Staff View: 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

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

This 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, temp...

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Main Authors:	Touma H.J., Mansor M., Rahman M.S.A., Mokhlis H., Ying Y.J.
Other Authors:	57222640905
Format:	Article
Published:	Institute of Advanced Engineering and Science 2024
Subjects:	Forecasting Machine learning Meteorological data Optimization Regression models Renewable energy
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id	my.uniten.dspace-34285
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spelling	my.uniten.dspace-342852024-10-14T11:18:50Z 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 Touma H.J. Mansor M. Rahman M.S.A. Mokhlis H. Ying Y.J. 57222640905 6701749037 36609854400 8136874200 56119339200 Forecasting Machine learning Meteorological data Optimization Regression models Renewable energy This 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. Final 2024-10-14T03:18:50Z 2024-10-14T03:18:50Z 2023 Article 10.52549/ijeei.v11i1.4115 2-s2.0-85151448487 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85151448487&doi=10.52549%2fijeei.v11i1.4115&partnerID=40&md5=b9be3f5701f36bddb987333a1bf86121 https://irepository.uniten.edu.my/handle/123456789/34285 11 1 225 240 All Open Access Gold Open Access Institute of Advanced Engineering and Science 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	Forecasting Machine learning Meteorological data Optimization Regression models Renewable energy
spellingShingle	Forecasting Machine learning Meteorological data Optimization Regression models Renewable energy Touma H.J. Mansor M. Rahman M.S.A. Mokhlis H. Ying Y.J. 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
description	This 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.
author2	57222640905
author_facet	57222640905 Touma H.J. Mansor M. Rahman M.S.A. Mokhlis H. Ying Y.J.
format	Article
author	Touma H.J. Mansor M. Rahman M.S.A. Mokhlis H. Ying Y.J.
author_sort	Touma H.J.
title	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
title_short	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
title_full	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
title_fullStr	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
title_full_unstemmed	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
title_sort	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
publisher	Institute of Advanced Engineering and Science
publishDate	2024
_version_	1814061114336477184
score	13.222552

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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