operation to predict precisely day ahead meteorological data (wind speed, temperature and solar irradiance) that influence directly on the calculations of generated power of wind turbines and solar photovoltaic generators. Consequently, the power generation schedulers can make appropriate decisions for the next 24 hours. The proposed research uses conventional IEEE-30-bus as a test system running in Johor province that selected as a test location. algorithm designed in Matlab is utilized to accomplish the day ahead optimal power flow. The obtained results show that the true and predicted values of meteorological data are similar significantly and thus, these predicted values demonstrate the feasibility of the presented prediction in performing the day ahead optimal power flow. Economically, the obtained results reveal that the predicted fuel cost considering wind turbines and solar photovoltaic generators is reduced to 645.34 USD/h as compared to 802.28 USD/h of the fuel cost without considering renewable energy sources. Environmentally, CO2 emission is reduced to 340.9 kg/h as compared to 419.37 kg/h of the conventional system. To validate the competency of the whale optimization, the OPF for the conventional system is investigated by other 2 metaheuristic optimization techniques to attain statistical metrics for comparative analysis. � 2023 Institute of Advanced Engineering and Science. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.52549/ijeei.v11i1.4115
dc.identifier.epage240
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85151448487
dc.identifier.spage225
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85151448487&doi=10.52549%2fijeei.v11i1.4115&partnerID=40&md5=b9be3f5701f36bddb987333a1bf86121
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/34285
dc.identifier.volume11
dc.pagecount15
dc.publisherInstitute of Advanced Engineering and Scienceen_US
dc.relation.ispartofAll Open Access
dc.relation.ispartofGold Open Access
dc.sourceScopus
dc.sourcetitleIndonesian Journal of Electrical Engineering and Informatics
dc.subjectForecasting
dc.subjectMachine learning
dc.subjectMeteorological data
dc.subjectOptimization
dc.subjectRegression models
dc.subjectRenewable energy
dc.titleInfluence of Renewable Energy Sources on Day Ahead Optimal Power Flow Based on Meteorological Data Forecast Using Machine Learning: A Case Study of Johor Provinceen_US
dc.typeArticleen_US
dspace.entity.typePublication
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