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

Haider Jouma Touma, Muhamad Mansor, Muhamad Safwan Abd Rahman, Hazlie Mokhlis, Yong Jia Ying

Abstract


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.



Keywords


Machine learning Regression models Meteorological data Forecasting Renewable energy Optimization

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